Part I: Deconstructing the Agile Manifesto to Make Better Barbecue

Show Highlights

In this episode, you’ll learn how Derek Lane’s journey in technology and study of the Agile Manifesto coincided with his pursuit of barbecue craftsmanship. These two pursuits eventually mapped together for Lane, and he’s sharing how you can apply the Agile Manifesto and its principles to making better barbecue. 

Along his journey, he created the 20-Day Agility Challenge, a free program where participants commit 15-30 minutes a day to focus on improving their agility. He and a group of colleagues also founded a free online community, Unlimited Agility, where people can take the challenge with others and continue to enable, equip, and educate one another.

Read the Transcript

0:00:00.0 Matthew Edwards: In this episode, I pick up my conversation with Derek Lane as he shares his journey in technology, software development, the Agile Manifesto, and best of all, how it all relates to barbecue.

0:00:17.0 Derek Lane: Every weekend I would try to smoke something. It was definitely this pursuit of craftsmanship. I’d start out with something… The idea is you start with something simple, you’re gonna do chicken, you’re gonna do ribs, and that’s the idea. Well, I’m in Texas and Texas brisket is king. I don’t know how many mistakes I made, I’m sure there were many, but I do know that when after probably about 12 to 14 hours, taking a brisket off that none of us could eat it. I learned a very valuable principle at that time, and this is back when you could still buy briskets for 40, 50 cents a pound. I mean, if it’s on sale now, it’s $2.50, $3, and if it’s not, it’s quite a bit more than that. So it’s a very expensive hobby, is my point, for you to make something that you can’t eat. Some of the techniques I learned, some of the principles that I learned were really to try to figure out how do I make that dollar go a little longer?

0:02:12.3 Matthew Edwards: Today, I wanna talk about something that’s near and dear to my heart, and I believe it’s near and dear to your heart, which is not only meat, and today we’ll talk about barbecue, but also then Agile, what is Agile and how might barbecue and Agile have this weird interrelationship that maybe not everybody else cross-maps in their head, but today we’re gonna talk about meat, barbecue in particular. Does that sound reasonable?

0:02:40.3 Derek Lane: Well, barbecue always sounds reasonable to me.

0:02:43.1 Matthew Edwards: Tell us a little bit about where you’ve come from, like just highlights of your journey, general mindsets, where you are today and where you’re heading, and then let’s mold that into one of the things that you use to teach people and guide and coach and mentor, and just generally pair with folks, which is this analogy or this mapping between barbecue and Agile and where we go from there. But first, teach us a little bit about you, please and thank you.

0:03:09.6 Derek Lane: Okay. Well, originally, my career kind of started as what I call hard engineering; architecture, civil engineering, mechanical engineering, computer drafting, that type of thing, and this is back when DOS was still the primary PC operating system. As a matter of fact, it was relatively new, so that’ll give everybody… Definitely dating myself there. Did that for several years and was able to learn, I guess, back all the way up through what was considered the best computer engineering and drafting systems at the time, and really felt like I had kind of explored a lot of what I wanted to learn, and felt like, “Hey, this is pretty early in my career, and I feel like I’ve already kind of seen all the landscape, what’s next?” And about that time was kind of the emergence of these new things like Microsoft Windows and Linux and other operating systems that are going out there, and that also led to open source software.

0:04:16.2 Derek Lane: So at some point I decided, “Let me go on the other side of the screen. Let me see what it’s like to actually write a lot of code.” And at some point around the late ’90s, it was ’99, 2000, was working on a project for a startup, and somebody mentioned to me that something I was doing looked extreme, and was it extreme programming? And I thought he was making a joke because XP was used as a lot of other things for a lot of other abbreviations, I guess you’d say, and I thought he was making a joke, looked into it, and this is all really pre-Internet, so you had to call the book store, you had to go down to look in the library. I mean, this is back before you could just look it up on Amazon, and found Kent Beck’s book, “Extreme Programming Explained: Embrace Change,” and was just fascinated by the style of the book. Every chapter is two to three pages long, the fact that he was communicating in a very abstract way, but was talking about how do you deliver the pragmatic aspects of value. And when I got through all of it, I really felt like, “Hey, I’m doing a lot of this stuff, but I’ve never heard of this extreme programming. Where is it? What is this?”

0:05:38.6 Derek Lane: Now my background kind of… I guess the formal training I’d received was definitely in a waterfall spiral and ultimately unified process, so really big things which were all state-of-the-art at the time, and realizing XP was one of these things now called the lightweight methodology. And so then I learned about feature-driven development and ultimately about Scrum and Crystal and many others, got to try some of those at different points, and eventually realized, “You know what, I’ve written a lot of code, I’ve architectured a lot of systems, I’ve used lots of different technology,” and that’s still interesting and fascinating to me, but the thing that seems to be the hardest thing is the people problem. When I was learning software, my opinion was that technology was about 90% of the problem, that there were so many technologies. Back then you had to decide what kind of database you were gonna use. I mean, there were so many decisions that you had to make from a technical standpoint, and then you had to get all those things to work together. So people was really the small part of the problem. Of course technology became more standardized, but became more variable too, because now you’ve got more technologies, you’ve got more languages, you have lots of new ideas on how to build things, and eventually I moved over to, “Hey, there’s lots of people who can write code.”

0:07:00.1 Derek Lane: Ultimately, once I understood a little more about the Agile mindset and learned about Lean, Lean startup, Lean enterprise, those types of things, just how to manage waste, how to identify and manage all the different kinds of waste that are part of the process, ultimately, I got to this idea of saying, “Okay, that’s the real problem. How do you get people to decide what they want when they really don’t know, how do you get people to work together and actually work together, not in the same room or the same department or meet every once a week? No, actually work together, and being able to see the nuance of the interactions of people and how that resulted in what was delivered, or whether anything was delivered at all.” And so I decided, “Well, let me spend a little more time learning this, this human aspect of delivering products.” And that’s kind of where I think I’ve spent a little more time. So I’ve spent a little less time, but I kinda inverted my formula. I think it’s now probably 90% to 95% is a people problem, and it’s really about 5% to 10% a technology problem. But to be fair, that obviously with things like free Amazon Web Services and Google Cloud and the proliferation of technology that’s available, that’s definitely had an impact as well.

0:08:22.0 Matthew Edwards: So the journey that you’ve been on though is really a journey of realization, and I will amplify right now that this journey of realization, in my opinion, my interpretation or my perception of the things you said is really a by-product of the type of personality who’s constantly wanting to know more, wanting to see more, wanting to understand, asking why. In other words, you don’t just accidentally discover, “Hey, I’m doing things that are like this XP thing, I wonder what that means,” you choose it. All that stuff is done on purpose, so right off the bat, what in my opinion you’ve already illustrated is you have a hunger to learn and become and evolve, you’re always looking out the window saying, “Alright, I’m doing this thing, but am I doing this thing well? Am I doing it usefully?” And you believed everything was 90% tech and 10% people, and then through the years you’ve discovered that, “Dude, it’s 90% people and 10% tech.” That realization could have been prompted to you by reading it in a book, but it really sounds like you’ve discovered it by living.

0:09:26.6 Derek Lane: Yes, it’s a constant learning curve. And as I moved into software, it was the same way. And I’ve had a lot of frustrated folks who say, “Why are you spending time with that? Why aren’t you spending here doing the thing that we’re paying you to do, or this one thing that we’ve already spent time on? Why are you looking at this other thing?” It’s been a… I’ve been chastised more than once for that. So yeah, it wasn’t until probably I would say in the early 2000s that I learned that there was actually a diagnosis for it, that people actually have been classified as a continuous learner. This idea that there’s actually something wrong with me may be true, but they can’t blame it on the fact that I like to learn new stuff and that I’m always working to learn how to get better. They can blame that on something else, but they can’t blame it on that.

0:10:18.6 Matthew Edwards: But if we fast forward then on that journey, this has led you to a current endeavor or activity that you’re working on called Unlimited Agility, or in particular something that you’ve used as like the tip of the spear called the 20-day Agility Challenge. And I believe based on what I’ve studied and learned and discussed and considered as it relates to what you’re doing, the entire focus is on enabling, equipping, and educating people.

0:10:42.1 Derek Lane: Essentially the 20-day Agility Challenge is my attempt to take a lot of the lessons I’ve learned, almost all through mistakes or misunderstandings on my part, and put them in a format that over a period of 20 days an individual can be challenged against the Agile Manifesto, and the unique aspect of this, or what my hope is, obviously it’s difficult to have your hands in the middle of something and not get some of you on it, but my hope is that the person is challenging themself against the Agile Manifesto as I understand it today, not against Derek’s way of doing things, not against Derek’s version of Agile.

0:11:26.6 Derek Lane: My hope is that this is independent of me as much as can be, and I’ve gone through a number of folks that I’ve worked with over the years that I respect a lot to go through and review it, to give me feedback, to tell me how we can improve it, actually applying the principles in the manifesto to building of this particular challenge that the… One of the things about agility is that you have to decide, as you said, are you going to really pursue this or are you just gonna do the minimum that someone says you have to do to check the box and go on down the road? If you’re really going get better, whether that… You don’t have to be a coach or scrum master, if you want to understand agility and how it applies to business, to everyday life, to some organization that you’re involved in, you’re going to have to work at it. So that’s the intent of the 20-day Agility Challenge.

0:12:18.0 Derek Lane: And then with some feedback there, early on, I was like, “Well, this is great, it’s designed for an individual to do it theirself, but everybody’s not the kind of individual who wants to do this by themself. They’d rather go through it with a group. So how can we do that?” So we created an online community that’s free to join called Unlimited Agility, and that’s one of the things… The goal is really to focus and pursue servant leadership, because that’s so abstract, through the means that we’re more familiar with, which it might be Lean or Agile, or growth mindset or human-centered design, DevOps, any of those things fit in there, ’cause that’s the pragmatic, that’s the tangible thing that we see, but servant leadership can still be the underlying set of principles there and be a contributing factor to the outcome of applying Lean or Agile or so forth.

0:13:17.0 Derek Lane: So one of the things that we do in the community is we offer cohorts for folks who want to go through the 20-day Agility Challenge with others. Again, the point is not to go to the cohort and get the answers to the quiz, that defeats the purpose, that’s not the intent at all. It’s really just to say what can we do to help you ask yourself another question so you can really determine why do you believe this about Agile? Why do you think Agile is this? Why do you think Agile is not this? Where did you get that idea? How did it come to be? And a lot of folks just haven’t taken that time. And then the second thing they haven’t done is really dig into the Agile Manifesto. So that’s kind of my first attempt. And I’m working on… We’ve been trying to get a book out that would even explore that a little bit more and add to it for everybody who doesn’t have access to online, or at least a consistent connection, those kind of things. So we’re hoping that that will grow into more, but we’ve got other things there we can talk about maybe next time.

0:14:17.2 Matthew Edwards: I wanna amplify right before we move on from that, it’s a focus on servant leadership, it’s a focus on craftsmanship, which that whole journey, like Pete Breen wrote an excellent book on software craftsmanship quite a while ago, just talking about this was a journey, it’s not something you accomplished. And so you’re really talking about becoming more tomorrow than you were today, and more today than yesterday, but it’s a continual journey. That’s one of the things I wanted to amplify, is the servant leadership, the pursuit of the craftsmanship, and the other interesting thing too that your desire to foster is a psychologically safe judgment-free environment where everyone is valued, is really what you communicated there, and the intent is a safe place to consider and think out loud and get some alternative perspectives or additional or modified perspectives. Tell us about how you’re mapping some of the tenets or behaviors or patterns, the types of things that you see in your love and journey of barbecue, tell us a little about the barbecue journey.

0:15:25.5 Derek Lane: I guess I’d mentioned around 2000, 1999, 2000 is when I came across Kent Beck’s book. And it was a couple years later that I kind of decided, “You know what, I have never learned how to cook.” So I could cook a burger, a hot dog outside, but that was the extent, that and toast, that was about the extent of my cooking ability. So what I decided to do was, back then, you could go down and for 100 bucks, you could easily buy a smoker. Now, the popularity of this has gotten to where they’re hundreds and hundreds or even thousands of dollars for even a kind of a low level or entry level smoker, depending on what you’re looking for. But I got one, I made the brilliant first time person mistake, which is it was un-assembled when I got it, and I assembled it in the den and then it wouldn’t go out through the back door, one of those kind of things, so I had to take a couple of parts off so I could get it on the porch.

0:16:27.2 Derek Lane: And I think ever since then, I was doing… Every weekend I would try to smoke something, it was definitely this pursuit of craftsmanship. I’d start out with something… The idea is you start with something simple, you’re gonna do chicken, you’re gonna do ribs, and that’s the idea. Well, I’m in Texas, and Texas brisket is king, so brisket’s gotta come up on the dial pretty quick. And I think the first time or two I did chicken or sausage or something, and that’s not… Again, this is not grilling, this is smoking, so it’s low and slow, is the phrase that goes with really that type of barbecue, as opposed to turn it up to 900 and try to flame kiss everything. So at some point I got a brisket, I put it on there, read everything I could read, and that… Again, this is pre-internet, so it’s go to Barnes and Nobles or go to wherever, find a couple of books, put your head in them for a couple days, try to figure out what they’re saying, and then we’re gonna go try it. I don’t know how many mistakes I made, I’m sure there were many, but I do know that after probably about 12 to 14 hours, taking a brisket off that none of us could eat it. I learned a very valuable principle at that time, and this is back when you could still buy brisket for 40, 50 cents a pound. If it’s on sale now it’s $2.50, $3, and if it’s not, it’s quite a bit more than that. So it’s a very expensive hobby, is my point, for you to make something that you can’t eat.

0:18:01.3 Derek Lane: And so I had to find more… Some of the techniques I learned, some of the principles that I learned were really to try to figure out how do I make that dollar go a little longer? How do I stretch it out and go from there? And one of the things I did after talking to you was actually, I’ve threatened to do this for years, and I’ve never actually sat down and done it, but was to actually go through the Agile Manifesto, put my barbecue hat on and say, “What really maps to this idea of applying Agile values and principles to creating good barbecue?” And if you’re interested, I thought maybe we could spend a couple minutes going through that.

0:18:42.3 Matthew Edwards: Also, right now we’re recording this podcast in the morning, I am already thinking about lunch and dinner.


0:18:50.2 Matthew Edwards: Eventually we did have to stop for lunch, and we continued to meet and discuss the Agile Manifesto, its 12 principles, and how it very much translates to creating better barbecue. Make sure you don’t miss them. Subscribe to the Long Way Around the Barn.


Podcast: Unlimited Agility

Show Highlights

The Agile Manifesto is often thought of as a historical event or document, but Derek Lane is hoping to redefine how it’s introduced and revisited because the principles are time- and battle-tested in how it brings value to people. As 2021 marks the 20th anniversary of the Agile Manifesto, Lane and fellow colleagues have formed a community, Unlimited Agility, where you won’t find answers, but you will find like-minded individuals to challenge your beliefs and help you grow in your thinking and your work.

Key Takeaways

  • The Agile Manifesto isn’t a one-stop visit, it doesn’t make sense until you continually revisit it and recalibrate your understanding.
  • Parallel concepts exist – Craftsmanship, Servant Leadership, Lean, Scrum, Kanban – and looking at the Agile Manifesto through their lenses help to broaden understanding 
  • Take the 20-Day Agility Challenge and join the community.
The third Unlimited Agility Conference is being planned right now. This conference was created to promote practitioners of servant leadership who are local and regional leaders who work every day, side-by-side with individuals, teams, and organizations. 

Read the Transcript

0:00:00.0 Matthew Edwards: My guest today was around 20 years ago, when the Agile Manifesto was written, and has watched it evolve in the minds of people, teams, companies and cultures through the years since. He is a community builder, author, speaker and Agile coach. The list goes on and even includes a barbecue life coach, in the event that’s interesting to you. Derek Lane visits with me about how the organization, Unlimited Agility, is building a community for people to consider their journey and how it compares to the original intent of the Agile Manifesto.

0:00:37.4 Derek Lane: I guess I had realized I had learned a lot, I felt like I’ve kind of validated that learning and been able to learn better ways to introduce people to it so that they have a better appreciation for it, and they understand this is not a one-time stop, this is not the… I’m gonna go visit the Capitol or Disneyland. It’s one time, that’s all I’m going to go my whole life. No, this is somewhere you need to come on a regular basis. You’re not going to get it all the first time, and some of it’s just not going to make sense. You’re not ready for it. You need to go back if you need to go back, and you need to go back.

0:01:14.2 Matthew Edwards: Welcome to The Long Way Around The Barn, where we discuss many of today’s technology adoption and transformation challenges and explore varied ways to get to your desired outcomes. There’s usually more than one way to achieve your goals. Sometimes the path is simple, sometimes the path is long, expensive, complicated, and/or painful. In this podcast, we explore options and recommended courses of action to get you to where you’re going now.

0:01:45.8 The Long Way Around The Barn is brought to you by Trility Consulting. For those wanting to defend or extend their market share, Trility simplifies, automates and secures your world, your way. Learn how you can experience reliable delivery results at

0:02:06.6 Matthew Edwards: One of the things I’m curious about learning from you, Derek, is Unlimited Agility. Will you teach us a little bit about what you’re intending to explore? What is the motivator? What’s the desired outcome? How did you get here? And where do you wanna go? And then tell us a little bit about the journey.

0:02:23.2 Derek Lane: Sure, okay. I guess this all kind of started in January this year when I… For whatever reason, a random thread was running through my head and I realized from, of course, last year, I knew this, but it wasn’t time yet, that the 20th anniversary of the Agile Manifesto would be happening in February. I’ve gone through a number of different learning curves as we’ve discussed some of those over time, and I’m constantly trying to figure out, “How do I make this better and is this something I should stop doing and do something else?” In recent years, I’ve gone through a reboot of what the Agile Manifesto is and how it should be presented, versus how I’ve been taught and seen other people coach it over time.

0:03:06.5 Derek Lane: I think the difference is, is that most people, when they’re introduced to it, it’s at most, no matter what, it’s a half day, one class, a two-day class, whatever it is, it occupies anywhere from half an hour, to a couple of hours. And the Agile Manifesto is introduced as a historical event, a historical document, and it’s just really watered down and then it’s focused on practices and maybe some concepts, but we move quickly to the checklist, the 12-step program.

0:03:34.7 Derek Lane: This is Agile, Agile is supposed to be this, so this is what it looks like. Well, through the lens of Scrum, it looks one way. Through the lens of XP, it might look different, through the lens of Kanban, it might look significantly different. Through the lens of Safe, it looks radically different. So depending on what your introduction point is to the Agile Manifesto, and agility, has a huge impact on your reference point of what it is and what its importance is, and the ability to achieve or be able to accelerate this idea that we call agile or agility. And then there are parallel concepts and domains out there such as Lean, and we have even more abstract ideas, such as craftsmanship. What is craftsmanship? We have this idea of servant leadership, what does that mean? There’s lots of debate about these things, and when it all kind of zeroes in on the Agile Manifesto, I guess I had realized I had learned a lot, I felt like I’ve validated that learning and been able to learn better ways to introduce people to it so that they have a better appreciation for it, and they understand this is not a one-time stop, this is not the…

0:04:48.8 Derek Lane: I’m gonna go visit the Capitol or Disney Land. It’s one time, that’s all I’m going to go do my whole life. No, this is somewhere you need to come on a regular basis, you’re not going to get it all the first time, and some of it’s just not going to make sense. You’re not ready for it. You need to go back if you need to go back, and you need to go back. I think it’s a missing element in how Agile is often thought of as either a goal or now people are trying to update it as a journey or a destination, but I think we’re still missing this element that I’m now calling regenerative agility, which is this ongoing… Returning to the source to validate what we think we’ve picked up, throw away the stuff that was really junk, build on top of what we have now, pick something up new and then let’s go on again and then let’s come back again. So it’s very inherent, for those who know the Agile Manifesto and familiar with it, to see this idea of both iterative and incremental improvement, because learning is at the center of all of this, this idea of constant growth and improvement.

0:05:48.8 Derek Lane: Again, my idea was, how do I take something I’ve learned and give it back to the community? How do I find a way to honor the work… I talked to a number of folks, a number of former co-workers and colleagues and things, and came up with what now I call the 20-Day Agility Challenge, and the idea is basically a step-by-step period of time, from anywhere from as little as 15 minutes a day to as long as you want to spend.

0:06:20.2 Derek Lane: Typically, it’s not more than 30 minutes to an hour, but the idea is that you… Each individual would challenge their own beliefs on what Agile is, against not what I say, but against what the Agile Manifesto says. So it’s a deep dive inspection into every element of the Agile Manifesto over a period of 20 days. And then at the end, it’s like, “Well, this isn’t all there is. What do we do next? How do we take this and move on?” The 20-Day Agility Challenge is free, it was intended to be available, it’s delivered right now through email, and it’s available for anybody at, so anybody can go out there and sign up. But in the beta testing of this, a lot of feedback came in from some folks saying, “Well gee, Derek, this is good, it’s designed for an individual challenge, but there’s a lot of people who aren’t ready for that, or that’s not really their approach to things, they do a lot better in a group,” and I agree, so while some folks… And we talked about options, will pick some co-workers or somebody and go through this, not everybody has that option, or not everybody is going to be the kind of personality that’s going to go try to round up people to do this.

0:07:34.1 Derek Lane: So we decided to create an online community where we could create regular cohorts where folks could get together from around the world who wanted to go through this and create kind of an accountability group, so everyone would have at least a couple of accountability partners that could go through the challenge themselves, they would have someone to discuss this with, and then we actually have a couple of times a week through that 20-day period where someone who’s already been through the challenge will help facilitate it. So far, it’s been myself and a couple of other folks. Our goal is… There’s no answers here, this is not a matter of… We’re not filling in the blank. This is not quiz time. The goal is for you to challenge your own beliefs against what the Agile Manifesto says, for us to figure out, how can we help you if you get stuck? And we have interesting questions and really good discussions and topics have come up, and some of them are online, just through the chat type situation, and some of them are more through a Zoom type situation, so much more interactive as far as in person.

0:08:35.7 Derek Lane: But that led us to creating a community, and then that led us to a kind of a more foundational realization that there are other concepts that people struggle with besides agility. Lean, for example, which I mentioned, the growth mindset. What does that even mean? Servant leadership, craftsmanship, all of these things. How do I get from here to… Are these things related? Or are they… Is Scrum the same as Agile because someone told me that? And if it is, then what is this Kanban thing and why aren’t we doing that? And so there’s all of this complexity between these concepts, a lot of them are abstract, and then the general interaction that people have more on the day-to-day basis with what we typically refer to as the practices, and so we’ve kind of expanded that a little bit. That led us, interestingly enough, to creating an arena for people to practice, to try out these new skills, to share what they’ve learned besides just an online community, so we’ve created the Unlimited Agility conference and we’ve held two of them so far, we’re running them quarterly. The next one will be in November, and the idea there is to really invite people who aren’t necessarily the big name people. This is not “Come and listen to someone else,” this is “Come and interact with someone else,” this is “Come and share your experiences.”

0:10:00.7 Derek Lane: And so we think we’re pioneering a new approach to virtual conferences, we got to thinking about what are the things that people don’t like, the things that don’t work, the things that are different in a virtual environment, an online environment, then from a physical conference. And I wrote a LinkedIn article to try to enumerate a lot of those, but what we’ve learned so far is that in a physical conference, one of the things people like is the physical interaction. After a speaker gets done with their session, maybe I could go catch them afterwards and ask them questions, or get coffee or get dinner. Well, we can’t do that in a virtual environment. Even if we have breakout rooms, it’s not really the same thing. But what we did is we decided, well, what would happen if the speaker was to sit in the audience with the attendees? You can’t do that in a physical conference, it’s just physically not possible. Newton’s second law, I think, might have something to say about that. So what we decided to do is we have all of our speakers record their sessions, and we’re using a modified TED Talks format, so none of them are more than 20, 25 minutes long.

0:11:05.4 Derek Lane: But the idea is that now because they’ve done that, this doesn’t mean they’re going to record and the speaker is gone, this means now the speaker’s in the audience, and they can interact with the audience. They can say, “Oh, I meant to add this here.” They can say, “Here’s another reference, it wasn’t occurring to me at the time.” But this is the first time that we know of where a speaker gets to actually participate and put theirselves in the role of the attendee for their own session. So we’ve created this new kind of way of thinking about delivering content, and it’s much more communal, it’s less serial, and it allows then, that extended conversation to go on, people can literally ask a question at the moment it occurs to them during the session and get a response from the speaker in more or less realtime. So we’ve kind of combined this idea of the Q&A and the director’s cut with the idea of an interactive session. It’s just interacting in a different way than we think of, if we were physically in the same room and I could raise my hand, and eventually you might call on… Or you might not.

0:12:13.4 Matthew Edwards: Well, let me reiterate so far what I think I’ve heard, which is the motivation here, as I understand it from you, is to give an opportunity for people to reflect upon where they are in their journey in relation to the original intent or communication of The Agile Manifesto, and it was motivated to some extent, in context of the upcoming 20th anniversary. And so out of that, the conversation is, “Hey, how have we evolved? How are we doing?” And so you’re looking to create a type of environment where people are able to come together and say, “Hey, I was thinking through this, this is how I’ve typically understood, this is how I’ve applied it. How are other people doing it?” So it’s kind of like a conference, not really a conference, it’s kind of like cohort, but the assumption is Agile Manifesto, where are we and you in relation to the manifesto? So what do you believe? What do you practice? And why do you believe it? And then how are we doing evolving? It sounds like you’re creating a safe environment for people to evolve together with a single point of origin, which is the manifesto.

0:13:28.5 Derek Lane: Yes, it’s that. I would say that it… However, a slight difference, I would say, is that it started with this idea of focusing on the Agile Manifesto, but it’s now expanded to include Lean principles and what is servant leadership, (Robert) Greenleaf’s work. And so it’s no longer… While the name Unlimited Agility is still, I think, an accurate description, it’s not limited to agility in the sense of the Agile Manifesto. You also have Agility by being a better leader. One of the things we’ve learned from folks like… Was it Adam Grant and Simon Sinek? And those folks, is that they’re talking about leadership, they’re talking about a different kind of leadership than most of us have been exposed to and have been trained that way. They’re talking about leaders where the employees are first, they’re talking about leaders where customers are right, but it’s not a matter of just the customer is always right, it’s a matter of, “Well, the customer is right, but they’re right, why?” Because we need to validate that if what we’re doing isn’t valuable to them, they’re not gonna continue to be our customer. So it’s no longer a technology-centric in the way that, often, Agile is thought of.

0:14:47.2 Derek Lane: Because Agile starts out by saying that this is the Agile Manifesto for software development. There’s nothing wrong with that, but with the change of literally a handful of words, we can abstract the software-specific aspects of this and realize that we don’t have to change 99% of the rest of these values and principles, and they literally apply in almost every context.

0:15:11.7 Matthew Edwards: So what would you say so far in this journey, has been an unexpected surprise? Whether a pleasant surprise or a distasteful surprise where you realized, “My goodness, what was I thinking? I need to make a change.” What type of surprises have you experienced along this journey?

0:15:30.4 Derek Lane: Well, I’ve definitely been through the, “Gee, I thought this was gonna be different or easier, or fewer steps,” a number of times. I think the challenge that has always been there, that it’s not unique to this effort, of helping… Of communicating to people that there is no checklist that’s going to make you agile or lean, or smart, or fast, or profitable, or… Fill in the blank. This is not a 12-step program. Agile is not a 12-step program, and I’ve been saying that for years, and I’ve been saying that about a number of different things besides Agile, but the challenge is that the force, the gravity of a black hole is pulling so many people in business, so many people in technology, to hurry up and get it done, to check the box to do the next thing. It’s all about the task and the project plan and the delivery date, and it’s not about value, it’s not about people, and that’s what the Agile Manifesto starts out by telling us that, this is all about people. Some people seem to already be a bit like Neo in the Matrix, they already have a… They suspect something is not right about the way things are going, but they’re not quite sure what it is, and then there are other people who are well aware that… They don’t know what to do about it.

0:16:53.1 Derek Lane: Nothing they’ve done has worked. And so I think a community like this can really help them because first of all, it’s going to put them in touch with people who have either been or are currently in the same spot that they are, so just the fact that we know now that there are other people that are just like us, that’s a huge psychological benefit, that creates a certain amount of community there. And then we’re hoping to create these other mechanisms for folks to be able to then exercise and practice and learn new ways of doing things that are maybe external or tangential from their normal everyday lives. But then as they learn and meet people from around the world who have had success with something and they get a new idea, and now they’ve got a sounding board and accountability partners, an expert, so-called, that… Someone who’s just a little further down the trail than they are, they can go ask these folks for help and say, “What do you think? What would you do? What could I try?” And I think it gives them a whole new range of options that are very personalized, and… Because they’re creating this new community, and our goal is for it to be a self-sustaining community where the members of the community decide where we need to go next. And we’ve got a long list of potential things on a backlog, but right now, now we’re back to, “We’ve gotta validate those…

0:18:22.1 Derek Lane: The community finds those valuable now,” versus later, versus never. One of the other benefits of our format is that a lot of people who have a lot of experience, and you’ve done this, you’ve been in a room and you’ve heard somebody, they’re just a little timid, they might talk a little softly, but you’re like, “Wow, man, you’ve got some gold there. Why don’t you share this? Why don’t you speak up?” And that’s just not their personality. But with our format, they can record and get a lot of feedback. Through the process of recording, they can record their session, and then they can from a safe distance, because social media has proven… People are perfectly… Feel perfectly safe with the keyboard in front of them, between them and their audience. And be able to then interact with the audience and gain that confidence and be able to still share a lot of the values and experiences that they’ve had. And I think in the last conference, we really had a lot of transparency from some of the speakers who were saying… Explaining, “Here’s some stuff that didn’t work,” and being willing to be vulnerable. I think that’s… It’s becoming a little more acceptable in some arenas, I think, with the help of folks like the TED Talk from Brene Brown and other folks like that, that are encouraging leaders to realize the value of being vulnerable with the folks that you lead, and the value that that gives them to help them be better leaders.

0:19:48.5 Derek Lane: Now we’re getting back into servant leadership, so it’s amazing how all of these things are interconnected, the patterns are repetitive, and they appear to exist in each one of these domains, or in this case, in lots of these different concepts. So if we can maybe… Dismantle is not the right word, maybe if we can reveal enough of the pattern so that people can see the parallels of something they’re more familiar with, then they will gain confidence and they can grow quicker in an area that maybe they’re not as familiar with.

0:20:22.9 Matthew Edwards: So after you have one of these conferences, the types of material that’s reviewed during these conferences, does that continue to be available to the attendees or folks later?

0:20:35.4 Derek Lane: Well, the discussions that happen on the… Essentially the chat boards, the Facebook and Twitter-like features of the community, those are available and open all the time. So because we wanted to make this available to everybody, we created some separate levels of membership. The entry level or the lowest level that’s free as far as charging goes, is available to anybody who wants to just basically go on and create an account, but what we’re doing to try to help… Again, to kinda hopefully make the community self-supporting and self-sustaining, is we have from the day of a conference, which always happens on a Thursday, it’s open to the public, so anybody who basically registered for the conference gets access to all of the materials for the conference, including a speaker’s roundtable that we do at the end where the speakers get to ask each other questions, and a number of other interesting things that we try to come up with. But after that, the idea is that any of the paid level memberships will have access to all of that archive and that material, so yes, it is available and it’s online, but after that kind of window of a week, then it’s available to any of the paid level memberships.

0:21:48.2 Matthew Edwards: So Derek, what do you think you need next in order to evolve it to the next level?

0:21:53.2 Derek Lane: Well, I would say, just to tag on to the previous idea, the conference is free. Again, we have a free level of the membership, the 20-Day Agility Challenge is free, so we’re scheduling to have three or four of what we’re calling either lakeside or fireside chats, depending on what time of year it is. So we’ll have two of each, each year, if we’re fortunate. And that’s really more of the idea of a sit-down interview with someone who is considered more of a leader and expert in some area. That’s something that we’ve already got cued up, and that’s coming in the pipeline. Our goal is to really be able to give 10% of the profit that might come in from running the… To charities that we’ve validated. So we’ve already verified and validated three charities, and we hope to be able to… We’ve got another couple in the pipeline, and we would like to be able to set that up to where that’s just a part of everything we do, so that as people can register for free for the conference, instead of paying for the conference, they could donate to this charity.

0:22:55.6 Derek Lane: We feel like that’s a way to practice this idea of servant leadership and be able to… But in a real physical, tangible way, to any of the charities that they might feel more affinity for. So that’s something that we’re looking to expand on as… We’re really just looking for additional volunteers. As we get more volunteers, we’re able to do more things. We’re looking for folks to help with… We’ve got another set of websites that we’d like to get up and running, that we just haven’t had the bandwidth to get up and running. So those are skills that would be greatly valued and help. We’ve got stuff cued up as far as trying to make workshops available, so we want to have both some free and some, again, some paid content that would help support the community so we can do more things. And those will be in a number of different areas. I think to start with, we’re kind of saying, “Here’s some introductory level things,” but not introductory in the way that, again, that it’s often communicated. We want to be able to do something that we feel like is kind of “We’ve learned more, and here’s maybe a better way to introduce some of these ideas.” But we have a number of additional, like I said, things on the backlog. I think where we’re going is to grow a group of volunteers that are interested in exercising that servant leader or that craftsmanship muscle, and we want to give them a chance to do that.

0:24:31.8 Matthew Edwards: I went out and looked at your sites previously, and looking at them again today, and so for someone who wants to learn, what’s the front door URL you’d like people to go to to get a look into it?

0:24:44.8 Derek Lane: Sure, the 20-day Agility Challenge is 20, it’s the number two zero, they can go there and register for the 20-Day Agility challenge. They’ll basically receive that… It literally is a matter of putting your name and your email in. You can also do this, again, with a cohort, that’s where you can go to the community and join the community, and there’s a separate group that’s just for the 20-day Agility Challenge Cohorts. For the Unlimited Agility community, it’s Their regular triple W website is one of those we’re almost ready to launch, but not quite there yet. So the membership website is up and running, and that’s where you can go and choose a plan. Again, we just encourage folks to choose the free practitioner plan, and that’s all the way at the bottom, as the options that you have for membership. But the landing page there tries to describe what the community is and what we’re trying to do, and it shows the charities that we’re currently supporting right there, and tries to answer any of the typical questions, there’s some FAQ type stuff on there.

0:26:02.6 Matthew Edwards: Right on. That’s outstanding. Derek, we have covered very many topics to a lot of depth and breadth, across our time talking together, and I just want to thank you very much for taking all this time to give us insight into your journey and the types of things you’ve learned and where you are and where you’re heading, and in particular, the work you’re doing with the 20-Day Challenge and the Unlimited Agility. That sounds amazing, and well done. Thank you for your time, good sir.

0:26:32.7 Derek Lane: Hey, thank you. Thanks for your interest, I appreciate it.

0:26:35.5 Matthew Edwards: This is just the beginning of our conversation with Derek Lane. Our next several podcasts deconstruct the Agile Manifesto using the analogy of learning how to barbecue. Now, if you would love to smoke meat or would love to improve your ability to smoke meat and other items I would never have thought possible, and you have a desire to always become more today than yesterday, using the Agile Manifesto as your guide, I encourage you to keep listening. Derek should write a book on this topic, but until then, you’ll have to settle for listening to raw, candid conversation that might also make you hungry along the way.

0:27:16.6 The Long Way Around The Barn is brought to you by Trility consulting where Matthew serves as the CEO and president. If you need to find a more simple, reliable path to achieve your desired outcomes, visit

0:27:33.3 Matthew Edwards: To my listeners, thank you for staying with us. I hope you’re able to take what you heard today and apply it in your context so that you’re able to realize the predictable, repeatable outcomes you desire for you, your teams, company and clients. Thank you.


Podcast, Part III: Bridging the Gap Between the Art and Science of Data Analytics

Show Highlights

Science is the iterative testing, results change over time with variables. For data science, what’s true today could dramatically or incrementally change tomorrow based on one variable. The art of it is accepting that there will be exponential opportunities to discover more, learn more, and communicate more to find value and purpose in data.

This final episode with Jacey Heuer provides insights into how individuals can seek opportunities in this field and how organizations can purposefully mature data science and advanced analytics.

Missed the first two episodes? Listen to them both: Part I and Part II.

Read the Transcript

0:00:58.1 ME: In our third session with Jacey Heuer, he helps us bridge the gap between the art and science of data analytics. We discuss what is required of people and organizations to explore, adopt, implement, and evolve today’s data science practices for themselves and their organizations.

0:01:18.2 Jacey Heuer: And so I really look at this as, again, bringing it back to science and art. Science gets you to the insight, the art then is how you tell that story and paint that picture to create comfort with some of that uncertainty that you’re now revealing in your data.

0:01:35.2 ME: So as it relates to individuals and organizations and the adoption of a more formal data behavior, through your experience and your perspective, the study, the work that you’ve done, how do we make this a normal, common daily conversation for people and companies instead of this emerging knowledge area that some people are studying?

0:02:05.9 JH: You’re right, the passion is a key component of this, right? I think passion across anything you’re engaged in is important to be able to find that and it’s a true driver motivators finding your passion. Mine is learning, happens to be with data science, and those kind of come together well for me. Just going a bit deeper into my personality with this too, is data science, as much as there’s science involved in it, there’s a lot of art involved in it. Personally for me, my background, I have an art background as well, in my past. When you think about left brain, right brain, creativity, logical, all that kind of stuff, it’s usually more binary, more definitive, and for whatever reason, I have some bit of a crossover in that. I can find enjoyment in both sides of that and it works well for me with data science, but what I think about from the standpoint of trying to wrap your brain around, what does this mean, how do I gain comfort in sort of the mindset that it takes to deal with and feel okay with ambiguity, uncertainty, right?

0:03:11.9 JH: I think so much and so often in business, which rightly so, it’s, I want to know definitively, 100% accuracy, what’s gonna happen in the future and so on. That’s a fair mindset, and I think there’s a lot of good leaders and people realize that’s not possible, and you make your own decision too. Given the information I have at hand, what’s the best decision I can make, and you go with that. Data science is really taking that human decision process, which you’re already dealing with, uncertainty, whether you’re aware of it or not, and just putting more support to quantify some of that unknown through data. And in that does require a new mindset of, the information I’m taking in may become more broad because I’m getting more data supporting the breadth of my decision-making, but then that also then becomes the realization and vulnerability of really seeing the uncertainty that the decisions I’m making, distilling those in my mind, that uncertainty in a way that I may not be aware of, but now because that data’s present, I’m aware of that uncertainty and becoming more potentially concerned with that uncertainty.

0:04:23.9 JH: And that’s where the side of the data scientist becomes vital and important, it’s a storytelling. And so how do you tell that story and manage the uncertainty that you’re now highlighting to a leadership or an individual that they might not have been aware of before? At least consciously aware of that is maybe the better way to state that. And so I really look at this as, again, bringing it back to science and arts. Science gets you to the insight, the art then is how you tell that story and paint that picture to create comfort with some of that uncertainty that you’re now revealing in your data.

0:04:56.1 ME: That’s similar to just about any career, I imagine, but I know explicitly in the technology side of things where there can be absolutely fabulous software developers who have not yet discovered that they have to also be able to communicate the goal and the journey and the value and manage that message and I wonder if that’s not a learned behavior for any human, but the fact that you’ve articulated the relationship between art and science all as the same collective responsibility, that’s really powerful.

0:05:37.1 JH: Science inherently is journeying into the unknown. Science is meant to constantly test and retest and so on. That’s what good science is, but there’s rigidity, there’s a tool belt that can be applied to that testing. It’s a known set of tools, generally. The art side, the learning there comes through experience, comes through vulnerability, comes through the willingness to test out, does this… From a data science perspective, does this plot with the dots on it mean more than the plot with the lines on it, does the bar chart mean more than the pie chart and so on and so forth, and how do I combine those together to get that message across, and at the same time, beyond the visual, it’s… Your written and verbal communication as well becomes essential ’cause you’re the one creating the confidence in this new idea that you’re bringing to the business.

0:06:37.7 JH: You’re bringing across… A good example I have would be the concept of distribution density plots, so it’s a very statistical term, basically all it is, you think about a normal distribution bell curve, it’s putting some statistics to that bell curve, just for example. How do you convey what that means to someone that has no statistics background? When you say the word density plot, their eyes glaze over. Being able to distill that down to elementary terms, do it in a way that gets your point across and drives the decision that, I think requires just stepping into the arena, finding and seeking out bits of that opportunity to challenge an idea, challenge a mindset with some data-driven visual, some data-driven insight and put it out there and see what happens. Again, science versus art, science, I think you can practice, you can get through history of defined techniques. Art is more, what works, I just have to try it.

0:07:47.4 ME: So I will amplify that to walk into my next question. Your statement was just, “I have to try it.” And part of my curiosity from your perspective is, let’s talk about someone in an organization who’s just now discovering the whole field of data on purpose. Doing data on purpose. So we’re not talking about just your historical typical, “Let’s create a 2D plot in Excel and call it a day.” We’re talking about trying to understand multiple dimensions of many seemingly unrelated things that when put together may actually reveal something that would never have occurred to our minds, we wouldn’t have seen because we weren’t looking for those types of things. For someone that’s just now figuring things out saying, “Hey, I really think that this might be a thing, I want to look into this.” We’re assuming that they’re starting in kindergarten, they’re starting with near zero. Where would they go? How should people get involved, get their feet wet, jump in? What do you see? What do you know? What would you recommend?

0:08:57.0 JH: Luckily, especially within the last decade or so, the learning options online, the open free learning options online have accelerated vastly. Like with a lot of things, a Google search for data science is a good starting point. There’s a number of open free coding academies. Coursera’s a great one, Udacity, things like that, not to market for anything individual, but it’s starting there as just this data science road map. What do I need to learn? What are the foundation skills to kind of build on? And getting a sense of what the scope looks like, I think starting with just that Google search can help define what are some of these terms and areas of this space that pop up and begin to emerge, things like statistics and programming, R and Python and SQL and kind of this whole space, just starting there with that cloud of what’s out there, to me, is always a good way to begin any project. What is my space that I’m living in? Really then what’s probably been most useful to me, it comes down to learning some of the core concepts and technologies, and then seeking out opportunities to practice and apply those, even if you’re stumbling your way through practicing, applying those, start trying to force those into whatever you’re working on right now, and it may not be the solution for your project at hand, but can I take a sliver of it and make it work from a data science lens to build up my skill set?

0:10:34.7 JH: To really give a maybe more concrete answer to things to focus on, I think it’s… Traditional statistics is a great place to start, and again, there’s a number of resources that are great for that, just through a Google search, statistics being what is the difference between mean and mode and what’s your range, min and max, how do I define a distribution? Things like that. Starting there, then moving into probability, probability is a big concept in data science, machine learning, so getting your mind around that space. You don’t have to be an expert in it, but at least becoming familiar with terms of probability. Probability Bayesian inference is another area that’s out there that goes hand-in-hand with probability as well, those three areas, traditional statistics, probability and then Bayesian inference, which has a lot of probability in it, are three sort of core foundational areas of this spaces, stats to be involved in. And then it’s moving into the technology side, so now you’ve learned and got a grasp on some of these statistical ideas, pick up R, Python. I’m an R guy.

0:11:46.3 JH: Python tends to dominate. Depending on your source, Python might be a little bit in front of R, it could go back and forth. Either one, the mindset I have is become an expert in one, but be familiar across both of them. ‘Cause you need to be able to operate on both sides, and either one of them, you can be working in R and you can leverage Python, you can be in Python, you can leverage R and go back and forth. There’s a lot of capability in the libraries and packages that are out there. And then as you develop the skill set of your technology, some of the base statistics, now start venturing into your machine learning, your AI. And depending on your source and your mindset, all of this really comes back around to developing the skill set to be an expert line fitter is what it comes down to. I say that kind of tongue and cheek, but really, anything you’re doing from a modeling perspective, it’s your taking your data set, which may be X number of columns wide, you can re-imagine that as being X dimensions in space, you have one dimension, two dimension, three-dimensional space, which is what we all live in. You can plot three dimensions on a plot relatively easily, but as you go up into higher dimensions, you can’t really plot that.

0:13:06.4 JH: That’s where a lot of the mathematics come into play then it’s how do you navigate a multi-dimensional space of data and be able to, out of that, to kind of, your thoughts earlier math, you distill meaning from something that in this multi-dimensional space, you can’t visualize and there’s no simple way to get your mind around it. That’s where machine learning and AI and stuff comes into play then. It’s those tools are effectively putting a pattern, finding the pattern in that multi-dimensional space that lets you either split it up or pinpoint a data point and so on. So that’s kind of the foundational skill set I think I would focus on, thinking about it. And then from that, there’s subsets and offshoots, you get into TensorFlow and PyTorch and all these other things into the cloud, all that, but that’s the core of where you really started when you’re talking about “What do I need to get into and start learning to go down this path?”

0:14:01.9 ME: So you led with, “Look for opportunities,” and then after that, I believe you said, “You need to go learn some fundamental elements of statistics.” And there were three different areas you were focusing upon. Then, “Go learn about some of the technology.” Then after that, you were talking about how you can start to take the statistics plus the technology and start discovering, seeking or otherwise applying that. So you’re starting to become operational at that point. So the first two steps are really classes of preparation, if you will, classes of data, prepare steps, but you start to become operational after you have those two classes of things under your belt in terms of familiarity, experiential pursuit that type of thing. So really three big steps. What you just communicated is a time-based journey of course, but I think one of the most valuable things you may have said there is, ultimately you have to seek the opportunities, or this was just an academic exercise of reading about this, then reading about this and then tomorrow there’s new subjects.

0:15:11.2 JH: Very true, and really, the reason for that is, space is so broad. I don’t think it’s unique to data science and this discipline, but there’s so many methods, so much research out there, problems are… There’s no standard, typically no standard problem. And so it’s really that process of, “I have a problem, now what are some methods that I can maybe force on that problem?” I tell you, I think the power… And again, I think this is common across many skills and disciplines, but it’s as you add breadth to your knowledge base, really a lot of the power you bring to your role as a… Your emerging role as a data scientist is not necessarily the expertise you have in a particular method or approach, but it’s the knowledge base you contain of what are alternatives to solving this problem. So now I have instead of one tool that I try to force onto this problem, I’ve got a selection of 10 tools that I can explore that space. I may not be an expert in all 10, but at least I know I can try 10 of those and find the one that seems promising and then really dig into that and become a deeper expert to solve that particular problem. That’s where, again as you step further into this career, your breadth of knowledge becomes greater and a lot of that skill set and value comes from, “I’m not a one-trick pony.” For lack of a better term, “I can pull from this tool set and find a better answer, the best answer.”

0:16:47.6 ME: Well, that is consistent with what you said earlier, which is, you’d like to be an expert in at least one, but functional and useful in both or all. To some extent, I can be an expert and a generalist, and that will take me further down the road than, “I have a hammer.”

0:17:05.0 JH: A lot of that, I think is just tied to the availability of information in this space. So I have the tools at my disposal to go and learn, and again, going back to some of the prior comments, having the passion to learn, being driven by some learning, identifying when you have that knowledge gap and then going, seeking out and learning that new tool set that previously you may have just been, kind of aware of, but now I know I might need it to answer the questions, so let’s go dig into that. Capitalizing on that motivation and building that knowledge from there, I think is essential as well.

0:17:44.7 ME: If I’m an individual, regardless of where I am on my career path, I’m new in my career, or I’ve been around for a while, or I’m in the later third of my journey, whatever it is, is really irrelevant. And if I’m an individual and I’m in a company and they’re not asking me, they’re not talking about any type of analytics, they’re not talking about BI, they’re not talking about any of this stuff. And I’m interested in doing this stuff, it’s probably on me to figure out, “Okay, where is my company? Where are they wanting to go? What problems do they want to solve? And how can I apply these things I’m exploring to proactively propose and find and encourage opportunities? And that might actually be a wonderful journey, it could be a wonderfully educational journey, or it could be a tough journey in the event that you stand alone with that appetite to learn like that.

0:18:37.0 JH: That’s the reality. Whether you’re in a role that isn’t defined traditionally as a data scientist or data analyst, and you’re trying to spark your journey into that, and the organization hasn’t adopted yet, or you’re in a role that, you’re a data scientist in a larger data science team and the organization is fully invested in it. I think for many organizations, there’s still an education gap of what really is advanced analytics and data science and what are the questions that we need to leverage them to solve for us? How do we ask that question? When do we bring them in?

0:19:15.5 JH: I think that’s a universal continuous thing, and it requires to solve that, it requires again, the term vulnerability, is the vulnerability and the willingness to push the idea forward as you continue to gain your knowledge, continue to gain insight and learnings, bring those up to those in the organization who are the decision-makers, the project owners, whatever it might be as, “Here’s a new way of thinking about this.” Likely, they may have heard of it, probably haven’t heard of what ML or AI actually means, wouldn’t say imposing, but putting that perspective out there, making them aware of it becomes as much of your role as anything, if you want to bring that… Develop that skill set, and bring that impact to your organization, you really need to drive that thinking and drive the mindset shift that it requires to incorporate advanced analytics data science into an organization.

0:20:11.3 ME: So if I’m a C-Suite leader, and I have all kinds of amazing responsibilities that go with my role in the organization, just like your role in the organization, and I’m feeling the pressure to make my numbers, and manage my market, and address the current economic situation, all of the things. And you’re the aspiring data person, and you come to me and say, “Hey, Matthew, I’ve been looking at this stuff. I’ve been studying some things. I have a couple of thoughts.” How would you approach me? What would you say to me? Not that I’m belligerent and stubborn and cranky, but rather I’m just on the move, and I’m looking for concrete chunks, if you will.

0:20:48.2 JH: It’s a great, great thought exercise and an important one. What’s been powerful for me, it’s showcasing… As you call it, showcasing out of the possible, but doing it in comparison to current state. So being able to… Whatever your question is, just for the example here, showing, here’s the report, the current process, the current output, what it looks like now, and I’m delivering that to you, so I’m maintaining my relationship with you. I’m not falling short or anything like that, but I’m taking some of these new learnings, and it takes a time commitment, but passion should drive that, to now, let’s layer in a slice or two of something new on the side of that. Maybe I’m forecasting for next quarter for you. And traditionally, it’s just been… What happened last year, we’re gonna add some percentage to that year over year, and something very simple.

0:21:43.8 JH: And now I’m gonna go in and at its current state when I’m enhancing it, by putting some confidence intervals on it, and giving better scenario analysis around if you do X, we see Y. And start to tell that story of what’s the next level. And it may not be perfect, but you’re at least creating awareness of the capability that you’re developing, and bringing to the organization. And hopefully, through that beginning to create excitement around “Hey, I’m the leader, the executive. I could see the improvement here, let’s dig into that further.” And you start to get the wheel spinning and that progress rolling from that.

0:22:20.6 ME: That’s very tangible. Here’s what we’re currently doing, here’s what we’re using it for, and what it seems to mean to us. Here’s what we could be doing, and here’s how it may actually add additional dimension or insight or view or value. That’s really good, that’s very concrete.

0:22:37.3 JH: It’s powerful, and I’ll say, what can be scary in that, fearful in that is, you have to put yourself out there again. I go back to this just because I’m not the stereotype, IT mindsets or data science mind… Personality, and things like that. But again, it’s not waiting for the business direction sometimes, but just taking a chance and stating, “I think if we did this, this could be the improvement.” And at least starting that conversation. It’s that awareness, that seed of awareness that becomes powerful and that it might not be right, but at least you’re creating visibility to a capability that either exist in your skillset or it can exist, and now starting that conversation.

0:23:24.6 ME: Well, let’s shift it a little bit then. So these companies that are starting to realize, “Hey, we need to be a little more aggressive, a little more assertive about what data, how data, when data. How can we get to where we really want to go, and how do we make this data thing work for us?” But if I’m a company, and I’m looking for people, where am I going to find people? If I don’t have people saying, “I’ve been thinking about this, I want to do this, and I’m starting brand new.” Where am I going to find these folks? Are there data conventions? And you guys are all hanging out like, “Pass the tea. Let’s talk about this.”


0:24:03.5 JH: Candidly, I don’t know if I have a proper answer for that or a great answer for that, other than I think in the space… Data science as much as… We’ve talked about the hard skills of data science, the art of data science, I think the other piece in there to be aware of it’s the subject matter expertise for that organization that becomes essential. You could think of a diagram of this with those three elements in it. That subject matter knowledge becomes essential to really developing impact out of advanced analytics and data science for the organization. I think often for an organization to define success in this, it’s finding individuals that are again, driven by learning, have curiosity, and motivated to learn, preferably in this space, but having in place mechanisms that allow them to ramp up the business knowledge that they bring, that organizational knowledge. What product are you manufacturing in the nuances of manufacturing that product? How does thes sales team sell that product? That business knowledge and the nuances of that are key to success in data science.

0:25:24.0 JH: Using myself as an example, when I turned into an organization, I tried to focus the first few months on just strictly relationship building. Finding that conduit into who are the people that represent the space in the organization, that can become my source of… My vessel of knowledge that I can tap into. Because when I’m working with data and trying to build a model, there’s endless questions around “Do I pull in column A or column B? Do I combine them? Do I create something entirely new? Does this mean anything?” Because what I think is meaningful in the data may be statistically significant, all this kind of stuff, when it actually goes out to the field, and you get feedback and that expert knowledge on, “Well, we actually don’t operate like that, so your insight is meaningless.” If I can get that knowledge, or at least a representation of that, that’s where a lot of power exists, that my underlying skill sets, technical knowledge, storytelling abilities, all that stuff can come together, and leverage that subject matter knowledge. So I don’t know if I answered your question well Matthew, or not, but I think organizations developing pipelines or… Pipelines isn’t the best word… Environments that are conducive to that transfer of knowledge between the subject matter expert, and the…

0:26:38.3 JH: Data scientist, the advanced analytics, and those using the data. That knowledge sharing, I think is where a lot of that power resides.

0:26:47.2 ME: So that’s a way they can discover the value and use and help grow and foster a culture that grows people, but you didn’t yet tell me if there are conventions where there are data scientists like you all sitting in smoking jackets, having tea, discussing the latest algorithms of the breakfast.

0:27:07.4 JH: So those do exist depending on your space and need and so on, right?

0:27:13.6 ME: Right.

0:27:14.9 JH: The term data science is just over a decade old in formality. If I’m remembering correctly, I think it’s credited with originating at LinkedIn as kind of where it started with formerly, and don’t quote me on that. A lot of the build-up and hype to this sort of where we are now with data science… Let me rephrase, not build-up in hype, but growth in this discipline and the rate of growth in this discipline overtime started with the technology companies latching onto researchers that were presenting on neural nets, artificial intelligence, machine learning at their dedicated conferences. So one of the conferences that has been around for decades, is called NIPS N-I-P-S, it’s now NeurIPS is the new term given to it, but it’s all… What was up until a decade ago, a conference attended by maybe a couple of hundred researchers off in kind of the corner, to now it’s annually attended by thousands of people that come to this. That’s where a lot of the original poaching occurred, these researchers brought from academia into practical application data science going forward now. That’s a extreme example.

0:28:37.8 JH: I think there’s many different organizations out there. I think of TWI is one, IIA, Institute of International Analytics, and so on. There’s all these different organizations that, again, to your point, Matthew, it’s maybe not sitting around in smoking jackets and so on, but gatherings of analytics and analytic mindsets that bring a lot of talent together and a lot of skill sets together that can be sources of experienced skill sets, experienced individuals in these resources. And then to give credit to the universities. Again, over the last decade or so, more universities are offering more programs related to business analytics, data analytics and so on. That pipeline is filling up, becoming more robust, becoming more refined as well, and there’s a quality, new grads beginning to come out of universities as more learnings are applied there.

0:29:35.8 ME: It’s a normal, normal problem. So educational institutions are themselves businesses or else they cease to exist. It’s not a free world here, so these folks have the responsibility and the desire and the goal to enable and equip and educate and all of the types of things. A reality though is the gap between learning these concepts to… Even illustrated by your earlier point, go learn about statistical things, whether it’s statistics in and of themselves, probability base, and all of those things. Then learning the tools that are the Python and anything else that makes sense, and then figuring out how to operationalize that and then starting to get into splinters. That’s a journey that has to be lived. Journeys aren’t ordinarily lived in college or university. Journeys can be enabled. The fact that universities are offering more and more data education is outstanding.

0:30:28.9 ME: But it’s fun to see how this is evolving. It’s fun to see where it’s going. To your point, 10 years, thereabouts, plus or minus, plenty of places to go on the web, many conventions to go to, seeing how it’s evolved from a small subset of researchers to a more populated thousands and thousands of people who are interested now. What a wonderful evolution of an idea that we’re getting to watch, unfold right now. And then as far as what does it mean? Heck, that’s part of the whole challenge. What is it? When is it? What does it mean? How we make use of it? This has been a phenomenal conversation with you, good sir. Thank you very much for taking the time to teach us about so very many, just aspects of the journey of data and your journey with data, and even very much thank you for taking the moment to just give some pointers to people who want to learn how to have a journey like the one you’re having. Thank you.

0:31:28.4 JH: Thank you, Matthew, and I couldn’t agree more with those thoughts that are… Right. It’s a great journey that this whole space and discipline is on, and there’s a lot of runway left in it. And because of the uncertainty, there’s a lot of room for creativity and impact to be had as more people venture out and become skilled in this space, as well. So it’s been a… I’ve enjoyed the conversation and learned more about myself and hopefully be able to share some good thoughts as well along the way, so thank you.


Podcast, Part II: The Artistry Required for Data Science Wins

Show Highlights

In the second episode of this three-part series, Jacey Heuer helps us dive into the evolving roles and responsibilities of data science. We explore how individuals and organizations can nurture how data is purposefully used and valued within the company.

Missed the first part? Listen to Part I.

Individual Takeaways

  • Adopt a scientific mindset: The more you learn, the more you learn how much more there is to know.
  • Hone storytelling capabilities to engage and build relationships that ensure the lifespan and value of data is woven into the culture.
  • Set one-, five-, and 10-years goals and aim to achieve them in six months to fail fast and advance the work faster than expected.
  • Create buy-in using the minimum viable product (MVP) or proof of concept approaches.
  • Prepare to expand your capabilities based on the maturity and size of the team focused on data science work. As projects develop, you’ll move from experimenting and developing prototypes to developing refined production code.

Organizational Takeaways

  • When your company begins to use data analytics, roles and responsibilities must expand and evolve. Ensure your people have opportunities to grow their capabilities.
  • Data must be treated as an “asset” and viewed as a tool for innovation. It can’t be tacked on at the end. Ideally, it plays a role in both new and legacy systems when aggregating data and capturing digital exhaust.
  • Engage and find common ground with all areas of business by helping them comprehend how data science “expands the size of the pie” rather than take a bigger slice.

Read the Transcript

0:00:05.5 Matthew Edwards: Welcome to the Long Way Around the Barn, where we discuss many of today’s technology adoption and transformation challenges, and explore varied ways to get to your desired outcomes. There’s usually more than one way to achieve your goals. Sometimes the path is simple, sometimes the path is long, expensive, complicated, and/or painful. In this podcast, we explore options and recommended courses of action to get you to where you’re going, now.

0:00:37.3 The Long Way Around the Barn is brought to you by Trility Consulting. For those wanting to defend or extend their market share, Trility simplifies, automates, and secures your world, your way. Learn how you can experience reliable delivery results at

0:00:57.0 ME: In this episode of the Long Way Around the Barn, I picked up where Jacey Heuer and I left off in our first conversation on data science, which has now become a three-part series. Today’s conversation focuses on how both individuals and organizations can leverage data analytics and machine learning, to evolve and mature in their purposeful use of data science.

0:01:22.0 Jacey Heuer: It takes a diligent effort from the data team, the advanced analytics team, to engage with the architects, the developers, those groups, to get your foot in the door, your seat at the table. I think getting to that state means that data is seen as a valuable asset to the organization, and is understood as a tool to drive this evolution into a next stage of growth for many organizations, to achieve those dreams of AI, machine learning and so on, that lie out there.

0:01:57.7 ME: We start by diving into how the various roles fit into today’s data science ecosystem.

0:02:04.8 JH: To the primary roles that I define in a mature team, as it relates to the actual analytics, the data analyst, the data scientist, machine learning engineer, and their MLOps, and what’s becoming a newer term though, taking this further, it’s the notion of a decision scientist.

0:02:24.1 JH: There’s a lot of roots in, you could say, traditional software development in terms of defining, and what is becoming defined for data science, and I’ll say the space of advanced analytics. Generally speaking, not every organization, every team will be structured this way, but I think it’s a good aspirational structure to build into, and it’s the idea of that you have your data scientists and they shouldn’t… The real focus is on prototyping, developing the predictive, prescriptive algorithm, and taking that first shot at that. Then you have this data analyst role, which is really more of the traditional analytics role, where it’s closely tied into the organization, they’re doing a lot of the ad hoc work on, “I want to know why so and so happened. What’s the driver of X?” things like that.

0:03:20.2 JH: So there’s a little bit of predictiveness to it, but it’s a lot of that sort of, “Tell me what happened and help me understand what happened in that role.” And then you could start extending this out, and you start thinking about the machine learning engineer. That’s really taken the step now to go from the data scientist who’s made that prototype, to handing it off to the machine learning engineer, and their role is to now bring that to production, put it into the pipeline. Oftentimes, that may be also handling the productionalizing of the data engineering pipeline or the data pipeline is all right.

0:03:52.7 JH: So being able to go, in a production sense, from the data source, maybe it’s through your data lake, through transformations, and into this model that often it’s written in Python. R and Python are those two languages that dominate the space. Python is often the better language because it’s a general programming language, it integrates well with the more applications, things like that, but R still has its space or its place. I’m partial to R. Nothing wrong with either one.

0:04:23.1 JH: But that machine learning engineer, they’re really tasked with bringing this into production. And then the sort of next step in this is the MLOps. And machine learning engineer falls into that, MLOps, kind of a bigger category, but it’s that role of once that algorithm is in production, it’s up on the mobile phone, it’s up on the progressive web app, it’s being used, now it’s an ongoing process of monitoring that and being able to understand, “Is there drift occurring? Is your accuracy changing? Is performance in that model changing?” This gets into, if you’ve heard of the ROC curve, AUC, and things like that, that monitor performance of that model. And that in itself, can be… Depending on the number of models that have been deployed, can be a task. If you have a few hundred models out there and a changing data environment, there’ll be a need to update, to change, it may be that individual’s task to go in and re-train the model or work with the data scientist again to reprototype a new model.

0:05:31.4 JH: So that’s the general, I’d say the primary roles that I define in a mature team, as it relates to the actual analytics; the data analyst, the data scientist, machine learning engineer and their MLOps. And what’s becoming a newer term though, taking this further, it’s the notion of a decision scientist. This is really the person that is crossing the gap or bridging the gap from, “We’ve implemented or discovered an algorithm, discovered a model that can predict so and so with a high accuracy,” whatever it is. Now their role is to be able to take that and drive the implementation, the buy-in from the business partners to help them make better decisions. So they’re much more of a… Have a foot in both camps of, “I understand the models, I understand the technical side, but I can sell the impact of this and influence the decision that the business partner is making.”

0:06:31.2 ME: What is the name of this role, again?

0:06:34.5 JH: The term that I see for this and I like to give it, it’s decision scientist, is what it is. So it’s much more on the side of really focused on changing, improving the decision and having a tighter role on that side of it, as opposed to what can be more technical, which is the data scientist or machine learning engineer. They’re much more focused on the data, on the programming and so on.

0:07:00.1 JH: And reality of this is, many organizations won’t be at a maturity level to have those distinct functions and roles. And there’s going to be a blend, and it’ll be maybe one or two people that have to span the breadth of that and be able to balance traditional analytics with discovering new algorithms, to productionalizing it, the doing some data engineering, to MLOps, to speaking with the business partners and selling the decision, the new decision, the decision process to them, and so on. And that’s good and bad, obviously. You can overwhelm a small team with that, but you can also find great success in that. There’s a mindset involved in this. I don’t know who to quote this to, but it’s a good mindset that I like. It’s essentially, establish what your one, five, 10-year goals are and try to do it in six months. So you’re probably going to fail, but you’re going to be a lot further along than that person who is trying to walk to those longer-term goals.

0:08:02.0 ME: You’re saying that the larger the organization, the more likely these ideas or behavior classes will be shared across different roles but that then suggests, then small organizations or smaller organizations, one or more people may be wearing more than one hat.

0:08:19.6 JH: I think the better term is more mature data organizations. You could be a small or large organization, but what’s the maturity level of your usage of data, the support of the data needs, data strategy, data management, things like that. Often, it is… Kind of follows a sequence, where it may start with this data analyst role making the initial engagement. A business partner comes to the data team and says, “Hey, we have a desire to understand X better.” The data analyst can go and work on that, develop some initial insights. And out of those insights, that’s where the data scientist can step in and now take those insights and let’s build an algorithm for that. We understand that we reduce price, we drive up quantity, typical price elasticity.

0:09:03.8 JH: We see that in our data, our industry, our market reflects that. Well, let’s go and build an algorithm that can optimize pricing across our 80,000 SKUs. So we build this algorithm and we bring in environmental variables, variables for weather, regional variables, all this kind of stuff and really make this robust. Well, now we need to put it into production. So I hand it off to ML Engineering, they go and build this pipeline, write it in Python, maybe the data scientist worked in R, we do a conversion in the Python, they tie it into a mobile application, so sales reps can have pricing information at their fingertips while they’re having conversations.

0:09:45.6 JH: So now you have the sequence playing out, where again, often in a less mature data group in an organization, that’s going to be one or two people wearing those multiple hats. And if that’s the state, you’re a less mature organization, I think the best approach to it, and it kind of follows the notion of Agile methodology and things like that, but it’s really this MVP notion. The best way to eat an elephant is one bite at a time, is a real concept when you’re trying to grow your maturity of your data team. And let them focus on really developing the different pieces of it and getting it in place before expanding them to have to take on something more. Identify that project that you can get buy-in on, that… Expect to have some value for the organization and go and build that out, to really develop that POC and that first win.

0:10:36.6 ME: That’s interesting. That’s a fun evolution. One of the things we’ve watched change through the years is the idea of information security, regulatory compliance stuff. In days gone by in the software world, there were requirements which turned into designs, which turned into software, which turned into testing, which turned into production stuff, and that’s largely sequential. The serial dependency is going into production so waterfall-y And then as we’ve evolved and rethought the role of testing as everybody’s role and information security is everybody’s role and all of these things, and we introduced continuous integration, continuous delivery, it’s really thrown a lot of things on their head.

0:11:15.9 ME: Nowadays, we’re able to actually attach tools, and granted, sometimes they’re just literally hanging ornaments off trees, but we’re able to attach tools like vulnerability assessment tools, we can write penetration test suites or smoke suites, we can attach them to a pipeline that says, “For every new payload that comes down the line, apply these attributes, characteristics and ideas to it, and make sure that it’s heading in the direction that we all choose.” You can fail the build right there or you can flag it and send a love note to somebody and then you remediate it in a meeting later with coffee.

0:11:54.1 ME: And now, we’re all able to be together in one cross-pollinated team, bring in Infosec on purpose, so design with Infosec in mind, on purpose, from the beginning. And so, acceptance criteria and user stories and epics and all of these things have attributes that says, “For this, these things must exist and these other things can’t exist.” And now information security can be tested during the design, as well as the development continuously, instead of surprising people later like an afterthought, like salting after you’ve grilled the meat, as opposed to before, that type of thing. And even that’s its own religious conversation.

0:12:35.2 ME: With the data stuff, I’m curious. Do you feel like data is being included in… You mentioned Agile, so I’ll talk about scrum teams, delivery teams, strike teams, that type of thing. These cross-pollinated teams composed of developers, designers, human factors, folks, data folks, all of the different types of folks, one team, one priority, one deliverable, one win, one party, that type of thing. Do you feel like the idea of data is being proactively included in the design and development of ideas, or it’s an afterthought, or you’re getting Frankenstein on a regular basis and somehow you have to make magic out of a pile of garbage? How are you seeing things evolve and where do you hope it’s going?

0:13:18.1 JH: The Frankenstein is a good illustration of that. I think, often, data as it is for analytics needs is an afterthought when it comes to application design and development and everything that goes along with that. And a lot of that, I think it’s primarily due to the relative youth of advanced analytics, data science, machine learning, and so on. In reality, the moniker data scientist is maybe a decade old or so, there’s been statisticians and so on before that, and data science is really kind of just the next step down what was that path.

0:14:00.9 JH: So for example, for me, having practiced data science in a number of mature organizations, mature being they’re 90-plus years old or been around for a while and built systems to meet certain requirements, transactional requirements, things like that, and they perform their purpose well, but that purpose wasn’t necessarily with a mindset for, “How can we maybe improve this or leverage the knowledge that can come out of those systems to be applied elsewhere in the business, the data that can come out of that?”

0:14:33.5 JH: And the term I’d give that, it’s these applications are creating data exhaust, to give it a term, where it’s a byproduct, maybe it’s getting stored in a SQL server some place or some database, and maybe there’s some loose reporting being built on it, but it’s probably not easy to go and query, maybe it’s a production database by itself, so if you try to query a lot of it, you’re running into concerns of impacts on performance for the production database and production systems, and so on. And so one of the practices that I’ve been really focused on with this experience is injecting the presence of data science advanced analytics into that application design process, into the design of those new systems, to give a lens into, “What does the algorithm need to be performant? What kind of data do we need? And let’s ensure there’s a thought process behind how that data is being generated, the flexibility to test potentially within that system, how data is being generated and where it’s going, how it’s flowing out, how could it be accessed, how can it be queried?

0:15:53.2 JH: There’s a good example, this is going to be a bit of a technical example, so forgive me for this, but one of the systems in a prior organization I worked with, would move everything in very embedded, complex XMLs was how the ETL process happened. And so from a data science perspective, that’s not an easy thing to shred apart and dig into, to get to all these layers and hierarchies within a super complex XML, but the system performs to its purpose within the organization, and it does what it’s supposed to do. So from that side of it, it’s a great system that works.

0:16:36.0 JH: It’s an old system, but it works. But from the data side, it’s a mess. It causes us to have to Frankenstein things together to try to work with it, was what the outcome was. The idea is evolving, but I think it takes a diligent effort from the data team, the advanced analytics team, to engage with the architects, the developers, those groups to get your foot in the door, your seat at the table, to ensure now, as we go forward and new applications are being built and designed, there’s a mindset for, “What does data science need to be able to leverage this and take us from data exhaust into data gold or data as an asset?”

0:17:19.6 ME: This is a wonderful, wonderful, awesome mess that you’re talking about. We’ve watched the same thing through the years with testing, where it was always test in the arrears, but then people wanted to understand, “Why is the cost of acquisition and cost of ownership so darn high? Why does it hurt so badly to debug software when it’s in production?” Well, test in arrears is the answer, guys. So test-driven, moving testing or quality behaviors as far upstream as possible means consider quality while building, not later. And we’ve watched the same evolution in security, whereby we design with security in mind, as opposed to try and bolt that stuff on later.

0:18:04.6 ME: And that digital exhaust conversation that you’re talking about is a standard problem, even for old school production support people, whereby somebody built some software, they dropped a tarball, threw it over the wall, somebody pumped it on to some old rack and stack hardware in a brick and mortar, and now the developers went home and the infrastructure people have to figure out, “How are we going to make this sucker run?” And then after that, “Why is it broken? Oh gosh, we don’t have log files.” So we have all kinds of challenges through history of no logging, some logging, way too much logging, you’re killing me.

0:18:42.0 ME: And the Infosec people in particular have been on the wrong end of the stick for that and testers were too, where they had to go figure out why, not what, why. Well, hello logging. And Infosec people, they have inconsistent logging, so they trap everything, like they’re the Costco of data, just trying to find any action, so that they can then attach tools and do sifting on it. So we’ve watched software, in particular, change from, “I do my job, now you do your job,” to, “We are doing this job together,” and it sounds like you are smack in the middle of that outstanding, awesome, messy, sometimes painful evolution, which is, “This is a thing, but not enough people understand the value of the thing, so they’ve got us sitting in this room without windows.”

0:19:33.7 JH: Yes, you hit the nail on the head, Matthew. And that ties back into the conversation of roles and so on. If you go back to the development of a software engineering team or Infosec team, cyber security, things like that, we’re getting established, finding how we fit into the organization, depending on… There’s a lot of opinions on this too, right now, in terms of where should advanced analytics data sit within your organization? Do you report up through IT? Do you report up through marketing? Where do you touch? That’s another sort of big question that’s out there.

0:20:12.2 JH: My preference and what I’m coming to understand really works best is to really establish its own pillar in the organization. So the same way that you have marketing, same way you have IT, finance, you have data, having a chief data officer that has a C… And reports up to the CEO and everything underneath of that, that is really when I think getting to that state means that data is seen as a valuable asset to the organization and is understood as a tool to drive this evolution into a next stage of growth for many organizations trying to achieve those dreams of AI, machine learning and so on, that lie out there.

0:20:53.9 ME: A lot of these paradigms might be continually challenged, if not destroyed and re-factored. So the idea of these verticals have, how do I separate data from marketing, from IT, from ops. A lot of those things are HR, Human Resources derived frameworks, but they aren’t delivery frameworks. And so we’ve continued to have this interesting challenge in companies, of, “I have all of these vertically organized people, but they have to deliver horizontally.” So how that gets addressed on the CDO side or embedded or whatever, companies are going to figure that out on their own, they usually do. Although across whole careers, not necessarily on Saturday. An interesting thing you’ve said to me though, although you didn’t really say it like this, it makes me think that the idea of data by design is actually a thing, and that when we’re building systems, when we’re building out epics and user stories and acceptance criteria, the people that are there, the developers, the designers, the data folks, sometimes that gets messy where people think it’s an old kind of a database perspective as opposed to, “What do I actually want to know? What am I actually going to do?” And let that influence the design and the implementation thereafter. Without asking those questions, this is a Frankenstein conversation all day, every day. Data by design needs to become a thing and data needs to be included in strike teams or delivery teams on purpose on a regular basis.

0:22:30.6 JH: The importance of the presence of that knowledge on what’s needed to bring that data to value, to become an asset. So you mentioned asking the question of what do we need and what do we want to know, that really has to come from the data scientist, the advanced analytics team, having a voice in that conversation, to be able to say, “If we’re building an application that is going to provide recommendations for a product to an end user, well, in that application, I need to know potentially what algorithm is going to be applied, how it’s going to be applied, and what does that algorithm need to perform from a data perspective. How easy… Is it going to be a online versus offline learning environment, which essentially the differences between streaming versus batch in terms of how we model and build predictions. What does that mean? What is that going to take? Do I need certain REST APIs built in to access data in some way, or is it going to be a batch dump overnight, into the data lake for us to build something on?”

0:23:34.7 JH: All those questions really need to be designed and have a perspective from a data scientist or an engineer that has knowledge of the data science requirements, the process, and preferably it would be the joining of those two together to allow them to work and bridge that gap. But it’s in… The success that I’ve had in driving those conversations, it’s been, “How do you get creative in trying to convince people that doing so expands the size of the pie and doesn’t just take a bigger slice of the pie for me or for you?” So finding that benefit, that software developer, that systems architect, whoever you’re working with, engaging them in a conversation in a way that lets them see the benefit to them, from a data science perspective, so that you get that buy-in because I know now, with their support, my life’s going to be easier because I’m going to get the data, the access that I need to build a stronger model, a more robust model.

0:24:36.0 ME: One of the other interesting things that you said I’d like to amplify is, you talked about how in some environments where the idea of analytics wasn’t taken into consideration in advance, you end up having to go find out if data exists at all and if it does exist, in what state is it captured, if at all and is it fragmented, dirty, is it sporadic, what do you have available to you, and what state is it in? You have to do that before you can even decide, “Okay, here’s the problem we want to solve, here are the things we need to know, here are the desired outcomes, or the things we want to decide along the journey. So I need this data. What’s in the system already?”

0:25:19.4 ME: So that impacts people’s perceptions of the adoption velocity of data people too, I would think. In other words, somebody says, “Dude, all I want to know is… What I want to know what’s taken you so darn long.” And your answer is, “But you never looked at this before, so we don’t collect all of the data. Some of the data we do collect is in 700 repos spread out across… Who knows? Time and space, and most of it’s dirty. So before I can even get to my job, I have to find the data, clean the data, get the data, and then get people to re-factor stuff.” That makes it look like you guys are slow. So how do you handle that? What kind of experience are you having there?

0:26:04.9 JH: Yeah, so that is… Directly ties into the power of storytelling. The power of storytelling of the journey, not waiting until we have, “Here’s a shiny object, we built it and let’s show that,” but showing the journey that we’re on to get to that object, that output and so on. because you’re right, the reality is that often, the mindset from those requesting the insight is, “There’s got to be an easy button. You’re a data scientist, we have data, just click your button, hit your mouse and tell me my answer.” In many ways, those questions that are being asked of us are all in themselves mini-innovations, because they’re not standard run-of-the-mill questions. It’s…

0:26:53.8 JH: You captured it well, Matthew, in terms of, “We’ve got to go and find this data, clean the data, experiment, iterate on those experiments, potentially bring it to production, whatever, build an interface for it to be consumed” and so on. And so it’s important to be vulnerable and honest with that journey and educate those stakeholders on, “This is the reality of the current state, what we’re working with. We’ve dug into… You came to us with your question, we’ve gone out and did our initial assessment exploration, this is the current landscape that we have and because of that, this is going to be the roadmap, the timeline to achieve what we need, and we’ll engage with you as we go forward.”

0:27:39.0 JH: “We have a weekly, biweekly, whatever that time frame is, dialogue with you to update on progression, pivot and iterate and so on.” But it’s that storytelling that is essential. Going on a bit of a tangent here, that’s… I think, in terms of resources to go and educate and become a data scientist that are out there, those programs do great at learning the technical side of data science, but it’s that relationship, the storytelling side, again, that is as critical as any ability to write an algorithm, to program in Python and so on. How do you inform of what it takes, give transparency to that process, to build that relationship with your business partners, is essential.

0:28:30.5 ME: That makes sense. So the storytelling and the relationships. And it sounds like really, leadership needs to have an understanding of the value and need for analytics to start with, but then they need to have an additional understanding of, it needs to be data by design. And so, you could be walking into a legacy house and you need to figure out how to retrofit. Well, that’s going to have a slower adoption velocity than if I was starting with a brand new system, zero code on a blank screen and I can do data by design. And so the relationship, the communication, the story, that’s probably a pinnacle part of your entire existence, which is communicate.

0:29:11.2 JH: It is, and a good framework for it, that I think can help that story, it’s one, it’s positioning as… Often, it’s a capability, you’re developing a new capability for the organization, which is advanced analytics, assuming you’re not mature, it’s a different state. But that capability building, there’s really four pillars to that. It’s people, process, technology, and governance is kind of what I put into that. And so how do you, within those four pillars again, of people, process, technology and governance, what do you need to accomplish within those pillars? What gaps do you have? And tell the story around that. How do I go and resource this properly? Is it a data issue? Is it a application design issue? Is it a… We don’t have the right question coming from the business? We can’t answer that. This is a better question. Within that building of the capability, put the story together and I think that becomes useful to that dialogue, that relationship building with the business partners.

0:30:14.3 ME: As the idea of data, data science, data analytics is evolving, as its own body of knowledge, its own set of practices, you’re actually doing software development in Python and R. That being said, even though your output includes mathing, lots of it, the reality is, you’re delivering software in some way, shape or form that needs to be integrated into a larger ecosystem of some sort. So different question for you. Based on your experiences and the things that you’ve seen and just the general industry, given that it’s actually a software engineering craft, in addition to all of the wonderful analytical math and algorithm, all the things that you’re doing, do you feel like the data science industry itself recognizes that they are software developers, and therefore they also need to be pursuing software craftsmanship?

0:31:11.7 JH: Yes, mostly. That’s…

0:31:15.4 ME: I realize that was meaty. But anytime somebody says, “I build software,” we need to build reliable software, and that requires lots of good engineering practices.

0:31:26.4 JH: It does, right? So it’s a great question, and the reason I say yes, mostly, is because this relates back to the notion of the different roles and disciplines, data scientists, machine learning, engineering and so on, but I follow this as well. I’ll say, I came into this discipline from the statistics side, and not from the software engineering development side. And being vulnerable here, being candid, it shows in the way I write code. So it’s very much I write code for experiment and iteration and prototyping in that data science mindset. And you’re right, what’s needed though when you take that into production, you need quality code meets the Python style guide, stuff like that, commented well, if you believe in commenting, all that kind of stuff.

0:32:16.8 JH: That’s where that software development really comes into play. And I think the reality is, there’s probably a bit of a mismatch in skills there, if you can… But I think it’s evolving and becoming more refined as we go forward. There is a skill set difference between those two, even from the standpoint of… As we develop and leverage things like GitHub and code repositories and stuff like that, and everything that goes along with software, software engineering, software development, that’s a growing… Has a growing presence on the data science side as well, the collaboration of algorithm, coding and building a notebook, all that kind of stuff. So it’s a great question, but I would say it’s still predominantly kind of an experiment, prototyping side, and then… How do you refine that into well-written production code, on the other side of that.

0:33:16.6 ME: It’s an evolution for everybody. Even historical hardware-based, the rack and stack, brick and mortar, data center type folks, the infrastructure type folks, the people that were historically doing those types… Those focused operational behaviors, that world has changed out from under them as well, where we’ve moved into cloud engineering, and if I can have a 100% software to find everything, then that means all of a sudden, software developers can actually define all of their own infrastructure and networking and failover and all of the rubbish. But at the same time, now, the infrastructure folks actually need to become software developers. So we’re watching lots of amazing and awesome things change, and the data world is just another lovely facet of how we’re evolving, building things that are useful to us. Really, ultimately, you just have to figure out like we all are, is, “What problem are we trying to solve? What are the desired outcomes and what are the things that are necessary to get from there to there?” and then design it and do it in such a way, and especially attitudinally, be willing to change.

0:34:30.2 ME: “I am going to break something. I’m not as smart as I think that I am, and I have to be reminded daily,” and I do get reminded. It’s just an evolutionary thing. I think this journey that you’re on is phenomenal, and it’s not because you have all the answers, it’s because you don’t. That’s what makes it phenomenal. And I think people miss that, when they consider iterative development or iterative change, is, “It’s okay, tomorrow, I’m going to be plus one.” Is that where you think your industry is, is absolutely, plus one? Are you thinking you’re 10X daily like, “Dude, we have a long way to go”?

0:35:12.2 JH: No, I like the way you kind of illustrate that, Matthew. And what’s in that, I think, is most valuable there, it’s the realization that we don’t know everything, and the participants in the room don’t know everything. I think when you’re pursuing, whether it’s a data science objective, whatever it is, having that understanding that we’re all learning, is as valuable as anything, and allows for… I’ve used this term a few times, vulnerability to be present and to be comfortable with that, where I don’t know everything there is to be known about topic X, you may know more than me, but let’s be open about that and build our knowledge collectively, again, expand the size of our pie, as opposed to one of us taking a bigger slice, is I think, an important mindset to have, not only in building and maturing data science, advanced analytics, but in whatever you’re taking on is essential, the scientific mindset. Really, the understanding that once you realize that, you know enough to know that I don’t know, that is a good state to be in.

0:36:28.1 ME: There’s the interesting pure science of this whole conversation, the creation of and evolution of an idea, and then there’s the operational science of this idea, which is, “This business has allocated a million dollars to this project, and it has some amazing set of features that need to exist, that serve these users and these industries, and there’s a definition of done, desired outcomes and all that,” there’s a box. And so somehow, you have this amazing challenge of telling a story that makes the idea of data, where it is in its life span, and the value of data, as it relates to this business and project come to life for somebody to say, “Yep, we should be doing this for sure.” But then you have to figure out how to get inside this existing, moving organism as well, which is, “We build stuff, we move it into production, we generate revenue, serve clients, make them all smile.” You’re building a plane and flying it at the same time, and even though this isn’t a Zoom video for people that don’t know, we do Zoom so that we can interact with each other in video. Jacey, you’re still smiling this whole time like, “Yeah, this is a bunch of crazy, and I love every second of it.”

0:37:43.6 JH: Yes, it’s enjoying the journey, enjoying the grind, whatever term you want to give to it, is essential for, again, not just the path I’m on or you’re on, Matthew, whatever it is, falling in love with that journey and the chaos of that, and the opportunity to learn within that space. My personality, I’m driven by learning. If I see this as an opportunity to learn, that’s what motivates me to go and pursue it and take that on, and data science, advanced analytics, this whole discipline space is rich with that. It’s learning every day. For me, it’s learning a new algorithm, a new mathematical concept, a new development idea. How to integrate, move into a cloud environment. That’s a whole other beast in itself, as all the services of cloud and transforming from on-prem to cloud and everything goes along with that. So the space for learning is vast, that’s exciting, and it should be.

0:38:50.8 ME: So as we start to wrap up, I wondered if we could get your viewpoint on the idea of data and all of the roles, just example, the roles that you’ve talked about, they may or may not exist in all of the different companies or all of the different HR frameworks or whatever it is we want to talk about, and the value of data and when data and how data and where to include them, and when should it… The front… Did you do it in arrears? Am I good with Frankenstein? Why is… What’s my adoption velocity? Why did it cost so much money just to get this data? What is going… That crazy, crazy mess. If someone is going to say, “Hey, I want to figure out what data analytics is, and I want to figure out how I can leverage these things to evolve my company,” how do people figure out where to start? Is there a clean answer or is it context-driven? Is it just always messy?

0:39:44.8 JH: My perspective on it, it starts with understanding, “What are the desires of the organization?” Obviously, “Are we developing a new product? What’s our strategy look like?” All that kind of stuff, in terms of that vision going forward. And from that, it’s understanding, “What’s the current data landscape look like?” And that’s a beast in itself, in defining that. But it’s really getting your mind around that as a starting point, can often inform, “What are we capable of? What can we do now? And who or what resources do we need to level up and move forward?”

0:40:25.0 JH: As poor as this can sound, I think oftentimes, companies like to just jump to, “Let’s get a data scientist, they’ll solve it.” Well, the data scientist comes in, if they don’t have the data to work on, they’re just kind of floating out there, trying to figure that out or missing that piece. And so, data as a foundation and working on that, I don’t think it’s ever solved, but focusing on that, building it so it becomes a true resource and not just exhaust, that is… That’s, I think, the initial, essential key focus to launch off of. And in that, it may be a combination of data science and data engineering coming together, whatever that is, but I think, in my perspective, that foundation of building a strong, robust data environment is essential to any success that can come out of that, come out of the venture and the path into advanced analytics, machine learning, AI, and so on.

0:41:25.2 ME: If you don’t know what you want to know, or you don’t know where you want to be after this effort has happened, adding a data scientist isn’t going to change anything other than your budget, your run rate, but it’s not going to change your outcomes. So, it’s kind of like, you shouldn’t ever go to the grocery store on an empty stomach and you should know why you’re going there before you walk in, or don’t send me. That’s the net. You really need to know where you want to be, or else don’t just hire somebody.

0:41:55.7 JH: From a data science perspective, hearing the terms, “Go and discover something for me in the data” is often a little cringe-worthy. because then it’s a… You need that objective, I need to know, “Am I trying to make lasagna? So this is the ingredients I have to go get from the grocery store to make lasagna.” Sending us on that, just a wild goose chase, to say, “Go and find X millions of dollars in the data.” It’s possible, but it may not be super probable. But having an objective, “We’re trying to solve this question, this business problem,” then now we have something concrete to anchor around, to go look for in the data and build this for a purpose and objective and so on.

0:42:38.9 ME: Well, I think we ought to go explore some more of these subjects together. So for today, what I want to say is thank you, and I look forward to talking with you again real soon.

0:42:49.8 JH: Thank you, Matthew, I appreciate it.

0:42:55.0 Speaker 2: The Long Way Around the Barn is brought to you by Trility Consulting, where Matthew serves as the CEO and President. If you need to find a more simple, reliable path to achieve your desired outcomes, visit

0:43:11.4 ME: To my listeners, thank you for staying with us. I hope you’re able to take what you heard today and apply it in your context, so that you’re able to realize the predictable, repeatable outcomes you desire for you, your teams, company and clients. Thank you.


Podcast, Part I: Vulnerable Storytelling to Advance Data Science

Show Highlights

You wouldn’t think a data scientist would tout vulnerability and storytelling as requirements for success, but that is exactly what Jacey Heuer has learned across multiple industries and projects that have failed and succeeded. In the first of this three-part series, Heuer shares that “what you think you know today should change tomorrow because you’re always discovering something more.”

Key Takeaways

Success in data science means:

  • Acknowledging that 80% of projects never make it out of production, and not because of a failure of science but a failure in communication and being vulnerable. 
  • Putting yourself out there by connecting with different people. 
  • Acquiring and honing new skills and behaviors that support a deeper understanding of systems thinking and the dynamic variables within those systems.
  • Always iterating and reinventing. The work is never done, and it’s never easy.

Three distinctions for roles and responsibilities:

  • Data Analysts work with stakeholders in-depth to understand the problems, goals, and outcomes needed.
  • Data Scientists focus on prototyping and exploring and twisting and turning data – looking for the algorithm.
  • Machine Learning Engineers productionalize the output.

Read the Transcript

0:00:57.9 Matthew: On this episode of The Long Way Around The Barn, we kick off a three-part series with Jacey Heuer, a data scientist with a passion for learning, a passion for teaching, and an unquenchable passion for helping leaders understand the profound impacts of data-based decisions. I absolutely loved my conversations with Jacey, and was surprised and highly interested when he told me how vulnerability and storytelling were two of the greatest attributes of a useful data scientist. In these podcasts, Jacey shares with us a little about his personal and professional journey as a data scientist.

0:01:37.6 Jacey Heuer: And what I feel today might change tomorrow, and so on. What’s sort of the core component of that is the scientific thought process. I’m not going get too far ahead, but that’s something that connects with me deeply. Part of the reason I’m a data scientist is this: Your vision, what you think you know today should change tomorrow, because you’re always discovering something more. That’s the scientific process.

0:02:00.8 Matthew: His views on the development of data science as a body of knowledge and professional practice, how companies can realize the value of data decisions, and what people need to explore, learn and pursue in order to become a credible data scientist. JC, thank you for taking the time to meet with us, talk with us, teach us and just include us. Tell us a little bit about… We know currently that you’re working in the data space on purpose. You love it, it’s a passion, it’s your journey, it’s your current chapter or multiple chapters, but tell us a little bit about your journey, Where have you been? Where have you come from? How did you end up here? And then tell us about where you are and where you’d like to be heading. Teach us about you.

0:02:50.3 Jacey Heuer: Thanks for having me, Matthew, I appreciate it. And I liked the emphasis on purpose there. So my journey started… I’ll go way back to start with maybe, right? So I started off as an athlete, very focused on athletics. Coming through high school into my undergrad, I was gonna play professional basketball. So I’m a pretty tall guy, relatively athletic, depending who you talk to. And so that was really my initial journey. Various reasons it didn’t pan out. I ended up graduating and getting my undergrad, and finance is kinda where I started. And so there’s a lot of connection into data with finance, accounting, stuff like that. It’s not a stretch by any means, to get to the data side of that discipline. I started off in financial analytics, and then decided to go back and get my MBA. And so I was getting my MBA at Iowa State around the time that data science was really becoming more of a mainstream term. It was noted as being the sexiest job of the decade and all that kinda stuff. Around this time is when it was first getting popular. And so that was kind of my initial motivation, to be like, “Yes, I like finance.” I’m getting this sort of data bug as I step out into the professional world.

0:04:14.1 Jacey Heuer: Going through my MBA course at Iowa State, I was introduced to some text analytics classes and courses, which is really sort of my first real step into what I would call real data science, kinda that movement beyond traditional business intelligence, financial analytics, stuff like that. So, got some exposure there out of that. I started to really focus on “What is this career path that I want, where do I want to go, and how do I do this within this data science space?” So I started networking, as sort of cliche as that can be, just getting my name out there, meeting people, stepping out, being vulnerable, putting myself out there, connecting with different people, and I was able to take a role in data analytics with commercial real estate, which is… There’s some traditional applications of that. There’s also some… From when I was looking for a data science sort of transformative application. That was a new thing in commercial real estate at the time, and it’s still a relatively new thing. That industry is relatively data-tight; data is held close to the chest, it’s not publicly available all the time. And there’s ways to go around that and all that kinda stuff, but that was sort of my first big opportunity and big step into this journey of data science.

0:05:30.6 Jacey Heuer: And so I was able to finish my MBA, start this role with this commercial real estate company, leading their international commercial real estate research publication. So we’re doing analytics on Europe, on Australia, on the US, similar countries around the world, understanding different forecasts around interest rates, around metro markets, all this kinda stuff, drivers of hotness in the commercial real estate industry across these metros and things like that. That was sort of my real first taste of a data science professional setting. I’m really diving into this knee-deep. From there, this was kind of in tune with when more universities were now starting to catch up and launch their graduate programs around data science, so I decided to go back, earn my graduate degree in data science. Out of that, it was just kind of a launch pad to keep moving forward then. And I’ve always had this kind of notion in my mind, as I’ve gone down this journey is, there’s currently this double-edged sword of, how often should you change? Should you take an opportunity? And how long should you stay in that current role before you feel like you’ve learned? And… What’s that balance of, “Am I going too fast? Am I going fast enough?”

0:06:46.7 Jacey Heuer: And to me, I’ve landed on that side of trying to… As mystical as this can sound, listen to the universe; not give too much thought to it and just kinda let it flow. So when an opportunity comes along, it’s an assessment of, “Does this really feel right to me? If it does, let’s take it.” That’s given me the ability to practice and step into data science and work in the data space across a few different industries. So as I’ve gone forward, I’ve worked in… I mentioned commercial real estate, financial services, e-commerce, now manufacturing, the energy industry as well, and been able to experience, really, different company dynamics, different sizes of companies, and how they approach data, data science, data management. What the nuances of changing a culture to be more open, to being data-driven, what does that mean? What are the challenges of that? And that’s really been what’s led me to this state, and I think what’s kinda guiding me forward as well. It’s listening to the universe, listening to the flow, accepting kinda what comes next, and then just kinda moving forward with that. If that makes sense, hopefully, but…

0:07:58.8 Matthew: No, that’s outstanding. One of the things that struck me, and you may already be aware of this pattern, and I’m just catching up to you. In order to be an athlete on purpose, you have to be aware of a universe level or a system level, whole system level, set of variables, and all of these variables in the system are dynamic. Some of them might be static, some of them are variable. And all of these things are learning new skills, honing existing skills, deciding to try and make some things, some behaviors, some quirks, some types of behaviors go away. But your goal was to take all of these system variables, understand these variables in the mix, and move forward in some way, shape, or form. Whether you tacitly recognized it at the time or not, it seems like, as a purposed, goal-oriented athlete, you were already a systems thinker. What’s interesting then is how you translated that systems thinking into another, more… Well, defined for undergraduate school degree, finance, which was also systems thinking, also structure. Did you do that on purpose? Did you discover it along the way? That’s an interesting map from my perspective, right off the bat.

0:09:22.2 Jacey Heuer: I would say that wasn’t on purpose by any means. It was more of a, “This is my personality, this is sort of this… ” Again, I… Not to sound mystical, but it’s sort of that sense of, “This just seems to fit as the next step, and let’s take this, and put myself out there and see what happens.” I think you hit the nail on the head, Matthew, when you talk about that systems thinking from an athlete’s perspective. It’s having that sort of top to bottom, bottom to top, thorough understanding of: How does the team work? How do the pieces come together? What’s that more macro vision, that strategy that we’re going after, and how do we deliver that strategy within these sort of subcomponents? And something I’ve noticed, as I’ve gone further in my career with data science… There’s… And I think this is… It’s common across many disciplines, many practices, there’s sort of the balance of… Those with the ability to really… To be the… To have that real depth of technical skill set, and can knock out, “This is my task, I can do that task,” and those with the ability to really see what’s the relationship with that task into the bigger whole and connect these pieces together. And I’ll say, from a data science perspective, the skill set to really understand, “How does this algorithm, this thing I’m working on, tie in to that business impact, tie in to the bigger whole?” That’s a valuable skill set to have.

0:10:56.3 Jacey Heuer: And I’ll say, for me, having both an MBA, data science master’s degrees, and putting those two together has given that sort of benefit where I can understand how, if I’m building this algorithm, writing this code, what’s the impact to the business? And how do you speak to that impact to build those relationships with those that are ultimately going to adopt this output? That’s the feedback that we want, that we’re seeking, and why a common statistic for data science is that it’s something like 80% of models and algorithms never make it to production. That’s a huge failure rate. And a lot of that is, you’ll do all the legwork, the foundational work, getting it up to that state, and then go to that last mile to get adoption, you don’t get that buy-in from the business; that relationship isn’t there, that trust isn’t there. And that’s something where, on the athlete’s side, as a basketball player, you know if that’s gonna happen, more immediate. You know if I’m taking the shot or I’m passing the ball to this person, they’re either gonna take it and shoot it and score or not. You know that they’re accepting your pass. You know it’s gonna happen. Data science side, it may not be evident or obvious right away. You may go through all this work, three months down the line, just to find out that what you were building doesn’t get adopted, and it falls into this abyss of what could have been data science.

0:12:26.9 Matthew: That map, from your bachelor’s degree in finance to then doing an MBA to get a broader perspective, it almost looks like a funnel, as I’m visualizing some of your journey, where the athlete himself was starting out as a systems thinker, so that’s already a wide funnel, if you will. And then finance was starting to apply structure and discipline, and honing some of that stuff, but just raw talent’s not enough to be a pro ball player. Just raw talent gets you down the road, but it doesn’t help you last. So somewhere along the way, you said, “I must focus, I must have structure, I must have purpose.” Somewhere, you chose that. To your point, listen to what you’re hearing and make decisions contextually, but you became aware of the need for doing something on purpose, and thinking about all of the variables, you moved into the MBA conversation with a data focus. The interesting thing about the MBA, from my perspective, is it’s not designed to give you the answer to all possible questions, but it is designed to make you aware of how very many different bodies of knowledge exist to just even make an operation operational and then healthy and useful.

0:13:44.9 Matthew: So you have this interesting blend between you want to be a competitor, a high-performing competitor, who is disciplined, to someone who’s now focused it to, “I understand math, I understand models, I understand the value proposition of an idea,” to then moving into, “Hey, there’s all of these things it takes to run a business, not just data stuff. But data helps drive, equip, enable, educate people to make decisions, but there’s all these other things as well. They all require data, but they’re all different types of behaviors.” You’ve walked into this data role, being aware of the need for systems thinking, of discipline, knowing that you’re not the only person in the company with a brain doing thinking, but then also realizing that the things that you’re creating need to be relevant to all of the other people in the business, or else it inadvertently supports that 80% of all models never make it to production. 80% of all shots taken never making it into the basket; that would be a fairly brutal statistic as a pro ball player. So in the data industry, that seems like some people are getting a lot of forgiveness, if you don’t mind my… What I’m saying there fairly directly is, 80% as an industry number? That’s pretty tough, dude. What are your thoughts on that?

0:15:09.4 Jacey Heuer: Yes, you hit the nail on the head, Matthew. And I think the mindset with data science, with AI… On one side, there’s a lot of buzzword, a lot of media coverage of it that drives a lot of it, and while the media coverage can be hyperbole sometime, the foundations of it are real. And the reality is that I think a lot of organizations, a lotta industries want to jump to, “Let’s just throw an algorithm at it, let’s just throw machine learning at it, and it’ll work,” without really realizing that the foundations, the data foundations underneath of that, the quality of that data, the governance of that data, the culture around managing that data, that is what drives the success of those 20% of models that get into production. It’s coming from having robust foundations in your data.

0:16:06.9 Jacey Heuer: And that’s probably the biggest distinction there, is that… Any model, any analytics that you’re doing, really, is a small set that, once that data foundation is in place, it’s much easier to iterate, experiment, prove value to your business partners, your stakeholders, and have a shorter putt to get to that adoption, and push through the end zone with that, and that, I think, is what gets lost in that 80% that doesn’t make it to production. As much as part of that’s maybe because of the relationship with the business, well, that relationship struggles because of the complexities that you’re trying to go through on the data side, and any of the confusion around “Why is it taking so long? Why can’t you just push the easy button?”, all that kinda stuff comes with that sort of messiness in the underlying data. Does that makes sense?

0:17:05.5 Matthew: It’s the sausage-making conversation, right? Have you ever been to a product demo? Many people have. Have you ever been to a product demo where all of the technical people said all of the technical things, but the people that were paying for the product development didn’t understand a single word that was spoken, like, “I know you said things. You seemed very excited about them. You seem confident. That makes me confident. I still have no idea what I just bought.” That seems like an easy gap that could exist in the data science world, to the executive leadership world inside a company, for example. For all of the executive leaders out there who are making decisions based on a single pane of glass, or a dashboard, or they’ve got a lovely, lovely, dynamic Excel spreadsheet with wonderful graphics on one of the pages in the workbook. For people that are trying to distill a whole business down to a single pane of glass, they may or may not be interested in the sausage-making. So how have you found, given all of your background and your awareness of these situations, how do you bridge this gap between, “I’ve got this data science stuff,” and “These guys are just looking for pie graphs”? How do you become relevant when they’re only using a single pane of glass?

0:18:26.3 Jacey Heuer: Yes. And that is, in many ways, the core of the challenge, that’s the art. And really, it comes from… It’s the relationship building, it’s the conversations, it’s the honesty around the vulnerability of letting these stakeholders know, “If we want to step forward into becoming truly more data-driven, changing the way we think about our decision making, our leveraging, and turn data as an asset, data as a resource and so on, what does that mean?” The reality of it is, you need to find that balance between that single pane of glass and the guts of making that sausage, and you have to pull back the covers a little bit on that, and the term I use, it’s the art of the possible. The being able to set the stage of, “This is the art of the possible, this is what we can do, if we have the strong foundation underneath of it.” And starting at that, “Here’s the shiny object, and now let’s peel it back and dig further into this and make that journey known, of what’s needed to get to that vision and art of the possible, and now let’s go and resource and attack these sort of sub-components that let us get that far.” And that takes clear communication and vulnerability.

0:19:46.4 Jacey Heuer: Again, I use that term a lot, because there’s no easy button for data science, for AI, for ML. As much as companies and vendors will push, “This is auto-ML, you can point and click,” all that kind of stuff. There’s a lot of work that goes in underneath of that, to make that work and work well for changing a business, changing the way they operate. Again, it’s giving that kind of clear vision of, “What can we bring, from a data science in advance and Linux perspective, to the organization?” and then laying out in honest terms, “These are the steps that we need to take, where the gaps are and how we can start tackling that.” Because it’s that vision that can hook someone and then going on that journey on, “How do you fill in those gaps, to get to that?” that’s the key, and making the partnership known.

0:20:43.7 Matthew: So set expectations, manage expectations, and in all cases, communicate and over-communicate.

0:20:51.1 Jacey Heuer: Correct. Iterate and iterate.

0:20:51.5 Matthew: And iterate.

0:20:56.0 Jacey Heuer: One of the key things I like to do when I enter into an organization, it’s go around and have these data science road shows. So meeting with different groups, different departments, and just educating them upfront, on, “This is the data science thought process, the data science project process. And what does that mean? And how is that different from maybe traditional software development or traditional engineering and things like that?” The data science means experimentation, means iteration, means going down a path, learning something, and then having to go back three steps and do it again. And so, it’s not a linear process all the time, but it’s very circular and it’s very iterative. And even when we get to the end of that path, we produce something. That thing we produced, may need to be re-invented a couple of months later, or you launch an algorithm and a pandemic hits, and what was driving that algorithm no longer has as much meaning because of the new environment. So you have to go and re-build that algorithm again and re-launch it again, because there’s new information being fed into it.

0:22:04.0 Matthew: There’s an interesting parallel inside organizations, which I imagine you’ve already seen and noticed because of your bachelor’s and your master’s. The idea of financial modeling, modeling itself and forecasting, whether it’s a go get a brand new vertical market, whether it’s segment a market, it’s create a new product and create demand for the product. The idea of finance has been around for a long time, and it’s understood by most, it’s discussed in undergrad and grad school, and even if people don’t go to university of any kind, everybody is familiar with, “You need to make more money than you spend, or else you’re upside down, you have a problem, you won’t last long.” But if I want to live for a very long time, I need to forecast. In other words, I need to say, “Based on the things I know today and the things I think I know about tomorrow, what will it take for me to get from where I am to where I need to go?”

0:22:53.0 Matthew: That forecasting idea, that’s an old idea, and it’s in companies already, today. And I’ve seen it done wonderfully and I’ve seen it done horribly, and the difference was communication, where somebody took the time to say, “Look, man, based on these 15 assumptions and these 17 system variables, which I don’t control any of them, and based on the things you think you want to be when you grow up, 19 months from now, here is version A, B and C of my forecast,” and people tend to accept that as, “Okay, given all of the knowns and the unknowns, this makes a lot of sense. You made me feel good. Okay, goodbye.” In the data space, it seems to be similar, but I wonder if that’s just a new enough idea that people don’t understand what they’re buying yet or how to use it yet, and so when you mentioned that, “Let’s just grab some MLs, let’s just grab some AI, let’s just grab that little algorithm and put it into my Excel spreadsheet,” I wonder if people don’t fully understand exactly what it is, what to do with it and how to make best use of it right now.

0:24:02.9 Jacey Heuer: I think you’re correct in every aspect of that. It’s sort of the shining light on a hill, shining object that’s sort of lingering out there, that I want to grab on to, and it sounds great, it sounds cool. And again, and not to discount it, it is ML, AI is real, the expected benefits of it are real, the readiness for some organizations to really adopt it, may not be as real. And I think that’s a key concept to keep in mind. Depending on the organization, there can be a lot of ingrained processes, ingrained mindsets. I’m going to look at the data, to justify or justify a position I already have. The confirmation bias. I already know what I want to find out, I’m gonna go find it in the data.

0:24:53.2 Jacey Heuer: So if I apply a ML model to that and it tells me something different, I’m not gonna trust that, because I have… I know what I already think, and that’s what I want. That’s one of the walls that, as we build data science into an organization, how do we tear that wall down and change that mindset to overcome that confirmation bias, the selection bias that may be present? And it may be built on years of experience, “This has worked for me for 30 years. Why would I change now?” Well, there’s more data becoming available, the industry may be changing, the environment’s changing, we’re in a pandemic, we’re in whatever it is, that’s the promise of data science, is, it’s quicker, more consistent, in many ways, more accurate decision-making that can come out of those models, those efforts.

0:25:48.4 Matthew: It seems like, to me, based on my own journey, based on the increasing numbers or classes of data that we continue to collect, that we didn’t use to collect, when we collect so much more data today than we ever did, and it’s only increasing, that at some point, the idea of a super smart financial controller or CFO being able to take in all of this multi-dimensional data and make sense of it in order to create a credible forecast, it seems like the role of the manual forecast will become less and less and less reliable, as the multiple dimensions of data that we collect continues to increase and not even at the same rates of speed. My guess is, is that we’ll just be in denial about the reliability in our ability to forecast multiple dimensions in Excel, as opposed to recognize that, “Hey, I want to do the same thing, but now with all of this data, maybe I need to go figure out what this ML thing is, or what is this AI thing, or… ” It just seems like the magic of the forecaster needs to change.

0:27:00.8 Jacey Heuer: What I think of, when you mentioned that, Matthew… I don’t know if I’d call it the magic of the forecaster, the mindset needs to change, maybe. It’s the base skill sets that go into this, go into forecasting, go into modeling, it’s the understanding of, “As I obtain more data and try to translate that into an action, translate that into conversation that a leader can take an action on, what are the skill sets I need, to be able to make that translation happen?” Because the data, the ML, the algorithm, as companies become more refined, more robust in their ability to build that foundation of data, that will continue to improve and become, I think, easier to get to, “This is my forecast, and it’s a more robust forecast because I’m taking in so many more variables, many more features into this forecast, and I can account for having an expectation of different anomalies and things like that to occur.” But my role as a forecaster now, has to be, “How do I translate that into meaningful action for the business and tell that story and convince the leaders of that action?”

0:28:17.0 Jacey Heuer: And I think that’s something where, academically… And there’s many boot camps and things out there, that build the technical skill set for data science, but what’s still catching up, is that communication, it’s that relationship building, it’s, “How do I tell the story in a way that’s actionable and that drives trust in my forecast, in what I’m doing?”

0:28:41.9 Matthew: In my mind, at least, it is similar to the technical people who demo a technical, they say technical things during the product demo, but somehow, they’re completely irrelevant to the people that are supposed to be benefiting from that whole journey, ’cause I didn’t say anything that mapped. Let me tell you about your five-year goals say this, your current books say this, your forecast says this, we’ve aggregated this data. After we take that data and look at it multi-dimensionally and we forecast it out differently, you have to take all of this giant universe of stuff and not talk about it and distill it down to something that’s just plain relevant. In other words, what I think I’ve heard you say so far is, you could be the smartest data scientist in the earth, and if you don’t have the ability to communicate, you’re in that 80%.

0:29:36.4 Jacey Heuer: Yes, you hit the nail on the head, Matthew. That’s the key right now, it’s that communication, I think, that drives a lot of that adoption. There’s pockets of, I think, industry spaces where that may not be as necessary. I think of, if a company is founded around data and data is at the core of their organization, I think of a start-up, think of any… Put your tech company in here. Generally speaking, I think they have a stronger data culture, because their product is data. But when you’re talking about many other industries that are out there, manufacturing, energy, in many ways, things like that, where it’s… You’re stepping into a legacy company, a company that may be 100 years old, and it’s going through this transition to become data-driven, that’s where a lot of that challenge, and even more so, the emphasis on that communication becomes pertinent to the success, to changing that 80% failure rate to 50%, to majority of these are getting implemented. That’s where, at least in my experience, having worked in those industries that have some of these legacy old companies, that’s a key to success, is that communication, that relationship building.

0:30:57.8 Matthew: So, that 80%, really, may more accurately reflect just an inability or a lack of success in setting and managing expectations and communicating. It’s not a failure of science, it’s just a failure of us being people. Being a person is hard and communicating is hard, it’s the science where we can find peace.

0:31:21.0 Jacey Heuer: Yes. Right. To put it another way, the art is what’s hard, the science is straightforward. I know the math, I know the linear algebra, all that kind of stuff, and that’s the way it is right now, as far as we know. But it’s the art of, now, translating that into something meaningful. That’s a big component of it.

0:31:47.5 Matthew: So I’m… John, I haven’t done the things that you do, and I’m not even intending to assert that I know all of the things that you do. If I’m able to start in a greenfield project, that I’m able to do all of the things the way I think they should be done and anything that doesn’t happen as it showed, is on me. Often times though, to your point, we end up in legacy situations, where the company is 100 years old, 140 years old, or it’s been under the leadership of a particular C-suite for the last 45 years, whatever, in all of those situations, that does represent, probably, growth, it represents constancy or continuity, it represents a good strong company, all of the things. But it also represents the way things are done, and it might also then, be an additional challenge. So for me, if I need to take all of the data in an enterprise and take that all together and meld it together and do a single pane of glass for a C-suite for them to say, “Aah, I can now make a decision.”

0:32:42.3 Matthew: The journey to get to that lovely single pane of glass, like Star Trek, just walk around, hold it in my hand and I can see the entire stinking ship on that one screen, it’s ridiculous. ‘Cause I can have 105 different repos, data repos out there in various states of hellacious dirty data, to, “Oh my gosh, just flush this stuff,” to, “That is gorgeous. Where did that come from?” to stuff that’s in data prisons, the stuff that’s outside the walls. In the worlds that I’ve walked, to get to that single pane of glass, that journey is not peace, it’s just a lot of stinking work. But what’s it like, for you?

0:33:22.6 Jacey Heuer: I chuckled a little bit at that, because it’s chaos in many ways. That’s the reality of it. Because especially these old mature companies, generally, I don’t want to put a blanket statement out there, but just given what I’ve worked in, and there’s nothing… It’s just the reality of it. It’s the way they’ve gotten here, they’ve been around… The company may have been around for 100 years. They found success somehow, to be here for 100 years. But the result of it can be, from a data perspective, that you have many different systems, applications generating data, data that’s… It’s not built for data science, it’s maybe built for reporting, it’s… Term I give it is, data exhaust. It’s just not really in a usable format, and there’s knowledge gaps. There may not be… The person that built the database may not be with the company anymore, or still using the database, but no one has any real knowledge of what’s in there. There’s data flowing into it, but how do we map it and get it out? Things like that.

0:34:25.0 Jacey Heuer: And the path that has been useful, in trying to work through that, drive a transformation into something more modern, more updated, more usable for data science, it’s finding those champions within the owners of that data. So where that data is owned, going out, and again, it’s back to communication, it’s back to the art, but its finding those champions and not to get too granular on this, but something that’s worked for us is, it’s working to establish a true data council, data stewardship, where you have this representation, where you have, instead of data being this by-product, this… It doesn’t have a forefront, a key role in the business, it now takes a step in the forefront. The ownership is established, and the connection to the goals of the organization are built out. So now I have this council of individuals representing the different parts of the business that are generating the data, and they have a voice in, “How is this being used?” and have transparency and clarity into, “This is how we would like to use it.” Well, the conversation started, “Then, well, this is what we can do. I didn’t know that. That’s interesting.”

0:35:44.8 Jacey Heuer: You start that communication through that council, through that stewardship program, that is the first step to getting to that foundation of a robust data layer. Now you can build that data science on top of… Build that AI and ML on top of… And start that transformation. What can be, I think, challenging in that, depending on the goals of the organization, it’s the time and resources needed to really do that, and that’s a mountain to climb in itself, is, “How do you convince of that story, that this is what we need to get to that next step with data science, AI, ML, all that kind of stuff?” That’s a journey in itself.

0:36:29.7 Matthew: Do you find, in your profession, that you’re asked or expected to, or you find the need to differentiate or define what is data science, what is machine learning, what is artificial… Do you have to differentiate these things, and how would you define that for us today, knowing full well that you may have broader and deeper things to say, than we’re all prepared to receive?

0:36:53.3 JC Heuer: I think of it this way. It’s not uncommon. Anything that’s new, there’s a fair number of examples out there, where three different people, you ask them to define something, they can have three different definitions of it. What does this mean to you? And it’s the same thing with the data science space. The way I break it down is in a couple of ways. On one level, in terms of data science and data analytics, it really falls into three categories. There’s sort of the diagnostic, descriptive sort of category pillar, which is, many companies will have some version of this, where maybe we have a SQL server, we can do some reporting, maybe we visualize it in Power BI or Tableau, we can see what happened. That’s really that sort of descriptive diagnostic.

0:37:43.7 Jacey Heuer: The predictive element, that’s where we’re taking that sort of understanding of the past and now, giving some expectation of what’s to come, we’re guiding your decision on what we think is going to happen. Putting some balance on that, confidence interval, things like that. And then the third element or pillar, is the prescriptive pillar. This is where we’re taking those predictions, now giving that recommendation. What’s the action that we think will happen, because of our understanding of the data of the environment? If we tweak this lever or turn this knob, we can drive some outcome, and that’s our prescriptive recommendations. We’re gonna decrease price 10%, we’re gonna increase quantities sold 30%, elasticity.

0:38:29.3 Jacey Heuer: That’s kind of at a high level, how I start to define that, is those three pillars. And when you step into specific roles, you think data scientist, data analyst, machine learning engineer, data engineer, decision scientist is out there now, there’s all these different roles and variants that are beginning to evolve, in it’s many ways. You think back 20, 30 years ago, with software development and sort of that path of defining more niche roles and areas of that discipline, data science and the data space is going through that. The key difference goes to, I think about defining data analysts, data scientists and machine learning engineer. I think those are three important roles to understand in the space. And data analyst is very much on the side of, “I’m working with the business stakeholders to understand a particular problem in-depth and sort of lay the ground, the landscape of, this is what we have in the data and how maybe we can help answer some of that.” A lot of it’s in that descriptive side of those three pillars I mentioned.

0:39:41.2 Jacey Heuer: Data science, that’s really that algorithm building. It’s the prototyping, it’s the experimentation, it’s going out and we’re taking this chunk of data, adding more data to it, doing clustering on it, doing segmentation, exploring this in any great depth in perspectives and twisting and turning it. And we’re trying to find that algorithm, that mathematical equation, where you can input data and get an output that gives us a prediction or some prescriptive action. That’s data science. And the machine learning engineer, that machine learning engineer, that’s who’s productionalizing that data science output. So now you have data analysts that are defining and understanding. Data science, building of an understanding, that, “Let’s put this into an algorithm.” The machine learning, taking an algorithm and putting it into production. Those are three distinctions that I think, get misunderstood, but are important to understand, from a leadership standpoint, from the design of, “What do I need to do data science?” Those are skill sets that are essential for success with this.

0:40:44.4 Matthew: What’s interesting to me though, is how you’re differentiating the data scientist from the machine learning person or ML ops, and that it sounds like when you were talking about the data scientist, this sounded like a software developer to some extent, to me, or a developer, which is, I’m taking this idea and I’m building it into a real thing. Then there’s these other folks that they take it out and move it into the wild, and that’s an interesting thing to me, because often times in the software development space, the people, there’s the business analyst who may have contributed to the definition of done or the direction, then there’s the folks that are building the thing. But often times those folks that build the thing are the same folks that have to move it out into the ether and then live with it and support it and evolve it. So are you suggesting that is not the same thing in the data space?

0:41:36.1 Jacey Heuer: I think you’re tracking with me, Matthew. I think you got it right. With the data side of it, a lot of it is because of that iteration, and sort of the, I don’t want to say burden, but the role of having to integrate this back into the software development process and manage that integration and maintain model performance. So you think of… I think of… If I’m building an application that… I’m gonna build a web app, for example. In many ways, I can build it, put it out there and it lives. There’s quality testing, things like that, but the application I built, is pretty well-defined, serves its purpose. If I’m building a machine learning algorithm and putting them into production, once that’s put out into production, it’s not the last version of that, that will exist.

0:42:28.3 Jacey Heuer: And so, the infrastructure, to be able to monitor that, maintain that, score that model, understand drift in that model. So what I mean by that is, monitor it for, “This used to be 90% accurate, now it’s 50% accurate. Well, what happened?” So, that’s the importance of this machine learning engineering and ML ops side of this, it’s taking that off the plate of the data scientist who’s focused on, “Let’s prototype this, let’s go and explore this world of data that’s out there and keep iterating on this,” and let the ML ops, ML engineering, tie this into software development, into the applications that exists in the organization, into the rest of the IT space, within the organization. That’s probably the key distinction there, and why it’s slightly different, I think, from the data side than what it might be in the software development side, if that makes sense.

0:43:22.6 Matthew: These things sound actually very amazing, JC. Basically, I’m gonna have to cycle on this a little bit, because at first, I thought you were saying, the data scientist is like a developer, but then that developer typically has to go and live with the things and iterate on those things. Whereas, it seems like you’re suggesting these guys are going to invent, create, evolve, but then someone else was gonna move it into the ether. So that makes it almost sound like one version of the word architect in the software world, which has its own loaded… English is hard. Quality, what does that mean to 10 different people? Cloud, what does that mean to 10 different people? Same thing.

0:44:02.2 Matthew: Here’s what I’d like to do, because our time is coming to a close for today. I don’t think we’re anywhere close to talking about a lot of the even more interesting things. For example, you being a practitioner. How would you advise, coach, encourage, teach or lead other people to introduce data? The whole point of data, data science, data management end of their organization. What are those steps? What does it look like? What is good communication? I’d like to talk to you some more and I’d like to do that in our next session together. So, we’ll save some of it for the next time, but first and foremost, I wanted to thank you for taking this time to teach us.

0:44:41.1 Jacey Heuer: Thank you for having me here today, and we’re just scratching the surface on this, and I’m excited to continue the conversation and go from there.

0:44:54.0 The Long Way Around the Barn is brought to you by Trility Consulting, where Matthew serves as the CEO and President. If you need to find a more simple, reliable path to achieve your desired outcomes, visit

0:45:10.3 Matthew: To my listeners, thank you for staying with us. I hope you’re able to take what you’ve heard today, and apply it in your context, so that you’re able to realize the predictable repeatable outcomes you desire for you, your teams, company, and clients. Thank you.


The Long Way Around the Barn

There is usually more than one way to achieve your goals. Sometimes, the path to the goal is longer than it needs to be because we are all challenged with similar things: We often see what we know or see what we want to see. 

In this podcast, we look for options and recommended courses of action to get you to your desired outcomes now. 


Podcast: A True Process for Leading

Show Highlights

In this episode, I visit with Todd Dunsirn who founded True Process, a medical software engineering company known for building a platform that integrates biomedical devices and captures clinical data. He sold the company to Baxter Healthcare in 2018.

Key Takeaways

  • Having a natural curiosity in other people opens you to new ideas and leads to life-changing opportunities. Those ideas can’t be forced and often arrive while doing something else.
  • Reflecting on your actions (what you say and do) is important as it impacts everyone in the company. 
  • Realizing everyone plays a vital role in a company. Listen to them, be humble, and empower them to do their thing (including making and learning from their mistakes).
  • Reinvesting in the company if possible. If you believe there is something bigger and better on the horizon, this helps ensure you have the resources to make it happen.
  • Understanding the financial state of your business at all times.
  • Building a company takes a toll on you, so take care of your physical and mental health.

Read the Transcript

0:00:58.0 Matthew Edwards: In today’s episode, I visit with Todd Dunsirn, an entrepreneur and founder of True Process, a medical software engineering company focused upon connected biomedical devices. Because of the growth, success, great products, services, and teams at True Process, the company eventually caught the attention of a potential buyer and Baxter Healthcare purchased True Process in 2018.

0:01:24.9 Matthew: So Todd, thank you for being here and taking the time to teach us about you and your journey. Welcome.

0:01:31.0 Todd: It’s great to be here.

0:01:32.9 Matthew: I’m interested, can you tell us a little bit about your journey as an entrepreneur business owner… Like, where have you come from, where are you right now and where do you think you’re heading? And I know some of that may be existential or philosophical but in general, where have you been and teach us.

0:01:51.8 Todd: Yes, so as far as an entrepreneurial setting and background, I grew up in that environment. My grandfather, when he came back from the war, he started his own tag and label business. And my father worked there with his brother. And they sold that, I think, in the ’90s sometime. And then my father and his uncle started another business in the materials converting space and they sold that business in 2001. So I grew up being surrounded by people who were working for themselves and working a lot, and putting in a lot of time. I remember my dad inventing machinery in his garage and just being fascinated by him building this printing press out of plywood and 2 x 4s, and metal rollers, and things like that.

0:02:42.2 Matthew: Wow.

0:02:44.0 Todd: So I was… And just having a mentor like that in your life, I was very fortunate to have that.

0:02:51.6 Matthew: That’s awesome. So were there explosions in your dad’s garage? Was it that kind of lab?

0:02:57.5 Todd: They were not… They weren’t… The only explosions were probably coming from him. [laughter]

0:03:03.7 Matthew Edwards: On to version 300.

0:03:05.5 Todd: Yes, when something didn’t go right. So that’s probably where that came from. So as far as where that brought me… So being like that. I guess I grew up thinking that I have to be an entrepreneur. It was one of those things… And I actually went to school for engineering. And when I think back about it, I really didn’t even give it much thought. I was just like, “Well, I’m gonna go to school for engineering.” ‘Cause my dad was an engineer. My grandfather was an engineer. So it was just one of those things where I just decided that’s what I was gonna do. And then after school, it was always just in my mind that I need to start a business or I need to do something, now. I segued into the IT space because growing up, I was also fortunate enough to have a father who supported my computer addiction, my video game addiction and all those things, and…

0:03:56.2 Matthew: Sure.

0:03:57.2 Todd: Playing with that stuff since the early ’80s up until today. I’m still curious and inquisitive, and always want to be learning the next new thing that’s out there. But as far as watching my dad do these things, going back to that, it also led to my… What I’m currently doing. And that’s working with my hands a lot right now. I recently… Well, in the last couple… The last year and a half, two years, I’ve set up a nice wood shop that I’ve been working on building furniture and kayaks, and… I’m restoring some old columns for my son’s house in Rochester, Minnesota, right now.

0:04:43.5 Matthew: Wow.

0:04:43.6 Todd: And it’s really… It’s just… After 15, 20 years of being in IT and working on things like that it’s… I’m finding it really enjoyable to be working with my hands and creating something that I can hold and see and other people appreciate it in a different way rather than… I mean, software’s great. I love it, but [chuckle]

0:05:03.1 Matthew: Sure.

0:05:03.2 Todd: There’s a different tangible feeling to something when you work on it for six to nine months and have it there.

0:05:11.0 Matthew: Right, agreed. That’s cool. So woodworking is currently where you’re spending your time?

0:05:17.6 Todd: Yes, so woodworking is one of them. I’m doing a lot of work… I spend a lot of time in Northern Wisconsin. So my wife and I bought some property up there that has a lot of forest and timber land, so I do a lot of work on that land. And… Whether, it’s driving a tractor and making trails or clearing trails, going foraging, hunting, gathering and all kinds of things, and just learning about the land too. And just doing some citizen science-type things about what’s on the land, what plants are on the land, what animals are on the land. Just a natural curiosity to just learn more.

0:05:58.7 Matthew: That’s awesome. So tell us then a little bit about… There’s one section of your journey here where you built this company and it took you amazing places. You learned a lot. And it’s led you to this place where… I don’t know if you may consider this a sabbatical, or if you’re in recovery, or you’ve just pivoted to new places in your life but tell us about the journey that led you to today where you’re doing woodworking and being a citizen forester and such.

0:06:38.6 Todd: Yes, so I think it’s that natural curiosity and meeting people, and talking to people, and learning their stories, like we’re doing now. I had several other smaller businesses, where it was essentially me or somebody else, up until about 2004. But prior to that, I had met somebody… My wife and I were out to dinner, and I struck up a conversation with somebody at a sushi restaurant here in Milwaukee, and we became friends. And after about a year and a half, this person called me with an opportunity. And then fast forward to 2004, that opportunity turned into what was the business that I had started, True Process. Having that desire to meet people and learn their stories and be open to new ideas led to something that changed my life incredibly.

0:07:34.9 Todd: Well, when we sold True Process in 2018, I told myself, I wasn’t gonna rush into anything or force a direction. And then when the pandemic hit, it made that urgency even less attractive, ’cause we’re all just kind of upside down and trying to figure things out. And I feel like successful business ideas and products come organically. They come through experiences. They come through meeting people. They come through dreaming. A lot of the ideas and things that I came up with at True Process and the product and just several strategies and things like that, they came to me when I was doing other things. When I was out for a run or walking or talking to somebody else about something.

0:08:24.6 Todd: It was never… It’s just something that’s never forced. So… I’ve… Starting a business to just start a business, to me, doesn’t feel right. There needs to be a spark, a passion that drives you. You wanna focus a lot of your energy into that endeavor and be enjoyable at the same time. So at this point… Believe it or not, I’m currently in the beginning stages of thinking about starting another small software product that’s gonna be very simple and focused on a niche need… At least a need that I have. Sometimes that’s how these things start. In the outdoor recreation space. And I’m kicking it around, I’m mocking it up, and I’m… I’ll put the pieces together and get something out there and see where it goes.

0:09:15.2 Matthew: So it makes sense then that there needs to be a spark, a passion, something that you discover or think of or see, or just something that gives you that motivation to say, “Hey, that may be something. Let me explore that.” And so this time that you’ve been spending since your last company True Process, which you built and eventually grew and mature and sold. And that led you to say, “Hey, that was fun. I’m gonna take a moment.” And then while you’ve been taking this moment, then I imagine if it’s similar to some of the other things you’ve talked about, you must be thinking of all kinds of amazing things, while you’re doing wood-working, or while you’re going to understand the land. You’re taking the time to think or discover, or to be encouraged or motivated…

0:10:09.4 Todd: Yes, it’s kind of… When you mentioned a sabbatical… It’s hard… I went to have an MRI on my shoulder, and the guy asked me what I do. And I didn’t really have a good answer for him, ’cause I don’t have instant feedback like, “Well, I’m a programmer.” Or I’m a this, and I’m a that. After I graduated from college… I got a job right away and started working like a lot of people, and just never took that time off. And I worked a lot. I tried to make things happen in the beginning years. And then when True Process started, it even got crazier and busier, and I ended up traveling a lot. And we had three little kids, we were just starting off. We had just bought an old house… And this was, all these crazy things going on.

0:10:57.4 Todd: And right now, I’m kind of enjoying just that downtime to kind of refresh… ‘Cause I… I feel like this… I’m about to turn 50, and I feel like this is that part. And this is that point in your life where you kinda look at where you are and what you wanna do, where you wanna go. And… Just… I know a lot of people who get to this point, and they’re like, “I just need a change. I just need to… ” ‘Cause it’s almost like a crossroads. It’s like I’m either gonna be doing this for the next 15 years or I’m gonna do something different. And it can be a scary decision. And I guess for me, thankfully, it was…

0:11:33.1 Todd: When we sold the company, that decision was kind of made. So I didn’t… It wasn’t a lot of thinking about it on my end. But right now I’m happy being home. My wife works at a great non-profit. I’m home, I’m more present. Granted, two of my kids are out of the house now, so I still have one… I still have one in high school, which I’m enjoying being around for him.

0:11:54.8 Matthew: That’s cool. So the True Process journey may be similar to other journeys, and so you can take this and dial it in to wherever you think makes the most sense. As a leader, did you find along that journey that… Well, what did you find along that journey in terms of as you were working to build the company, that meant you were working to build the people. What I have found through time is that working to build the people continues to show to me how many things I need to work on me. What types of things have you learned? How did you become a better leader because of your True Process journey?

0:12:33.3 Todd: Yes, a couple of things. So you’re exactly right, the company and True Process, and to say that… I wouldn’t even say I built True Process. I would say we built True Process. The people that were there…

0:12:49.4 Matthew: Sure.

0:12:49.8 Todd: And a lot of them were there for a long time. I think finding good people… Finding the people that you trust and empowering them and letting them do their thing. And my style is never… I’m not a yeller. I’m not a… I don’t get on people. I let people do their things and I let them make mistakes and I hope they learn from them, but I don’t lose my cool when it happens. And so I think that that is the… The biggest thing I learned along the way was to find good people and really, really listen. And not always… Not be the one talking all the time. Listen to what other people are saying.

0:13:35.3 Todd: And trying to be reflective of how your actions and the things you say and the things you do, how that impacts other people. ‘Cause another thing I learned is what you say and what you do, and even your body language when you’re working with people, it means a lot. I was always, I guess, somewhat humble, where I’ve thought, “Yes, I’m the CEO and I own the company and doing this and… ” But I didn’t feel like I was above or better, whatever you wanna say. I felt… And I always made a conscious effort too when I would talk to people. I hated the phrase, “So and so works for me.” Or these people work for me. I was always conscious to say, “I work with… ” Or “I’m on this team with these people.”

0:14:29.0 Matthew: Right on.

0:14:32.0 Todd: Just to make… ‘Cause it’s true. Everybody played a vital role in growing that company. So I guess listening and being humble and letting people do their things were some of the biggest things that I learned towards the end of that journey with True Process.

0:14:54.0 Matthew: It’s a journey. It’s just that simple. It’s a long journey.

0:15:00.6 Todd: Yes, and it’s like anything in life. I like to believe I was a better CEO towards the end of True Process than the beginning. And it’s even… I’ve been married for 25 years. I like to believe that I’m a better husband now than I was at the beginning too, just based on listening and self-recollection and acknowledging my strengths and weaknesses and faults and things like that. And that same thing applies to your professional career.

0:15:35.8 Matthew: Yes, agreed. Upon reflection then, can you think of times or moments or situations as a leader in your past where you think, “Jeez, I should have done that differently.” Or, I wish I had done that better. Or, it was a car accident. I’m sorry, I was driving the car and that just happened. Can you… Do you have some hot spots in your history where you reflect on like, “I’m putting a pin in that one because I can’t do it like that again.”

0:16:05.7 Todd: Yes, I think the biggest thing and it goes back to one of the most critical pieces… Or the most critical components of any business are the people. So I think the thing, if I could go back and when I think about some of the most challenging situations… I can’t think back. Nothing pops into my mind like, “Oh my God, if we would have just configured that differently, it would have all been better.” No, it was all based on, “Wow, if we would have had somebody else… ” Or if we wouldn’t have put with that behavior, we would have gotten further. Things would have been different. Things would have been better. I think just not tolerating certain behaviors and attitudes in certain people.

0:16:52.0 Todd: And towards the end of True Process. I have to say it was great. It was a great team and great people, and it didn’t have a lot of… But through the years, there were these challenges and those were the things that I remember where I’m like, “Wow, why are we putting up with this? This is continuously happening.” Or, “This individual is bringing this attitude or this behavior to the product or the company or the customers.”

0:17:20.9 Todd: I’m all for giving people a chance and helping to learn. But sometimes it’s just not gonna work. And I think sometimes, you know deep down right away it’s not gonna work, but I think we just… We let it go, we let it go, we let it go to a point where we have to do something because it’s not working. When looking back, I think I would do it honestly and fairly and amicably and just… This isn’t the right fit.

0:17:52.2 Matthew: So in those examples or that example you’re suggesting then you think you may have acted more quickly than you actually did at the time?

0:17:58.9 Todd: Yes. And we changed the method in which we hire over the… Over years. And it was a learning experience for us and how to assess getting the right people, getting the right people on the team. And towards the end, I think we had a really good way of doing that. And it’s hard. There was a period when we were growing really fast and it was just, “We just need to get somebody in here.” And that was not the right approach because we ended up getting a lot of people that I don’t think fit the culture or fit the mission or just the chemistry wasn’t right. And towards the end, the chemistry was really good.

0:18:43.0 Matthew: So then also you’re saying, “Hey, you’re feeling the sense of urgency, but still take a breath, take a few steps and make sure you’re making the right decision for the long run, not what looks like the right decision to stop the pain today.”

0:19:00.6 Todd: Yes, and making those… And when you need to make those decisions, the other thing, if I could go back would be to make decisions of change quicker, ’cause anybody that’s run a company, you know that takes a lot of energy out of you when you are dealing with those situations and you have to think about it. You’re generally caring about people, it is a personal thing when you have to talk to somebody about these things, but the longer you let it go, the worse it is for everybody. You’re better off to rip the band-aid off right away and get it over with and move on and focus on what you need to do.

0:19:34.9 Matthew: So then as it relates to some of the things you were doing at True Process, technology-driven, technology-oriented, I presume then, or I read into that, that data played a big role in the value proposition of the product itself. Is that accurate?

0:19:52.9 Todd: That’s exactly spot on. So the company started off, we had a consulting service side of the business where we did a lot of… We provide the strategy and execution for the roll out of these connected medical devices and systems for companies, and probably around halfway through the life of the company, we got the itch to get back into engineering and creating something. And I really believe that having a product was gonna transform the company and then provide a little bit more value to the company and satisfy even that just that engineering creativity need that we had. So the platform that we built was essentially a data collection platform that could aggregate all types of different medical devices data into a single space so that you could run analytics on it, do reporting on it, use it for research. The medical device field, it’s still very chopped up and disparate and there’s… Things just don’t communicate the way you think they do, maybe amongst a certain manufacturer, they do, but if you have five different manufacturers in a critical care setting, aside from maybe a few things, there’s very little feedback and data flying back and forth between these systems.

0:21:27.9 Todd: Some of these patient monitors that hospitals have are incredibly old. You’re generating data from serial ports, which we actually built things to collect from, and you’re basically taking it in like a… It’s like a fire hose, you just capture it all, we throw it in our database. But we also had IP-connected devices that we would connect you to, so we’d have to connect to these things and then aggregate that data.

0:21:55.5 Matthew: So large data stores?

0:21:58.8 Todd: Incredibly large and high frequency, too. This patient monitors were coming in at 500 readings a second, so we’re…

0:22:07.0 Matthew: It’s a high transaction type everything.

0:22:08.2 Todd: Yes, high transaction.

0:22:11.0 Matthew: So with that being medical too, then obviously that’s a highly regulated field in terms of privacy and security and so forth, did you have times when you were understandably and happily proactive about things, and did you find regulations changing out from under you? I mean, what role or what active participation did you guys take in saying, “Hey, data privacy, data security, this is a thing, and we’re going to go crazy making sure that it’s a thing.” What was your posture, your position? How did you manage all that? That’s a lot of data, by the way.

0:22:46.5 Todd: Yes, it’s a lot of data and fortunately for some of these devices there… The one thing you might have is a patient ID that sometimes that’s put into a device and that’s what correlates the patient with the data. We were able to anonymize that and strip that out, and we could line up the data so that they had a non-identifying tie to… If you had four devices, we could tie those together for this patient and it would essentially just be patient one or patient two. It didn’t really matter, so it wasn’t too complicated to do that but as far as the security and regulatory and those things, we did… We had a nice little certified development shop, which we invested a lot of time and money into, the product itself was filed with the FDA as a regulated medical device, and there’s different levels and things with all of that.

0:23:53.3 Todd: There’s certain classifications and we were on one of the lower ones with what we were doing. And a lot of it is how you define what the product is doing too with respect to how it needs to be classified and where that data is being used and what other systems that may be feeding. But it is a barrier entry to, I guess other startups or… ‘Cause we were essentially, I call it a bootstrapped company. We didn’t have any investors or big investors behind us and we built this platform from the ground up, so it’s quite… The time to do it, the time to put into a quality system, the time to put into regulatory filings and having the people that understand that on staff, and then the time to actually file those things and wait, it’s hard to… For a small company like we were to launch a product like that into the healthcare medical marketing.

0:24:55.3 Matthew: Did you find, through your experience, the regulations, the barrier to entry, however we categorize or characterize the idea, did you find that the extra hoops or the extra work you needed to do to conform with or be able to be attested against the regulatory compliance, the expectations, did you find that that added a definable amount of overhead or extra cost or complexity to your general operation, or was it something you just embedded and aid and it was an assumption? How did you manage that?

0:25:30.8 Todd: It was kind of built into the company, we knew what the costs were and we knew what we paid for to do that work. In our planning, you just know that this cost is going to exist and it’s going to be there.

0:25:48.6 Matthew: Yes, that makes sense. So you baked it in?

0:25:50.6 Todd: Yes.

0:25:51.8 Matthew: Part of your DNA?

0:25:54.5 Todd: Yes, and the team did a really good job too. Our quality system was pretty straightforward, pretty agile. We could get things done fast, but within… But also doing it the right way. A lot of our customers that we worked for were in these large Fortune 500 companies, and we were able to operate much quicker than them with regards to those types of things.

0:26:18.9 Matthew: So, I’ll repeat back to you, you correct me if I’ve mis-stated things or misunderstood. It sounds like what you said was the work that you did as a company, True Process, as a team of people, and the product and the output, the deliverable, if you will, boot-strapped. You had a lot of your own professional experiences that gave you the expertise to walk in, but to some extent, given the barrier to entry, the regulatory side of things, the fact that it’s medical devices and to your point, it’s a desperate ecosystem and in an emergency room or in a hospital, in terms of multiple manufacturers, classes of devices, they do or do not have interoperability, they may or may not be sharing same decade technology in some cases, those so many amazing complexities. What advice would you give to yourself, if there was another person out there is thinking, “I can do the next gen. I’m in. I wanna go do this.” How would you have coached you, but what would you say to the next version of you who then wants to walk in and make a difference?

0:27:30.3 Todd: Into going through the same… Going into the same field, same journey, what would I say? It’s a difficult one ’cause I’ve asked myself, “Would I wanna go back into that field?” And right after I was out of it, I was like, “No way, I’m not going… I’m never going back there. It’d be crazy to do that. I wanna do something fun.” That’s where you can crank out software and revisions and not have all that overhead and I guess I wouldn’t do anything differently than what we did. We were very fortunate to build the product by re-investing in the company, so if that’s one bit of advice I’d give to somebody else, I would say that was it. When the company was rolling up until we started the software and it was going well too, but we had some great, very profitable company moving forward, but a lot of the money was put back into the company, and back into developing a quality system and into getting our ISO certification, into developing the product a lot.

0:28:41.8 Todd: All that money was pumped basically back into the business now. I could have taken that out and distributed amongst everybody, but we kept it in the company for a reason and that’s ’cause I think we knew there was something bigger and better on the horizon. So that I think that was a good move and just live within your means and realize that you have a company that’s what’s providing and that’s what’s making things happen, so don’t bleed it dry, keep money in the company.

0:29:18.3 Matthew: Sure, that makes sense. In such a short conversation, I’m of course positive that you’ve glossed over so many amazing growth details that you had along the journey that may make you smile or cry dependent upon how deep you have to go to think through them and… So I know you’ve talked about the high points and some of the interesting things to me are that, it sounds like the most important thing that you did was figure out how to take care of people.

0:29:47.7 Todd: Yes, take care of people. One of the key components was having, and I guess this is along the lines of having good people, but having a good financial understanding of where you’re at in a business, and I was very, very fortunate I got to work with my brother for many years. He was our financial controller and CFO, and really kept the company organized from invoicing and tax standpoint and our interactions with the banks, just all those things, all of our healthcare. Just everything was very well done, and I’ve talked to other people. I’ve seen other people who sometimes in smaller companies, that’s kind of neglected or not paid attention to as well as it should be, but from the beginning, one of the first people that I hired was my brother. And not just ’cause he was my brother, but because I knew. I knew when he came in and helped me get organized when I started it and I saw what he could do, I was like, “I got hire him. I have to have him here,” ’cause number one I hate doing that kind of stuff, and number two, I’m bad at it, so that’s a recipe for disaster.

0:31:08.1 Todd: So being able to understand where you’re at, have somebody watching the numbers and managing the books and doing all that work, allowed me to go out and do things I like to do, facilitate the growth of the company rather than spending 15 hours a week trying to figure out how to do invoices.

0:31:28.3 Matthew: Sure. Well, that’s good. I’m assuming then that you had a great relationship with your brother, and that’s how you guys ended up working together, so that’s pretty cool you did get to work together.

0:31:39.5 Todd: Yes, we did.

0:31:41.9 Matthew: That’s awesome.

0:31:44.1 Todd: Yes, and great relationship, and he’s moved on. He’s moved on to even some great opportunities now, and it was a really nice experience ’cause he was actually my younger brother who I moved… I was the oldest, so there was about eight years separating us, so when I was in those crazy teenage older years, he was a little bit younger and I missed a little bit of his life when I went to college. So to join back up with him and work with him for so many years was really rewarding.

0:32:16.1 Matthew: That’s awesome, that’s awesome. So as an entrepreneur, that is your journey, that is, if it makes sense, that is your craft or one of your crafts, something that you pursue is… It’s not just “I am an entrepreneur, but there’s a series of things that I think about and study and do and explore and test and grow,” and that’s the act of, or the acts of that idea. What types of things do you do that… Do you believe contributes to you becoming more or you becoming a better entrepreneur, a better leader, prepares you for whatever the next chapter is. Where do you spend your time to become more, on what?

0:33:04.8 Todd: Yes, I think the one thing that kinda changed in my journey was just taking care of myself. Early on, I really didn’t. I wasn’t… I mean, I was an active kid and I was active in high school and then college and starting to work, and I didn’t prioritize that as an important part of my life. I remember when I started traveling a lot, that started to take quite a toll on my body, just being on the road five days a week, four days a week, and then five and then three and then four, and flying all over the country for years and years and years just took a toll on me. And I felt like my energy was going down, I felt I just wasn’t mentally acute as I should be, and I just made this decision one day, that I’m gonna change this and take care of myself and basically get in shape. Not physically, but even just mentally, and that was…

0:34:08.2 Todd: And that’s the one thing that has stuck with me even to this day, where it’s one of the things that I make the time for, and even when I don’t wanna do it, I do it, and I force myself to do it, just because I know, I know that’s what’s going to make me happier, give me more energy and keep me engaged. And the second thing I would say is, and this has been really hard during the pandemic, but getting out there and talking to people and meeting them, for instance, having a conversation with you, then the first time we talked was really… Was great, and I really enjoyed it, because I generally have this interest in meeting new people and hearing their stories, and no matter really who they are or what they’re doing, I find people’s individual journeys and stories interesting. And just learning from them and trying to just educate myself on the world around me, and also just to keep myself in check. I feel like that’s a big part of the journey, is to be self-aware of where I came from and the opportunities I had, and how fortunate I was to have the upbringing I did and the people around me, and being thankful of that, and just going through life now, looking for opportunities to pay that back or pay it forward, and if somebody wants to talk… I remember… When you start off in your late 20s and you’re trying to start a business, it’s amazing the amount of doors that just get shut in your face.

0:35:58.7 Matthew: Yes, maybe rightfully so, ’cause you don’t know what you’re doing. [laughter]

0:36:03.2 Todd: But I’m not that kind of person now. I’m busy and I’m in a different phase of my life, but I think it’s important to make time for people and to help people out with advice, and just give them a little bit of your time to make an introduction, whatever it is, just to… ‘Cause I was really fortunate to have that shot and to have that opportunity, and I’m hoping someday that I can continue to do that for other people.

0:36:39.2 Matthew Edwards: Right on, that’s awesome. So you’ve offered up some great lessons that you have found along your journey as an entrepreneur, as a leader in particular, that other people can learn from. Do you have any parting thoughts for us?

0:36:56.1 Todd: I think it’s kind of just pulling everything together and amplifying the… As a business owner, as an entrepreneur, as a CEO, whatever it is, and it doesn’t even apply just to those people, but to stay curious and stay kind, and just understand that everybody’s coming from a different background and people have different experiences in their day-to-day lives, and just try to be understanding and empathetic towards that. I think when you take that approach, it lowers your stress level and it lets you see the world, it lets you see your business, it lets you see the challenges a little bit more clearly, and removes those stressful things that happen every day that probably don’t need to be as stressful as you perceive them to be at that day.

0:37:52.5 Matthew: That’s good, that circles back to keeping yourself in check includes meeting new people, hearing about their stories, and that gives you context for whatever lenses you’re looking through at the time. That’s a good call out. You’ve had a fun journey, and now you’re thinking about something new, now obviously we’re not gonna ask you to share all the details and all that type of thing, but it’ll be exciting to watch and learn what sparks your interest and what’s next for you.

0:38:23.8 Todd: Yes, I actually, I’m on the board of a non-profit recreation ski area up in Northern Wisconsin, Minocqua Winter Park, and I’m on a committee and we’re working on some things and we’re trying to organize some information and data and things that relate to the park. And it’s exciting because there’s very little pressure doing it now as back when I first started. When I first went into business for myself, I’ll never forget, my wife and I were expecting our first child, and we just bought a house. We were living in an apartment in Milwaukee and we bought a house. And I had a job at some electronics company out in the west side of the city, and I came home and I said, “I think I’m gonna… I think I’m gonna quit my job and start a business.” And my wife looked at me and was like, “Yes, we’ll be fine.” Which is… And she’s always been there for me, always been the greatest supporter of any crazy idea that we’ve ever had together or I’ve ever had. So it’s different doing it now where… I’m fortunate to be able to do this and set it up and not have it be the thing we’re relying to buy diapers with, or better, buy food.

0:39:44.3 Matthew: Right, right. Well, that’s outstanding. Todd, thank you for taking the time to talk with us today, to teach us about your journey, to share some of your insights, and some of the learning things that I take away from this is, take care of the people on the team, stay humble, stay self-aware, take care of yourself. Those are some of the highlights for me, is that I know that I can directly apply to my own journey even this afternoon.

0:40:12.2 Todd: This was great, I really enjoyed it.


Podcast: The Makings of a Great Agile Coach

Show Highlights

In this episode, I visit with Damon Poole, who has provided Agile coaching to countless people at some very recognizable companies. He opened up about his journey in Agile, as well as what led up to him publishing, “Professional Coaching for Agilists: Accelerating Agile Adoption,” with Gillian Lee (available at InformIT as well as other places you’d expect).

Key Takeaways

  • Effective coaching helps people move forward when they are stuck. 
  • Teams who are coached do move faster.
  • Great coaches have qualities that make for great humans. No one embodies all of them, but you work on building better relationships on your teams and in your personal life.

If you are interested in applying both agile and coaching principles, consider reading the book. Preview sample content on InformIT.

Read the Transcript

0:00:58.4 Matthew D Edwards: Today, I visit with an old friend, which means to some extent, I’m dating both of us, but hopefully you our listener, will find bits of wisdom in this episode and the journey that led us here. Damon Poole has provided Agile coaching to countless people at some very recognizable companies. EMC, Capital One, Ford and Fidelity. He speaks everywhere, and he’s even virtual, thanks to our new normal, today. He’s also an accredited instructor with the International Coaching Federation, ICF. I invited him to visit with us because he and his collaborator, Gillian Lee, have published a book, “Professional Coaching for Agilists: Accelerating Agile Adoption.”

0:01:43.7 Matthew D Edwards: We are providing links to where you can buy this book at the best price. Damon isn’t expecting to get rich off the book, he’s very excited to get the book into your hands. So he wants to help you save money, get the material, learn the material, learn how to become more. What he and Gillian have done is put out a great book for people who love Agile, want to be better at it, and want to help those around them get better. The focus is professional coaching, and the book even includes coaching exercises. Today, I visit with him about his journey in this space and how he continues to advance himself, the people around him, and professional coaching itself. Welcome Damon. Thank you for being here. Would you tell us a little bit about your journey as a professional, like in particular, a professional who seeks to master his craft.

0:02:33.5 Matthew D Edwards: You and I met a long time ago, and we talked about a lot of different subjects, and we haven’t talked again for quite a while, and so there’s a whole lot of catching up to do. But even back then, you had a lot to teach, because I learned back then from you in terms of configuration and change management conversations. Will you tell us a little bit about where you’ve come from, where you are today, where you’re heading, just in terms of your journey.

0:03:00.1 Damon Poole: Sure. Well, these days, for whatever reason, I like to say I was born a programmer. [chuckle] I guess that’s to distinguish from mostly where I am today. So I didn’t realize it at the time, but I walked into… Actually, a little bit of this story is in… Actually, it’s not in… It’s not in the book. It’s in Bob Martin’s book. But anyway, walked into an appliance store and unbeknownst to me, I was doing pair programming. I’d never programmed before, but that’s actually sort of how I got into it. Some guy was writing a Star Trek program, and as an 11-year-old, I was pestering him, “Well, what’s that? Well what’s that? Well what’s that?” I was super fascinated with computers and I’d never really seen anything like that quite that close. And after about 20 minutes, he asked if maybe I couldn’t do something else, so I was quiet.

0:03:50.3 Damon Poole: And then eventually I said, “Hey, I don’t think that’s right. What is that?” And he goes, “Oh.” And he made a change. And he goes, “Oh, that’s it.” And then he asked me, “How long have you been doing this?” And I said, “I don’t know. When did I walk in?” So that’s… Having an adult have that kind of look on their face… I was like, “Wow, this is pretty cool.” And so I programmed only in small groups for many years after that. Then I discovered this waterfall thing. We started out shipping every couple of days, and at the peak we were shipping every 18 months. And somewhere in there was where I discovered Agile for the first time. And I… At first I thought it was evil. At first I thought that… We were getting requests like, “Couldn’t you process request for history faster?”

0:04:40.9 Damon Poole: Like… Who processes… Who needs… Well, it was continuous integration… Stuff like that. But eventually I saw the light and it was thanks to hobnobbing with folks like yourself and others. So I started to switch from technical person, to more product person, to more Agile person. And so I went full on Agile for quite a while. Then I got kind of tired of people not really getting the point of Agile… I had just banged my head against that… That wall too much. So I definitely learned a lot. And along that journey, I decided it was time to start earning enough to put away for retirement again. Through serendipity, I got into teaching Agile coaching again. That’s been fascinating, I love that. More recently, as the title of the book suggests Professional Coaching For Agilists, I’ve gotten into professional coaching.

0:05:37.3 Matthew D Edwards: Tell us a little bit about where you’re heading… So in your current company, your current role, responsibility, how do you define what it is you’re doing today, and where are you heading with it? Well, even lead… Teaching us what led you to the book.

0:05:53.0 Damon Poole: Well, that would be Bob Martin. [chuckle]

0:05:56.0 Matthew D Edwards: Okay. Alright.

0:05:58.9 Damon Poole: Among other things, but… It’s kind of the interesting part of the story. So he and I kind of loggerheads on Facebook. We’ve known each other for a long time, I guess. He came to do a talk for us in Boston as part of what’s now Agile New England. So he had this new book coming out, Clean Agile, and he asked me to review it. I guess, because he figured if we were at loggerheads and I was telling him what I thought and then I would do the same thing for his book. So I said, “Alright, I’ll review your book.” And there were two things in it that I kind of objected to, which he said… He said there was no need for Agile coaches. “Okay.” And the other one was something about scaling. And I sort of strongly objected to those two things. And so I thought that was that, and then he says, “Hey, you know, you seem to have a pretty strong alternate opinion there, how about if you wrote a bunch for the book?” And I was like, “Oh no, what have I done?” [laughter]

0:06:57.4 Matthew D Edwards: That’s what I get for talking.

0:06:58.7 Damon Poole: Exactly. Oops. [laughter] So I wrote up, I don’t know, 10-ish pages for that. And then after that, I was like, “Hey, you know, this might be the start of something.” And Gillian, my co-author has always been sort of pushing me in that direction. And so she added her shoulder to that, and so then I said, “Well, if you come along with me, then fine.” So that’s how that got going.

0:07:23.1 Matthew D Edwards: So the book came out just recently.

0:07:25.3 Damon Poole: Yeah. It’s actually still in the process of coming out, just a funny side story there. So it’s been out on InformIT for quite a while as an e-book, and then shortly after that on Amazon. The funniest thing was, it looked like it was for sale and I went through the process to see what was going on, ’cause people are always asking me, “Where is it available?” And it gave a strange shipping… Strange shipping option, which I’d never seen before, it was like a… Scheduled delivery or something. And I clicked on the learn more and it said, for bulky items. And I’m like, “Is this a bulky item?” So it took me a couple of days, but finally I noticed in the specs, it said that it was like 8 feet by 6 feet by 3 feet, and it weighed 20 kilograms or something. So clearly somebody miskeyed that and…

0:08:13.7 Matthew D Edwards: Wow.

0:08:14.6 Damon Poole: Yeah, so that was pretty humorous.

0:08:14.6 Matthew D Edwards: So the graphics must be amazing in that version of the book.

0:08:19.5 Damon Poole: Right… [laughter] Very.

0:08:23.3 Matthew D Edwards: Fold out everything. So your difference of opinion or different view on the value of coaching from Bob Martin’s is one of the things that led you to say, “Hey, maybe there’s something here, I should explore this a little bit.” And you had been doing coaching long before you decided, maybe I should write something. Is that accurate?

0:08:46.3 Damon Poole: Well, it depends on what the meaning of coaching is.

0:08:49.8 Matthew D Edwards: Fair enough. Alright.

0:08:51.5 Damon Poole: Yeah, I think I’d use the term coach… There’s the role, Agile coach, and then there’s coaching. Actually, not everybody knows that not everything an Agile coach does is coaching. But it gets confusing as to what it is. And I think the simplest way to define coaching is the thing that… Anything that you do that helps another person move forward that has nothing to do with your own expertise, other than coaching. And usually people are like, “Well, what’s the value in that?” Which is kind of difficult to define, but pretty straightforward to experience.

0:09:31.3 Matthew D Edwards: I wonder if that can be likened to a concept that Gerald Weinberg had in one of his consulting books, where he called it, ‘the Jiggler’. [chuckle] In that illustration, what he talked about was the idea of a running toilet, and how sometimes the only thing that you really had to do to get that toilet to behave in the correct way was just go jiggle the handle. And then one of his consulting conversations throughout that book, really what he was talking about was sometimes your role in an organization is to just help facilitate a flow or to just unblock something [laughter] previously blocked and it didn’t require amazing knowledge and experience and all kinds of crazy stuff. It was just fresh eyes. You just jiggled the handle a little bit, and then people were able to move forward and evolve and become more than they were prior to that. So I wonder if those are similar.

0:10:34.0 Damon Poole: I don’t know that I wanna sign up for the title of toilet jiggler. [chuckle] But… Gerry Weinberg.

0:10:40.9 Matthew D Edwards: Okay. Fair enough.

0:10:43.2 Matthew D Edwards: Awesome. I’ve dabbled in some of his books. The one that I’ve read through twice and I always recommend is Secrets of Consulting, which is not the best name, ’cause people say, “Well, I’m not a consultant.” But that book is such infotainment. You get knowledge and you laugh all the way through and you’re like…

0:11:00.1 Matthew D Edwards: Yeah.

0:11:00.9 Damon Poole: I think this is just a folk story. Oh, oh, there’s the punch. Oh, that’s good. Really wonderful book.

0:11:07.2 Matthew D Edwards: One of the roles or responsibilities that you’re suggesting, more or less a selfless role inside an environment, I think, is what you were saying. Which is what you’re doing isn’t necessarily serving you, you are being an enabler in that environment, and it may or may not directly benefit you but you’re directly benefiting them or that journey or that path they were walking.

0:11:32.2 Damon Poole: Absolutely. And I think actually, as an Agile coach… And when I use the term Agile coach, I would include Scrum master and RTE and various other things. Anything where you’re helping an organization or a person move forward in Agile and you’re using a coaching mindset. I think we all have an ego to some degree, shape or form, and there’s nothing wrong with that, right? We want to help people. And I think one of the ways that in anything new like Agile, we want to help people… is sharing our expertise. And then people say, “Oh hey, thanks for that expertise. That was really helpful.” And we might pride ourselves on that expertise. But I think the pure coaching side is that it’s not about, did you share expertise or not sort of leaning more towards the, did the person get what they were needing, whether it came from them or you or…

0:12:31.3 Matthew D Edwards: So in your experiences have you found… Or what types of difficulties or challenges have you found when talking with clients or potential clients or even advising someone else on why hire an Agile coach. Have you ever experienced resistance or chafing or difficulty in explaining why hire this person, this…

0:12:57.5 Damon Poole: Never. It’s always super simple. No. [laughter] It’s the biggest lie I’ve told this week. [laughter]

0:13:09.6 Damon Poole: I don’t know that anybody actually ever wakes up in the morning thinking, I need coaching. People might think other people need coaching. But there’s a couple of issues there. Like what is coaching? Coaching as a profession has really only been a more recent thing, like the early to mid-90s and before Agile, like, life coaching, executive coaching, coaching from an International Coach Federation perspective. So that’s an issue. And then it’s kind of a support service. So you’re not actually producing any code generally, unless you’re a technical coach… Technical coaches will do that, but that’s not really their main point. To some degree, it’s kind of like what does a manager really do?

0:13:55.2 Matthew D Edwards: But we have plenty of those. So quantifying the value is… Is kind of… It’s like the chicken and egg. If you don’t understand the value of Agile, then understanding the value of an Agile coach is difficult. And how do you understand the value of Agile, part of it is by getting an Agile coach. So that’s a hard problem. One of the biggest victories at Eliassen, and I’m sure other places, was when we came out with this thing, it’s a mouthful, but the Eliassen Maturity Matrix. And that originated from a couple of dozen coaches at Capital One getting frustrated with the hundreds of teams and spreading the coaching out way too much.

0:14:37.8 Damon Poole: It was too thin. So we were getting paid and that was great, but we felt like we could produce more value. So we developed this way to help the organization teams and individuals understand were they moving forward or not. And it was clear that un-coached teams did not move forward as fast as coached teams. Teams that got a concentration of a coach for an extended period of time, did the best. So that was the best ROI. So that was super helpful, and that’s one of the best ways that I found to sort of quantify that value. Doing it ahead of time, super hard. Once you’re in there, expanding, much easier.

0:15:20.6 Matthew D Edwards: Is one of the things that you wanted to do with your book or that you’ve done with your book is to just help bring clarity to say, “Hey, I can’t solve all of the things in all of the world, but as it relates to this idea I’d like to teach you about this.”

0:15:34.4 Damon Poole: So the book starts out saying… It basically literally says, “Forget about Agile and coaching and everything for a moment, and think of people that when you’re stuck in whatever you’re stuck in, maybe a personal issue, who do you reach out to?” And if you think of a person you reach out to and a person you don’t, and you think of their qualities and different… That are different. Like this one listens. This one is always like, “Mm-hmm, mm-hmm, I’ve been there… I’ve done… Okay, here’s what you need to do.” And then a week later, they’re like, “Hey, did you do it?” And they’re like, “Oh man, leave me alone.” So that first person, those are the qualities we look for in a coach, and sort of, taken to a very high degree of intentional purpose. And it’s kind of a long list. Like, on the don’t side, it’s actually kinda easier to list. Don’t interrupt, criticize, discourage, judge, evaluate… A whole bunch of things. And actually… Oh, oh, and don’t give unsolicited opinions. Actually doing all those in the same person… super hard. [chuckle]

0:16:36.5 Matthew D Edwards: I was just evaluating that in myself, thinking, first I should memorize the whole list, and then second, I’m curious to what extent I do or do not exhibit these characteristics in whole or part or in combination. I think that’ll be interesting. I might not… Well I expect there to come some humility with that realization.

0:17:00.6 Damon Poole: It is. And it’s not like anything against anybody, if you have things on that list, it’s really just things to think about. It’s a journey.

0:17:15.1 Matthew D Edwards: Right now, what I’d immediately mapped to is I asked a good life-long friend of mine a long time ago, what was one of the most important things he learned along his journey of being a parent. And he said the most important thing that he had learned was knowing when to shut up. But the way he communicated it was sometimes you need to actively shut up because they need time to think, they need time to process, they need time to consider options, and they don’t need you talking right now. And as I moved through that from the parent, I realized that that also applied to just about every relationship in my whole life, professional and personal, knowing when to talk, knowing when to shut up. It could, of course be said far more elegantly than shut up. [chuckle] But…

0:18:01.7 Damon Poole: Maybe not as clearly…

0:18:03.1 Matthew D Edwards: He was being… Well, he was being direct with me. I am dense sometimes, and so it was direct advice. But it sounds like maybe similar to what you’re suggesting, which is knowing when and then choose.

0:18:16.6 Damon Poole: Absolutely, there’s a lot of dimensions in what you just said. We could parse that all up and that could be an offering right there. Just what you said. So one dimension there is… Think about… It sounded like that took a while for that to sink in. It took a while for you to practice it, and all the while… kind of like what is even the value of this. That’s absolutely part of coaching. And oh… And you also mentioned the dimension, it sounds like it changed who you were as a person. It affected other interactions. And a lot of coaching actually is, not that you need to, but that you want to change yourself in certain ways. And actually, what you gave, as an example is one of them… To get accredited by the ICF, you can’t fake it. If what you were just saying was difficult, you wouldn’t make it. You have to do a 30-minute recording in which you’re exhibiting that all the way through and that’s hard.

0:19:15.7 Damon Poole: The other aspect of that, which I think is at the root of value of coaching, not necessarily Agile coaching, but professional coaching, is what really is the value to the other person if you’re not saying anything. And the way I would look at it is exactly what you said… The talking through, the thinking through. There’s a certain amount of that, that you can do in your own head with no other human around. But the way we’re built… And I don’t know the brain science on this, but it’s born out and you can use your own experience on this. The way that we’re built as humans, we actually are better able to think things through with another person just sitting there.

0:19:58.7 Damon Poole: I don’t know why, but you think about… There’s things that when you go to articulate them you’re like, “So it’s simple. It’s just… ” And nothing comes out. And you’re like, “Oh, I don’t actually know how to articulate that. Let me think about it.” So there’s just this process that with the person that’s there actually listening to what you’re saying, you can do some things. And then if in their response to you, they skilfully are able to leverage that… And I don’t mean paraphrasing for instance… In coaching paraphrasing, is actually bad. But asking a question that shows that you understood. So let’s say you listen for a bit and then you ask a question and the person just goes like this, “Ahhh… ” And then they’re just silent. So you caused them to think of something they weren’t thinking of before, because you listened to them. So you didn’t add any knowledge, but you helped them move forward, and that actually has value. And you can think of those times… Those conversations you had, you were like, “Oh, that person was really insightful.” But actually the new idea came from you.

0:21:12.0 Matthew D Edwards: Interesting. You know, there’s a sales methodology, if you will, called Challenger Sale. And in that one of the things that they articulate in that whole process is, is in order to make a sale, your responsibility is to help someone see in a way they had not previously seen. And the way I visualized it was, if I were to say something to you, and that made you turn your head to one side and then turn it sideways like, “Oh my gosh. I didn’t even know that was a room in this house. And now I need to just figure this room out. Where does this room come from?” And all of that… You can see that going across their face. But sometimes it sounds similar to what you’re suggesting, which is a role… A role of a coach is to help one… Someone see also. So think and see, but to be this non-intrusive encourager, if you will. That seems like, actually, a very hard role.

0:22:20.4 Damon Poole: Yeah, it’s super hard. And one of the things that makes it hard is… Like I said, nobody wakes up in the morning looking for coaching. Generally… People ask me this in classes all the time. They say, “Okay, okay, but… Do you ever find that people come to you and they’re not looking for coaching, they want your… They want your advice.” Or they want expertise. And I say, “Yeah, absolutely, 100% of the time.” Zero percent, people are looking for coaching. So what I say is, as coaches part of what you’re doing, is coachee education… You wouldn’t tell people that you’re doing… Well, I guess that’s what I’m doing right now. But you don’t generally tell people you are doing coachee education. You can’t go full born to coaching with somebody that doesn’t understand it or want it. So you have to find bridges to that.

0:23:09.7 Damon Poole: And one of them is… One of the simplest is, most people when they’re starting coaching, have to learn that actually, they initiate the transfer of knowledge far sooner than anybody asked for it… So if you just hold back for a while, you’ll be providing coaching value that you didn’t even know that you could do. Because as soon as you see a chance, you’re like, “Okay, here’s some knowledge.” Have you tried this? What about that? Right away. People generally don’t realize that it’s them holding back that is the first thing they can work on.

0:23:46.2 Matthew D Edwards: I’m going to have to sit and think about these things after we’re… When we’re no longer talking… You’re giving me a lot to think about. These are good. So in your journey, you’re currently enjoying and finding the value in helping other people through professional coaching. Is that an accurate statement?

0:24:05.9 Damon Poole: Yes, absolutely.

0:24:07.6 Matthew D Edwards: So do you feel like you’ve found a passion?

0:24:10.1 Damon Poole: Oh my.

0:24:11.5 Matthew D Edwards: You’re passionate about this.

0:24:14.6 Damon Poole: One of the things that I like to ask people when I’m doing Scrum training or Agile training is this idea that people should specialize. I ask people, raise your hand if you want to do what you’re doing right now for the rest of your life? I’ve never ever seen anybody raise their hand. I think, we all… We have certain passions, but I think we can learn new passions… We’re always learning something new. So for me, I would say that, A, this is my current passion, but B, it also was sort of each passion led to the next one. In programming, unless the design is given to you, there’s a certain amount of design… So programming, design, product management, business stuff, Agile, Agile coaching, coaching. So it’s been sort of a progression of passions. So yeah, I’m very passionate about it.

0:25:10.9 Damon Poole: And the interesting thing that you see from pure coaching is you see a much more human side of people. People come to you with, “How do I keep the product owner from double stuffing the sprints?” That’s like, I’m over and over again. How do I get people to show up to stand up some time? How hard is Scrum really? It’s super… It’s stupid, simple. Well, then, why isn’t everybody doing it? Well… ‘Cause there’s all this human stuff in there… That’s the way we’ve always done it. I can’t let go of control. So that’s all coaching stuff that’s very human-oriented. So I often see people… A side of people that you wouldn’t see when you’re just trying to solve two plus two… What is two plus two? Oh, it’s four. Oh, wow. Right… So I really enjoy that. Seeing the human side of people. People sometimes… You know… A tear in their eye. It’s beautiful.

0:26:08.7 Matthew D Edwards: So that makes sense that you’ve been on this journey that has led you to here so far. And where it leads you, next of course, makes it sound like you’re just like every other human, which is this journey composed of moments, and ideally those moments give you choices. And you’ve made some choices and you’ve had some good experiences, and this led you to learning about you, which also then led you to eventually write a book. Taking the time… To your point with the Scrum stuff, the human element is what’s difficult about Scrum. The recipe for Scrum is pretty easy to understand. It’s the human aspect of all of these things that’s hard. For someone who thinks that they want to become a professional coach, what advice would you give them?

0:27:05.6 Damon Poole: I may seem a little self-serving, but prior to… [laughter]

0:27:12.0 Damon Poole: But bear with me here. I’m fully aware of what I’m saying. Prior to our book coming out, I used to recommend… Well, I still recommend. It’s a great book. The Co-active book. Co-active Coaching. Amazing, amazing book. We got a lot of inspiration from that book. One of the things that I like about that book is it’s left to right, soup to nuts, top to bottom description of pretty much everything in professional coaching. Not to the same level of depth you get to in a 60 or 125 hour course. But in six to eight hours of reading, you’ve covered the landscape. And they’re not trying to sell you anything, they’re not pushing you towards training. It’s… They’re not leaving something out. It’s not… Two-thirds is all about how to market yourself or whatever. Our book from… It comes from a different perspective, but is very similar to that.

0:28:06.0 Damon Poole: A soup to nuts, not trying to push anything, and it covers the whole topic in whatever depth you can in six to eight hours. And so we’re not gonna get rich off this book. Books don’t generally make a lot of money unless it’s… It’s not a romance book or something… So I don’t think you can beat the knowledge ratio for the dollar. So that’s a really great place to start. Actually, you could even just read the first chapter and get a sense of like, I wanna keep going or not. So time-wise, it doesn’t have to be a big investment. Short of that the other thing that you can do… I think the sort of next tier would be the ICAgile, ICP-ACC… Full disclosure, I teach that. That’s just 21 hours. And then there’s a lot of instructors that teach that. And then the next sort of final step would be ICF, which… That’s 60, 125 or 200 hours of training. Your choice, depending on what level you wanna go for.

0:29:10.1 Matthew D Edwards: But if there’s someone in an organization who says, I wish that we could just have a professional coach for a while to just help us figure out how to become more. Do you have any advice for them on how they would position that in their organization?

0:29:29.0 Damon Poole: You can only help people see so much value. So wherever they are… This is very partial. I don’t know how to advise other people on this. Just my personal approach is, I literally ask people, what do you see is the opportunities and what do you see is the problems? That’s all I ask when I start. And the stuff that spills out from that is awesome… Then you just kind of feed it back to people. So here’s what I’ve heard. These things… You know, that’s not really something that I can help with. These things, I can, if that’s what you’re interested in. And then there’s either a match there and they go for it, or they don’t.

0:30:08.4 Matthew D Edwards: Well, we value people. People are some of the most interesting, amazing things that I get to do in this life and in my job, just people. And they can be horribly energizing or draining, or encouraging, or discouraging… It could all happen in 60 seconds. And then there’s still a whole day left to live. So people, I think are way more interesting. And so the idea of how to add value to other people on the journeys that they’re on, it seems to me that professional coaching and the work that you’re doing and the book that you’ve written can enable more people to figure out how to actually add value. The lowest common denominator is always people. And Damon, it sounds like your intent and your motivation for this book is to enable people. And the journey that you’re on is, how do I become more so that I can enable someone else to become more.

0:31:16.8 Damon Poole: Yeah. And we’ve really poured our heart out into the book… We didn’t hold back. There’s… If you like powerful questions, there’s over 100 in there. We… At one point we realized there was something missing and we couldn’t figure out what it was. We like doing games and activities. So we made a… Every chapter has activities that you can do either one-on-one or… One is, a powerful question of the day… To practice working towards powerful questions. You get one in mind and you try to just shoehorn it in wherever you can that it makes sense. And so all kinds of activities. There’s a reference in the back that summarizes all of the different coaching techniques, and you can read the first three chapters and then go to any chapter you want after that.

0:32:07.1 Damon Poole: So we try to make it as full of information and as easy to use as an ongoing reference, and to explain it as best we can. Because a lot of people, I think are expecting an Agile coaching book, and it’s really not an Agile coaching book. It is a book about coaching for any Agilist. Anybody that’s got that Agile torch just for whatever reason decided that they want to be the crazy person saying, “Agile is great.” And I think a lot of people in the organization wish, could we just do our work for a while… Why do we have to focus on this Agile thing? So anybody that’s looking to bring coaching forward, to add coaching as a skill.

0:32:54.7 Matthew D Edwards: Damon, thank you. It’s been a privilege to have you on our podcast today. Thank you for taking the time to teach us about you and your journey and your book. Thank you.

0:33:04.8 Damon Poole: My pleasure. Thank you for having me. It’s really been an honor on my side and you’ve given me a lot to think about. Every question, has the possibility of bringing forth insight. And I feel like you’ve done a lot of that for me. I’ve said some things, I didn’t say before that I’m going… “Ooh, I gotta remember that.” So thank you for the opportunity and don’t be a stranger.

0:33:31.5 The Long Way Around the Barn is brought to you by Trility Consulting, where Matthew serves as the CEO and president. If you need to find a more simple, reliable path to achieve your desired outcomes, visit

0:33:47.0 Matthew D Edwards: To my listeners, thank you for staying with us. I hope you were able to take what you heard today and apply it in your context so that you’re able to realize the predictable repeatable outcomes you desire for you, your teams, company, and clients. Thank you.


Podcast: Future Proof End-to-End Encryption and Data Security

Show Highlights

In this episode, I talk with Paul Clayson of Agile PQ, who as a young farmboy couldn’t wait to leave the Idaho cattle ranch to find easier work. Now, after 20 years in the startup world, he’s very fondly missing those days. Early in his career, he learned you only get one shot, so you better develop a winning strategy and stick to it. This knowledge came from serving as Chief of Staff for two congressmen and working for two Presidents in Washington, D.C.

The shot he’s taking now is with AgilePQ. His startup has the solution for today’s computing power and tomorrow’s quantum one with lightweight end-to-end encryption. The majority of industries – from energy, transportation, manufacturing, and the ones building consumer devices – must leverage the power of connected things and that means protecting their number one asset – data. 

We were also lucky enough to hear his most valuable lesson from his father, who served as a medic on Omaha Beach on D-Day.

Key Takeaways

  • There is an even greater explosion in IoT devices to come in the next five years.
  • Everyone is in a race to get to market first in an industry that is not well regulated.
  • Current encryption methods will be powerless when quantum computing is fully adopted.
  • AgilePQ’s solution provides the only security and encryption that fits on all IoT devices, no matter how small.

Read the Transcript

00:57 Matthew: In this episode of Long Way Around the Barn, I visit with a gentleman… He was a young boy on a cattle ranch in Idaho, could not wait to leave the ranch so that he could find easier work somewhere else. Now, after 20 years in multiple industries, including the startup world, he finally misses those days of simplicity and peace back on the ranch. Paul Clayson has done a lot. Early in his career, he learned sometimes you only get one shot or one opportunity to go after what’s important to you. So you need to develop a winning strategy, on purpose, and stick to it. This knowledge came from his days of serving as Chief of Staff for multiple congressmen and two American Presidents in Washington D.C.

01:42 Matthew: The purposeful shot he’s taking now is with AgilePQ. Many consumers may not be considering all the ways their IoT device ecosystems can be and are being exploited in their homes, offices, factories and cities. Paul’s company has developed and implemented a new method of end-to-end security for these device ecosystems. It is designed to exist in a world of quantum computers. If your business and industry needs to leverage or is currently leveraging IoT technology, this may be a podcast for you to hear regarding IoT security in a post-quantum computing world.

02:19 Matthew: And we learned another interesting fact about Paul while we were talking. Not only has Paul taken his civic responsibility very seriously in this country, but so too have many who came before him in his family. We were lucky enough to hear his most valuable lesson, one he learned from his father, who served as a medic on Omaha Beach on D-Day. Let me introduce you to Paul Clayson. Well, Paul, good afternoon. Thank you for taking the time to join us today on Long Way Around the Barn, and thank you for taking the time to teach us and just be with us. We appreciate your time.

02:56 Paul Clayson: It’s our pleasure to be with you. Thanks for the invitation.

03:00 Matthew: So tell us a little bit about your journey as a leader. Tell us about where you’ve been, where you are, and where you’d like to be going, or where you intend to be going right now.

03:07 Paul Clayson: Well, listen, the name of your podcast, Long Way Around the Barn is actually where my journey started. I’m an old farm boy, cattle rancher from Idaho, and I grew up doing that. And when I was out doing stuff on the farm, I could not wait to get off that farm where you had to birth calves in the middle of the night, you had to feed cows twice a day, and milk cows. You had to turn the crops, all of that. And I couldn’t wait to get out of there, so I wouldn’t have to work so hard.

03:40 Paul Clayson: Then I started doing technology start-ups, and I would like to now return to the farm so I don’t have to work so hard. That’s kind of the journey that we all go on in these start-ups. And I’ve been doing this for over 20 years on technology startups, with extremely early stage companies that are emerging technologies and emerging markets with emerging products. And that’s a challenge, but it’s a heck of a lot of fun. And we’re doing that again now with our current security company.

04:15 Paul Clayson: In my past, I haven’t always been in technology. I worked in politics for a while. I think for all of your listeners, my credibility just went out the window. But I worked in politics. I was Chief of Staff to two Congressmen, worked for two presidents in the past, ran some campaigns. And really, that’s where I cut my teeth on strategy, how do you develop a strategy. Because in politics, Matthew, you’ve got one shot. On one day, you’re either in or out of business. Well, I guess that’s disputed this year.

04:51 Paul Clayson: But usually on one day, you’re either in or out of business on that day. You can’t go back and throw more money at it. You can’t change your message, you can’t develop a new classic marketing campaign, you can’t go back and sell more. You’re out of business or you’re in business on that one day. And it forced me, early on in my career, to figure out how to develop a strategy that wins, and stick to that strategy, and then make adjustments and pivots as were necessary all the way along, to make sure that you get to that winning combination. So that’s kind of where my early experience was rooted.

05:30 Matthew: That’s good. So first and foremost, thank you for your service. We all have a civic responsibility to be part of, and contribute to, and help grow this country. And thank you for the work that you’ve done to help build that and grow that along the way as well. But thank you for not only seeing a need, but choosing to become part of the solution that enabled directions, and choices, and people, and so forth.

06:00 Paul Clayson: It was a lot of fun during those times in those years. I don’t know if my wife liked it. She just doesn’t always like the clashes and the conflict that comes in politics. And that’s part of why I chose not to go on and make that a life’s pursuit. But it was a lot of fun for me, and to be at the seat of decision-making for a while was pretty incredible. I look now, I go back to congressional offices, and I see the staff whose in their mid to late 20s.

06:39 Paul Clayson: And I look at those congressional offices, and I think they’re passing bills, they’re writing bills, they’re doing things that are changing the world. And what are we thinking putting our lives in their hands? And then I think, “Well, wait a minute. You were that age. You were in your early 20s, and it was really cool to you back then, and it was okay then. Why is it not okay now?” And you know what? It is. It is young people with tremendous innovation and incredible intelligence. It’s wonderful to see those kinds of people involved in our process.

07:16 Matthew: That’s cool. So it sounds like multiple parts of your career, a lot of your career has focused on fostering innovation, fostering thoughts, harnessing energy, choosing where you want to go, and getting there. And that includes the start-up work you’ve done, the work that you’ve done in the politics, doing civic response, taking your responsibility to the countries pretty seriously. And the things you’re doing now with your current company, so teach us a little bit about your current company. Who are you guys, what are you doing, what problem you’re trying to solve, where you’re heading, just teach us.

07:48 Paul Clayson: Sure, absolutely. I think being involved in technology started with me early. I don’t think it would ever be on any trivia question. But when I went back to Washington as a Chief of Staff to a congressman, we ended up being the first congressional office in history to outfit our entire congressional office with, at the time, Apple Macintosh computers, and then link those back into a network system for Congress. And that really started it, and I’d loved the technology ever since. Now, what we have is the computers. And the computing age has dramatically changed, dramatically since those early days, and it changes dramatically every year.

08:33 Paul Clayson: So what we now have is computing formats and platforms that are no longer on a large scale. They’re on a very microscopic scale. We’re taking things, different kinds of things, and connecting them to the Internet. And we call those Internet of Things or IoT devices. These are devices with extremely small processing capability and very limited functionality. So think like a nest thermostat where it’s a very small, what’s called a Class 0 device, with a very small processor, not much memory. And it performs a function where you can set your temperature in your house through the use of your smartphone, and sending a message back to that device through a server somewhere.

09:29 Paul Clayson: Well, those devices are now prolific everywhere we look. There’s over 20 billion of them, and projections are that there will be 35 billion of them by the end of 2021, and 75 billion by the end of 2025. It’s an explosion of these tiny devices. Those devices right now have not had security. Well over 98% of all those devices going into practice today and being used today do not have security on them.

10:05 Paul Clayson: So we went out as a company and said, “This is a massive hole. We have to create security that’ll operate on those small devices. And it must be secured. It can not only last today, but it’s gotta last a long time into the future ’cause these devices are gonna be around for decades. So it must survive in a quantum computing world as well, when it’s projected that quantum computers will break the encryption and the security systems that are on our smartphones and our laptops. So that’s what we did. We created a product, we went to market with it. And we can secure the small list of IoT devices and can even secure them in a post-quantum world, and we have now taken that to market.

10:48 Matthew: Alright. So the problem statement that you guys are working to address is securing our internet, Internet of Things, or connected things ecosystems, and recognizing then where you see this heading is an explosion of more and more devices and more and more roles across more industries and implementation types. And the common thread across all of them is everyone wants to get to market. But perhaps security is being kicked down the road, or security across these different classes of devices is inconsistent or non-existent, and for sure is not a regulated behavior. So it’s an entire class of attack vectors all by itself. So the approach your company is taking is security first.

11:38 Paul Clayson: That’s very, very well said. And we have to do that because the pandemic itself has created greater explosion of and dependence on these kinds of devices, not only because people are working from home, that’s a small part of it actually, but also because companies have now tried to look out at the market and say, “In the absence of people, how do we monitor processes, and devices, and things, and environments, and so forth?” So they started using devices more prolifically. And that has created a massive number of attack vectors out there. And when they have no security on them or very inadequate security, it opens up a world to bad actors for misuse of these devices. And we’re telling the world, “We do have solutions. There are solutions, but you’ve got to begin with it at the front end of your planning for IoT communication systems and deployment.”

12:44 Matthew: Okay, that makes sense. And you mentioned post-quantum as well. So where you anticipate the market heading is not only the need for security, but to address security in a quantum computing world. And so you’re thinking farther out into the future, than perhaps just get product to market, or just secure that thing, localize your thinking, as computing power changes, so too will the security design and architectures need to change. So you guys are already there.

13:18 Matthew: What are the things that you can teach us about some of the innovations that you believe differentiates you guys in the marketplace? It sounds like this post-quantum idea is one of those differentiators, if not the differentiator.

13:33 Paul Clayson: It is one of those differentiators. So maybe I’ll back into that, with the understanding that on your smartphone and mine, we have various security methods, layers of security that include an authentication and authorization layer. When computers are talking to each other, it includes encryption layers and encrypt data that is going back and forth. It includes all kinds of layers. That’s the best security method, by the way, is to have multiple layers. However, to encrypt a single message on your smartphone requires 3 megabytes and several rounds of encryption to just encrypt the small list of messages. That 3 megabytes of footprint on an operating code will not work on a nest thermostat or will not work on a small IoT device.

14:27 Paul Clayson: The real innovation that we did was we looked at that and said, “We have to change the way we encrypt those kinds of messages.” So rather than taking 3 megabytes or 3000 kilobytes, our system takes 2 kilobytes to execute those algorithms and one round of encryption instead of 14. That allows us to save massive amounts of battery power, another real innovation on our side, since these small IoT devices will be using batteries at a clip of about 90% of them will be battery powered.

15:04 Paul Clayson: It also allows us to speed up the encryption, because we’re not running a large amount of code and multiple rounds of encryption, so we can speed it up. And we cut so much of that operating code out from 3000 kilobytes down to 2 kilobytes. We were able to then increase the size of the keys. So every time an encrypted message is sent to you, it has to have a key at the front end and a key at the back end. And those keys are what allow us to obfuscate the data and then be encrypted on the back end. Well, because we cut so much out of the operating code, we were able to use a key size that instead of the standard on your phone, which is a 32 byte key, we used 288 bytes for a single key.

15:56 Paul Clayson: And what that allows us to do is have this much larger key space. So we not only figured out a way in our innovation to make the code smaller, we figured out a way to make it vastly more secure than what’s on your current smartphone. And that kind of key space will survive in a post-quantum world. So we’re able to accomplish both tasks and allow the smallest of devices to survive even in a post-quantum world. Those are some of the real innovations that our brilliant engineers came up with.

16:32 Matthew: So Paul, those things are all very interesting. And it sounds like you have a lot of work to do, a lot of great future in front of you on the work that you’re doing. Are there any particular markets, or industries, or market segments that you think, “Gosh, these guys are using a lot of IoT devices nowadays, and it looks like their risk is exponentially getting greater and greater. I’d really like to go talk to them” or “I’d like to know what their security strategy is,” or “There’s someone we’d like to work with.” Do you have areas that are more interesting to you than others right now, or is it everyone?

17:08 Paul Clayson: So it’s pretty astounding that even our own federal government have gone to market with IoT devices that are not secured. So just this week, in fact, or maybe it’s Friday of last week, the United States Congress, the Senate and the House passed a piece of legislation that mandated that all IoT devices, especially in the US military, must have a minimum layer of security on them if they’re going to do business with you as federal government. They did that because departments of federal government were going to market on initiatives with data that was, in some cases, even top secret that was being collected, but not having adequate security. So it forced the issue, that now has to take place.

17:57 Paul Clayson: We see that in multiple industries. So the energy industry, they’re using IoT devices on oil and gas refinery, so just an example. What if a bad actor could go out, take over an IoT device, send a false reading to the server saying, “The temperature on this furnace is exactly right.” But while they’re sending that message, they’re raising the temperature, and they can cause an explosion. Those are fickle resources, and the energy industry has those.

18:29 Paul Clayson: Transportation is another one. There are transportation systems for railroads and airlines and so forth that are using IoT devices that do not have adequate security. That’s a must. Consumers, you and I go out to buy a device and we make an immediate assumption that if we’re buying that device, it must be secured, and that isn’t happening in a lot of cases. So consumers, we’re starting to see consumer protection legislation come forward in states, GDPR has it already in Europe, US Congress is looking at consumer protection, state of California already passed one that says a minimum layer of security must be on these devices. So those are all markets, and there’s many, many more that have critical need and that we target, but it’s going to take some time to drive those initiatives to market and assure that 100% of these devices are secured right out of the chutes.

19:36 Matthew: Sure, that makes sense. And it does make sense that folks may presume or assume that if it’s available on the open market, and I can go to the store and buy it, it must therefore meet some minimal standard, or surely it couldn’t be in this box on the shelf for me to buy. But then we have the other interesting challenge for startups, and you know this by living this life, a startup only gets a short life, and this is to your point earlier at the front of this conversation, where your strategy is either correct or it’s not correct, and sometimes you only get one shot. Some startups are very focused, very heavily influenced perhaps by private equity funding, venture capital funding, or they only have five bucks left in the bank, and they believe they only get one bat. So getting something to the market so that they can gain traction often takes precedence over getting something to the market that’s also secured. So I think that there’s a lot of value to what you’re saying, and plenty of data to substantiate what you’re saying.

20:46 Paul Clayson: In fact, there was a recent study done by a group out of Santa Fe, New Mexico, that measures corporate risk, and they did a study and they showed that less than 25% of companies who are deploying IoT devices know where those IoT devices are on their network system, or even how many they have. So you very articulately stated the problem, and that is is that sometimes we go to market faster than we can secure, that is absolutely evidenced in the data.

21:21 Matthew: You know there are interesting forks in this conversation as well all over the place that I’m curious about your perspective on as it relates to the entire point of a connected device is to enable some sort of functionality or access that we didn’t have prior to the connection of the device. So the way that works then is after I plug it in, I now have access to more information that I had before the device. And when we take the numbers that you’ve just mentioned, the growth up through the next number of years to 2025, if we assume all of those devices are turned on, they’re collecting data, they’re sending data, all of that data is being stored somewhere. We have all kinds of amazing new and crazy problems to solve as well, which is this giant volume of data that’s going over the wire, that wasn’t previously going over the wire, and now it’s being aggregated, and it wasn’t previously being aggregated. So it’s not only just securing the IoT devices themselves, but the wires between the originating and terminating point, and then the data aggregation layers.

22:34 Matthew: So when you guys are focusing on IoT security, how far into the larger system conversation do you desire to go or do you plan to go is the line that you’re drawing, is we’re talking about your device itself, or does the device ecosystem include the originating and terminating points and the data and the data aggregation? What’s your purview? What’s your desire? How do you guys plan to be involved?

23:01 Paul Clayson: Well, by virtue of the fact that data is streaming from endpoint to server and back, we are right in the middle of that, we have to touch that. But it’s a very interesting dichotomy in the world today that companies consider data to be their gold standard now. They wanna protect their data, protect their IP, protect the collection of that data at all cost, yet they don’t take adequate measures to secure that data where it’s collected at the endpoint. It’s such an interesting thing. And I can tell you right now that we do know that nation states around the world are hacking into and collecting data that are in databases, corporate government, civic, any place they can get it, they’re downloading that data and storing it, even though it may be encrypted and can’t be broken now, because they know that quantum computers will come along, break the encryption, and then they have access to all of that data when that happens.

24:08 Paul Clayson: So data collection and utilization becomes a critical, critical topic going forward now. In our case, almost 100% of our customers don’t want us to touch their data. They don’t want us to see it, they don’t want us to collect it, they don’t want us to have access to it in any way. So we developed systems whereby key servers and the execution of an encryption system on an endpoint device can all be handled at the company itself. They can handle that. We deliberately developed it that way so the data wasn’t passing through our servers or any process, any IT system connected to us. Now, there are a few companies that say, “I don’t care, it can pass through your system, I just wanna sign it up as a SaaS model, runs through your servers and you can do all the key exchange there.” We can do that, but it’s not our preference. We want people just to secure their own data at their sites. So we advise a lot, a portion of our revenue model allows us to do consulting for companies on these IoT security systems and help them set up a system, and then utilize our technology going forward. So we’re right in the middle of that. We have to… We can’t avoid it, nor do we want to. We wanna be able to be a resource to our customers for this.

25:42 Matthew: That makes a lot of sense. And so, as well as possible, when it comes to data collection and utilization, I think what I hear you saying is you don’t actually want to collect data, you don’t want to utilize the data, you would like the client to take on the responsibility of the traffic and the round-housing and storage collection, all of the things. You can, if necessary, but that is not your desire. Your desire is you enable this framework so that the client can live a better life because of your involvement than prior to, but you don’t wanna get in their stuff, is what I think I heard you say. “Please take responsibility for your own stuff, we don’t wanna see all your stuff.”

26:25 Paul Clayson: That’s precisely, right. Plus they should be because their data is their gold standard regardless of who they are, and it’s worth a lot of money moving forward, and they need to protect it.

26:35 Matthew: So of data, there’s an interesting balance in the privacy and security conversation, which is knowing all of the things you need to know and none of the things you shouldn’t know, and finding that balance is really hard and variable with the more parties involved, the more complicated it becomes, it’s easy math. But when you’re talking about privacy and security, are you finding… Is your experience that… Are clients coming to you and saying, “Hey, not only do I wanna leverage your stuff, I want to know for sure that you don’t know anything about us. I wanna know about your privacy compliance.” Are they asking these questions, or are you finding that you need to educate them about, “Here are the privacy things you should think about, here are the compliance things you should think about”? Do you end up being the teacher a lot of these times, or do people show up and say, “I understand all this, just give me the stuff”?

27:32 Paul Clayson: Yeah, it’s both. There is a teaching element to this though, because we are operating at the smallest of IoT device level that we don’t know anybody else in the world can operate at that level with a full encryption product that’s also post-quantum. So we do have to teach a lot, we do have to help people understand what we’re doing, how that integrates into what they’re doing, and in some cases, we’ve helped people actually do the technical integration so that they’re confident that it’s done right. Because the knowledge workers, the expertise that’s out there to do it directly for them, for them to hire, has not been there, it’s an emerging industry. There haven’t been those knowledge workers on the IoT side who know how to do that. So that inhibits our growth a bit because it creates manual element to getting things done. But the longer we go along, the more people start to understand, and each deployment that we have helps us to understand better how to provide documentation and information to allow people to do it themselves more quickly.

28:41 Matthew: So Paul, an interesting question to me then is, and maybe to the people that are listening as well, as so many people work to understand the words “digital transformation” and “cloud adoption” and “cloud strategy”, all of these words, all of these words are difficult to use, what do they mean, and everybody believes something differently. The reality is, many companies either own all their own stuff, or they’re moving all of their stuff out into a cloud, whether it’s a private solution or a public solution, there’s a lot of cloud work going on, and historically, a lot of like the consumer-based IoT stuff, everything just magically happens. You just plug it in, things connect, it works, I’m super happy at my home. They don’t know if they’re in a public cloud or private cloud, and do they even need to care, that’s up to them in their context.

29:33 Matthew: When you’re working with clients, do you encourage clients to head one direction over another, or is it something that’s not as relevant to you? Is it context-driven? Like “With client 12 in industry 13, we highly recommend we work entirely in a private cloud, we’d like to do some on-prem stuff, but that’s what we recommend here versus over here with this client, it doesn’t really matter, a public cloud would actually be your lowest cost of acquisition, quickest time to market, and we can help you get the job done.” How do you interact in those different business models and do you guys have a preference or recommendation heading forward on those?

30:15 Paul Clayson: So there are some inherent advantages to using a public cloud because they become so big and they do so much of it that they also can develop and use best practices in the industry quicker than private clouds often can on their own. We don’t take the position as a company, whether we recommend one or the other. In some cases, there’s reasons why people don’t want this in a public cloud, they want their data completely owned by and controlled by them without any outside intervention. But without a doubt, the majority of our customers use public cloud, and they use that because they, again, are assuming that a public cloud operator is going to have the best security practices possible that are out there, and we have interfaced with all of the major public cloud players, and so they can accept encoded, encrypted messages from us, decrypt them on the backend, we can do that all seamlessly with public cloud. We can also deal with private cloud.

31:24 Paul Clayson: I think it’s just individual perspective and individual need, but the public clouds do a nice job with having tremendous security around a particular customer’s data. They know how to do that, they use best practices, they have very large security staff already. Sometimes it could take a private cloud, somebody developing their own internal system, a lot of years to figure out the difficulties that public clouds have already figured out.

32:04 Matthew: Yeah, that makes a lot of sense. Okay. There are different organizations using various levels of security and current public cloud solutions, so I get what you’re saying. Even the government is using public cloud solutions or instances, their own nuanced versions of it, but I get it. So you’re saying context-driven, but still customer choice. So you meet the customer where the customer is.

32:27 Paul Clayson: Yeah, I do wish, which we haven’t been able to get to yet as a new company, I do wish that every public cloud operator or every public cloud company out there would tell all of their people, “If you’re gonna be sending data to us from an endpoint, we can’t guarantee that it’s secured unless you’re operating with a full security system on that device, starting at the very endpoint.” If they do that, our market would explode. So far they haven’t been willing to go there, although they’re getting there, they’re starting to see the tremendous number of access points and the tremendous problems that it occurs, so hopefully we’ll get there.

33:09 Matthew: Yes, that makes sense. Maybe some policy legislation conversations continue to motivate things in that direction as well.

33:18 Paul Clayson: Yes, that’d be great.

33:18 Matthew: Alright, so teach us about this then as it relates to your journey as a leader and as a teammate, and then all the different chapters of growth and opportunity that you’ve had through your life. Are there things that you regularly do in your life or in your career that have helped you master your craft of being a leader, of being an innovator? Are there some things that have been greater influences in your journey than other things? What do you do on a regular basis that contributes to your journey?

33:50 Paul Clayson: Well, there’s two parts of answers to that question; one is organizationally, structurally, and the other is personally. So let me start with the personal. A long time ago, I learned and honed a process that I try to undertake, and not always successful, but I try to remember it. I call it the lair principle, L-A-I-R. Wild animals develop a system around them and create a lair where they live that includes their family members or people around them, it includes processes that they develop to go out and be successful at hunting and surviving. Well, to me, that lair principle is critical. It’s an acronym for listen, ask, investigate, which means reading or watching whatever it might be, and then repeat what you’ve just learned, teach other people, repeat, whether that be to write or review or record, but in some way capture and repeat what you’ve just learned. That principle of L-A-I-R creates a lair, if you will, of competitive advantage around you because you’re undertaking the right principles, you’re always looking for best practices.

35:11 Paul Clayson: We should never worry if it was invented here. “Not invented here” syndromes kill companies. So that listening, asking, investigating and retaining has been a core personal principle that I try to utilize in communications and in development. Outside of that, organizationally, there are multiple things that I’ve learned over time and that I try to adhere to in startups. There are three critical principles; making sure that you’ve got the vision, you understand the vision, and you keep the vision in front of everyone in the organization to a transparency that everybody knows everything in a startup, because you have to, you have to know if you’ve got plenty of money, or using your earlier comment, you’ve only got five bucks in the bank. You have to be able to be very transparent. And it’s not always pleasant, but it is always essential.

36:16 Paul Clayson: So you share with people, and oftentimes, the best ideas in a startup come from somebody who’s not even in the department, considering the critical function that they see, they think, and they hear, and they respond, and we listen, and we ask questions around it, and then we hopefully will investigate that to make sure it’s the right thing, and then we utilize it, we repeat it back. And then the final thing organizationally, just at a high level, is to continually check ourselves to determine if we are avoiding things… Doing things that don’t matter, there just is no value in doing well, those things which we shouldn’t be doing at all. Just zero value. We can become very good at it and it doesn’t benefit us. So we constantly have to be asking ourselves, “Is this gonna benefit us going forward or are we just getting motion but no progress because it has no value to us?”

37:19 Paul Clayson: Those are three really critical things in startup organizations that I’ve learned have a major, major impact, and then overarching all of it is just the simple statement to always do the right thing. Always do the right thing in our organizations. Don’t be ever, ever tempted to not do the right thing, ’cause that only leads to all kinds of strife and disruption.

37:48 Matthew: That was a great payload of things to teach us. So lair, learn, ask, investigate, and repeat. Did I get that right?

37:58 Paul Clayson: Yes.

38:00 Matthew: And then vision, and transparency, and do the right thing.

38:03 Paul Clayson: And avoid things that don’t matter.

38:06 Matthew: And avoid things that don’t matter.

38:08 Paul Clayson: Yeah.

38:09 Matthew: Those are hard to figure out sometimes.

38:11 Paul Clayson: They are very hard. [chuckle]

38:13 Matthew: Well, is there anything… When we talk about the journey, the long way around the barn, the whole point of that analogy is that sometimes we take a longer meandering way to get from A to B than we actually needed to. And as it relates to solving problems, sometimes the journey is actually an important piece of the education, and as it relates to life, good grief, we all have amazing journeys and all kinds of crazy directions and ups and downs and that type of thing. But is there anything that you think that I should have asked you that you’re surprised I didn’t, or is there anything that you think, “You know what, as we leave, these are my parting thoughts, these are the last things I’d like to share with you before I take off”?

39:00 Paul Clayson: Maybe only one, and that is, is there something you learned through failure that really, really set a course for you? And Yes, there was something that I learned through failure early on, and it probably was more rooted in being too full of myself to step back and to help recognize that it doesn’t matter if I already had a thought or an idea, if it can be expressed from within the organization, it’s better that it comes from there. And we all stand on the shoulders of giants who went before us, and we have to recognize that and go for it. I keep on my credenza here, a wonderful memorabilia.

40:00 Paul Clayson: Your listeners can’t see it, but this is a medical kit that my father carried on to Omaha Beach on D-Day during World War II, and he was a medic, and he would crawl out on the beach and pull people behind some sort of embankment or shelter, administer aid to them, or as one of his shipmates told me, at times he would hold their hand and comfort them till they died. I think about sometimes that kind of sacrifice that we all have in our lives, and life is not really about me, it’s about the journey that I learned from other people, from you, from the people in our company, and the values that they bring and what I can learn from them. And that’s probably something I learned by being too vocal and less accepting of other people’s ideas in the beginning, and hopefully we’ve rectified that over the years.

41:00 Matthew: That’s a powerful story. Wow. Thank you for sharing that. So as we close then today, one of the things that I have most enjoyed about the story is your journey, figuring out how to add value as a leader. Now, you didn’t use those words, but basically what I’ve heard you talk about is figuring out what matters, figuring out how to include and lead and guide, and then making sure that you’re actually part of the solution as opposed to being part of the problem, which includes, I believe, not your words again, know when to talk, know when to be quiet, know when to lead, know when to get out of the way.

41:46 Paul Clayson: Very well said. You summarized that very, very well. Your listeners could have benefited by having you say that and they wouldn’t have to listen to me, Matthew.

41:57 Matthew: Well Paul, thank you very much, this has been an outstanding time to learn from you. I hope you have a great day.

42:02 Paul Clayson: We will do, and thank you for the opportunity to be with you.


Podcast: Success in Tech Entrepreneurship is One Tap Away

Show Highlights

Jesse O'Neill-Oine

Jesse O’Neill-Oine is a multi-time tech entrepreneur and one of the original co-founders of SmartThings, a company purchased and now run by Samsung. In this episode, he discusses what he’s learned about himself on his journey in solving problems through technology and his most recent endeavor, One Tap Away – a next-generation platform aimed at bringing “contactless” access to amenities at multifamily properties.

Key Takeaways

  • While O’Neill-Oine and his partners have always had overlapping and complementary skills, what matters most is working with great people – from his first business, Refactr, to SmartThings and now One Tap Away.
  • Lean on other solutions when possible: In the build vs. buy debate, the answer is most often buy. A sweet spot is always a “glue company” where you are integrating existing things and pulling them together into a seamless package.
  • When it comes to information security, treat your users as first-class citizens and choose good partners who have a security-first mentality, saving yourself from having to go back and solve security issues later.
  • Not being afraid to ask questions is what helped O’Neill-Oine be a better technologist and solution provider, and while he has always loved technology, his focus has always been solving problems for people.
  • There’s no shortcut for becoming more and mastering your craft. 

Read the Transcript

00:58 Matthew D Edwards: My guest today is Jesse O’Neill-Oine. Jesse is one of the original founders of SmartThings, a company later purchased and now run by Samsung, and is now co-founder of a new company named One Tap Away, a next generation platform aimed at changing the operations, products and services offered in multifamily properties. You should check them out, So Jesse, welcome.

01:26 Jesse O’Neill-Oine: Thank you.

01:26 Matthew D Edwards: So tell us a little bit about you, your journey and technology, connected things, starting and running companies. You’ve done a lot, you have a lot to talk to us about, so your journey in tech.

01:41 Jesse O’Neill-Oine: I was actually going to go way back, because usually at various companies that I’ve started, I begin my journey at college, which was actually Aerospace Engineering, interestingly enough, so I’m a rocket scientist, but I don’t use that skill much. I actually was in college in the late ’90s, and the internet was the big hot-ness, and so while I was in this degree program, I fell in love with computers and programming and figuring out how to get these things online. I don’t even want to tell you how much I spent on my first PC or the loan that I took out to do it, but that’s really where I start, I was as an engineer with an engineering mindset, but got really into computers and tech and really wanted to pursue that, and anyone who’s been around long enough knows that the late ’90s was a great time to do that, you could be an English major and program Perl, which is the language I did start with. I got a job while I was still in college, actually at a company called Imaginet, did various web programming, HTML, early CSS, Perl programming, etc.

02:51 Jesse O’Neill-Oine: And I just dove in and I was pretty much self-taught. I took a couple of classes, one with C and the other was Scheme, I got to be honest, those have not done much for me in my career. [chuckle] So I didn’t have that traditional training, but dove into the web hardcore and really through this job at Imaginet, honed my programming chops, learned more languages and even there at that company was sort of the reason I go that far back was my buds then, or a lot of my buds now, and that was the first group of people that I talked seriously about like, “We should start a company.” Scott Vlaminck and Ben Edwards in particular, who then… Quite a few years later, in 2006, that’s when we started our first company, it was called Refactr, and it was a consulting company focused on startups. Our mantra was “Three developers in three months and we’ll build your MVP.” So that was the first company that I had started and we didn’t know what we were doing, we knew a lot of technology and how to program, we hadn’t worked with clients much, so we just sort of winged it and learned it as we went through those Refactr days and it went well.

04:10 Jesse O’Neill-Oine: We made a lot of great relationships in the community, we put a big focus on the local community and the user groups, and we even started… One of the guys, Ben Edwards, started mini bar here in Minnesota, which continues to this day to be one of the largest bar camp-style conferences. We put a lot of focus on that, and that’s actually what eventually netted out to us doing the bigger startup that anyone who Googles me will see is SmartThings. Somewhere around 2010 or so, at one of these conferences, we met Alex Hawkinson, who ended up being one of the co-founders of SmartThings with us. In that case, it was interesting, we started with him as a client for a company that he was working with and worked as a consulting company with him for several years doing this client work, during that time frame, we all became great pals, we respected each other, we realized probably more important than anything that our skills overlapped in a really good way, we had some really good tech people, some great design people, some great business people.

05:20 Jesse O’Neill-Oine: In 2012, we started SmartThings, there were seven of us that were co-founders, but again, overlapping skill sets, it worked out well. One of my pieces of advice that I give to people starting companies is don’t have seven co-founders, even though for us, it was fine. That brought us up to SmartThings, and I did that for several years, lots we can talk about connected things and the journey there, and then eventually I got the itch to get back into small, and that’s what has driven me towards starting a company yet again in One Tap Away, where we’re now focused on building a platform for multifamily amenities.

06:00 Matthew D Edwards: Right on. Well, that’s what I wanted to talk to you about next and was teach us about One Tap Away, what problem are you actually seeking to solve, or maybe it’s multiple problems, because you’ve already done multiple startups… You probably have a pretty good idea of what recipes make sense? When do they make sense? When do you put something in the trash? When do you kick it down in the road later? So right now, on One Tap Away, given your past success, your past relationships and all of the things you’ve learned, what is it you guys are focusing on? Where are you heading? What’s your target market? And what’s the value prob but overall, it’d be interesting to hear, not only this is what we’re going after, but these are some of the things that we’ve already tested and put in the trash.

06:46 Jesse O’Neill-Oine: I think on the tested and put in the trash thing, it’s like… I don’t know that there’s any perfect formula. For me, a lot of what guides us is we’ve worked with a pretty familiar group of guys, the co-founders of One Tap Away are also all SmartThings folks, and so a lot of it has been the shared context and experiences together that has helped us to then, as a team, try to go through. We knew we wanted to start a company together before we knew what the company would be. We thought about machine learning and everything going on there, and how hot that is. As you and I have talked about Matthew, we also looked at the aging population and how to potentially use technology, especially connected devices technology to help them stay happy and healthy in their homes for longer. Ultimately, they’ll wear One Tap Away as today, and we can talk more about the journey that got us there. Today, what we do is we’re an amenity platform for multifamily buildings. What that sort of means is that we’re finding… Especially outside of the real top-level Class A buildings is that not a lot of tech has been brought to bear, and there’s a lot of problems that exist today. Access control continues to be a challenge, some places have cards, some places don’t have anything.

08:08 Jesse O’Neill-Oine: What does that mean for getting your DoorDash guy into the building to give you food? There’s lots of un-answered questions still around access control, and so we’ve got some unique things we’ve done there to try to make it really easy for residents of these buildings to get access, but also in our mind, a lot of it is also about getting other people access when appropriate at times, it could be a delivery, it could be packages, etcetera. We do access control, and we couple that with the Smart locker system. We use the Smart locker system primarily to deal with the package problem, especially during COVID, everyone’s ordering packages. If you go into these buildings, some of the packages are out front, sometimes the delivery folks did get inside and they’re piled inside, it’s sort of a Wild West. In some buildings, they have a closet, everything gets thrown in there. But then we’ve looked at it and looked at other companies like, Luxer One, who provide these sort of smart package lockers and tried to take that idea, drive the cost down, make it really easy to get it into buildings. Right now, those locker systems are this huge upfront cost if you want them, we’re trying to drive the cost of those down and then provide this multi-use smart locker system.

09:27 Jesse O’Neill-Oine: Initially, like I’ve talked about, we use it for packages and trying to deal with that, that’s great because it sort of cleans up the place, it means that there’s more security for people’s new iPhone or whatever. But it also is a great lever for us as a company, because we’re not holding these packages hostages, but we’ve taken the package and put it in a locker, so we have a really great path to getting people to sign up and engage with our services. That’s one of the things we really looked at hard when starting the company regardless of what it was going be is, what makes us must-have, so we have that really good lever and channel to talk to the user. Of course, in all of this, we give people who don’t have smartphones and don’t want to engage with it a path to get their package, but it gives us a really good way to get people into our app, get people onto our platform, and then we can expose them to other amenities that we can provide. Again, it could be the keyless access control, with COVID there’s a lot around amenity reopening. A lot of places have gyms, they don’t have any policy for how they’re going to re-open the gym safely.

10:30 Jesse O’Neill-Oine: We can control access and so therefore we can help them come in and decide, “Okay, how many people do you want in there at a time? Is it one at a time? Is it multiple?” And then we can work with them to control that sort of stuff. And then from there, we see this world of amenities being much larger than this, and I can talk about a million different examples. We’ve done experiments with food delivery and group food ordering that could bring some interesting economies of scale to that as well as a centralization of the delivery again. We’ve done wash, dry, fold and dry cleaning tests with our lockers. Another amenity that could be really interesting for folks is the ability to just… When they’re down, they’re picking up their package, drop off the dirty laundry, and then later that night, pick it up clean… that sort of stuff. I’ll pause there, I’ve gone all over the place, but that gives you a sense of what we’re looking at.

11:25 Matthew D Edwards: Yeah, that’s a good call out. So some of the things you explored right off the bat… a big key in this conversation is, you have a group of folks that you’ve had a journey with and that you just plain trust. And an interesting thing that you said at the front of that conversation, that moment was, “There were a group of us, we knew we wanted to work together, then we needed to figure out what we wanted to work on.” That’s actually fabulous and amazing. So that suggests that you’re putting your relationships first, that is pretty cool.

11:58 Jesse O’Neill-Oine: My whole career… When I talked about being at Imaginet and that first group of guys, Ben, Scott and me, we knew we wanted to start something, but didn’t really know what we wanted to start either. For me… Scott actually has a great way of saying it, and I’ll blow his quote, but it’s, “I want to work with cool technology, I want to work on a problem that matters,” that has various degrees, it can be helping clean up packages. Said another way, “It doesn’t have to be world-changing for me, but it matters a lot that I’m making a difference for end users or the people who use my product,” and then number one for me is who I’m working with. We all go to jobs every day and every job, no matter how well run of a company it is, there are hard days, good days. So for me, it matters a whole lot who I’m going through that with, more so than the pure technology or even necessarily what the end product is, even though I still want it to be a cool fun technology where I’m learning and I want it to be a product I can get behind and see how it’s going to help users.

13:04 Matthew D Edwards: Right on. That makes a lot of sense. And then you said as you guys were figuring it out, there were things that you explored like machine learning or senior communities or otherwise, and said, “Hey, that makes sense, just not for us or not right now.” Did you go through a process of elimination. You ended up on… Did you call it multifamily units? Multiple dwelling units? How did you characterize it?

13:26 Jesse O’Neill-Oine: Yes, we were looking at… Now, COVID has thrown a loop, we started this company and then COVID came along, so it’ll be interesting. But we were looking at a number of different facets, technology adoption, it’s happening even in folks who are getting a little older… My mom is 70 and uses her smartphone and does all kinds of stuff. So technology adoption is really ramping up. We had this background in connected devices, so we knew with an adoption on the consumer side, we knew we could really do some powerful stuff, we were looking at that. We are also looking at, people are moving to cities, it’s a broad macro trend, this is where COVID… Who we’ll see if there’s truth to people fleeing New York City or not. I tend to still think there’s a pretty big trend towards people moving together and closer, and so there’s more and more multifamily buildings out there, and in those buildings, they have problems like this as well as opportunities, like there’s a ton of people in this one multifamily building, could we do something cool like Taco Tuesday that brings people together and we bring some interesting group dynamics to it and some interesting scale to thing.

14:36 Jesse O’Neill-Oine: So it was sort of some of those macro trends that we started looking at that then drove us into this area of looking at these multifamily properties, what we could do with IoT there, connected devices in general, as well as opening up this world of amenities available to these buildings, and part of how we’re able to help drive the cost down as we also look at revenue sharing opportunities with the building. So it’s not just for them a pure cost outlay, we can talk to them about profit sharing and revenue sharing for some of these amenities that stack on top of whatever, our basic packages, so we try to go in and form a little bit more of a partnership there with the building.

15:22 Matthew D Edwards: Right on. And now you are more or less heat mapping the types of people, the types of behaviors and activities inside these multifamily units, and then you’re also looking at industry competitive stuff saying, “Hey, what exists? How is it done? How can we do it better, different or otherwise?” and you’re not just looking at technology, that’s actually fun part of this conversation, at least from my perspective, is you are an engineer by training and you are a technologist by career, if you will, but yet you’re spending time thinking about people and needs, and then the economics of not only the people, but the multiple dwelling unit owners, if you will, the family unit owners, and you’re coming up with strategic ways to reduce that cost of acquisition and you haven’t said it, but I suspect you’re also working on ways to make it almost a hands-off cost of ownership conversation across time, just get it there and it works. What types of challenges have you already run into that you had to solve or you’re anticipating like, “We have a backlog, there’s 10 things we know that are going to suck wind and we’re going to have to solve all of them, and this is where we’re at.” What do you see in front of you?

16:43 Jesse O’Neill-Oine: Not technology things, mostly. A lot of where we’re at, it’s about… We’ve been actually taking largely bottoms-up approach in how we approach sales, and we’ve gotten fabulous feedback, but it’s a real challenge to go to the property manager, they have one set of problems that they care about, and they care about their residents quite a bit, and retention and that sort of stuff, but if we start at the bottom with them, we need to tell them how everything we’re doing solves their problem, but also pitch them the story for the people that own the building, we’re finding more and more that we might need a bit of a top-down approach where we can get to the property manager and explain to them some of the good financial side of this and get them interested from that regard, then get introduced and we know from other feedback we’ve already received that the property managers are going to love us because we’ve solved some real problems for them.

17:35 Jesse O’Neill-Oine: But we’re spending a lot of time just thinking about how to change that relationship and that almost go-to market strategy, if you will, because we need to convince the people that are going to sign the deal that it’s worthwhile to them and that there’s benefits to them, and it’s not just about solving a messy hallway or something like that. The property manager might care a ton, and it might really sell them or they might really be interested in one facet of what we do, but we need to be able to get that whole story across. So what’s in front of us right now is looking at that and figuring out different avenues, and we’ve got several. We’re talking to sort of big property manager networks that we have some contacts to, we’re also going through some alternate channels, like we happen to have some interesting relationships with a large laundry company, and they provide laundry machines and lots and lots of these multifamily buildings, and so we’ve been working with them on, can we work with them in sort of a partnership form around maybe a wash, dry, fold service that lets us get into the building through that channel, working with this other company, but then we can bring in the other beneficial amenities, the smart lockers for the packages into these buildings.

18:54 Matthew D Edwards: It sounds like even though you’re characterizing it as bottoms-up, and in my original receipt of that was you were talking about the tech first and then working up into the people arguments.

19:04 Jesse O’Neill-Oine: It’s not what I was talking about. To be clear, we did do that in parallel. This isn’t always the way you want to go, but we built a lot of software without clients, we built a lot of stuff based on what we thought was going to make sense, we were doing that sort of engineering bottoms-up stuff in parallel from my perspective, at a slightly higher level in the company, a sort of bottoms-up approach in terms of how we get into buildings. We’ve got some great technology that really resonates, but it doesn’t mean anything if we can’t get into hundreds and thousands of buildings. And so that’s where I spend a lot of time focusing on where I was when I brought it up, thinking of the bottoms-up approach is more about how we get into these buildings and get our software and our product in front of users. Again, at the same time, we were building a lot of technology in IP as well, to be able to go in and pitch this and show the problems that we could solve, so we were doing a ton at the same time.

20:00 Matthew D Edwards: Oh, sure. Right, yeah. You can’t just show up with a presentation deck.

20:03 Jesse O’Neill-Oine: I mean, you can and a lot of people argue that that’s how you should do it because you don’t know that you’ve got product market fit. So there’s a lot of people on the internet that’ll write reams of text about how you shouldn’t write a line of code. Different people have different opinions. We were where we were, we had a bunch of engineers and we knew how to build software and we started building cool software, and there’s stuff we’ve thrown away that didn’t go anywhere, and then a lot that exists today in terms of property management system integration so that we have a way to get all the users out of a building. We’ve got a lot of software written around controlling lockers and access control. In all cases, we try to lend… One of my mantras or beliefs for a long time has been to lean on other solutions where possible, and by that I mean AWS, using a cloud provider, we try to use what they can provide to a huge degree, we don’t usually have a build versus buy debate because it’s all always buy if you can.

21:07 Jesse O’Neill-Oine: We like to try to avoid some of the undifferentiated heavy lifting and build our business intelligence and use things like AWS cloud providers or other SaaS solutions. For example, our access control all goes through right now, today, another company that does this as their primary gig and we integrate with them. Very similar in a lot of ways to the way we approached smart things as we saw, there’s a lot of pieces of technology out there that on their own are cool, but not super useful. And if you can be a bit of a glue company and bring a lot of these pieces together into a more seamless package and experience, then that’s sort of a sweet spot. And so this company, it’s somewhat of a glue company, is how I sort of say it where in a lot of cases, we’re taking things that exist out there and we’re pulling them together into a beautiful and unique and seamless package, more so than developing all of this on our own, because we’d need to have a massive team. We’re 12 people with five of them being engineers so we have to stand on the shoulders of giants anytime we can.

22:19 Matthew D Edwards: Right on, that’s cool. So part of the premise of what you bring to the table then, for all practical purposes, is a contactless solution, I mean.

22:30 Jesse O’Neill-Oine: Yes. We want it to be a contactless as possible, yes.

22:33 Matthew D Edwards: Right. Especially now, it seems to be a popular time to talk about that. But when you’re talking about mobile phone utilization, you’re maybe talking about scheduling, you haven’t said these things, I’m asserting, I’m hypothesizing, but there’s a dependency on the end-user device. But it’s not the only path, you said there can be a non-technology path as well. I believe you said to interact, but as it relates to the contactless solution approach, what types of interesting security or privacy things have you had to solve or do you anticipate having to solve? And are they different from things you have to do in past lives as well?

23:11 Jesse O’Neill-Oine: In a lot of ways, it’s the same as any other project. And I do think that that is true. A lot of security and privacy, you have to take it as a first-class citizen from the start. You’ve got to design with security and privacy controls in mind. In a lot of cases, we lean again on these providers as well. So when you talk about basic cloud infrastructure security, a lot of that is by making smart choices in who we use for that sort of stuff. And even when it comes down to some of the contactless stuff, and again, a lot of times what we’re trying to do, and at least now as a startup, and this could always change, we could decide that this is our sweet sauce and we need to develop this IP, but we’re using another company who spent years and has gotten millions and millions of dollars of funding to solve some access control problems. So we’re able to interact with them in a secure way, they’re able to design their software with security and privacy in mind, and when you bring those all together and look at all the edges and the interaction points, you can make a secured, safe system that honors privacy controls, ,where we haven’t personally had to sit down and spend every last second of our time figuring out how to secure every last hook of it, as long as we can choose good partners. Right?

24:35 Matthew D Edwards: Right, that makes sense. Some states have individual privacy laws that are different from other privacy laws, and some industries have standards that you have to follow that are different than other standards. So everybody has a flavor, and it’s a different context and purpose, and so you have a really interesting problem to solve which is navigate the line, know when, know what, know how. And so my favorite part of what you’ve said so far is, you have to treat security as a first class citizen from day one. That is outstanding advice. It has to be done on purpose, it’s not an accident, it’s not later, it’s like, “Ah, we can get this done Saturday,” it’s doing on purpose, first.

25:26 Jesse O’Neill-Oine: Yes, and I’m not going to sit here and act like I do everything perfect. I don’t really even believe in perfect, but I think by having those sorts of attitudes, it helps get you a long ways right out of the gate that you’re not having to come back later and solve some of these problems because you thought about it at design time. And I lean a lot on that. I’m sure, because we’re only in a couple geographies, we haven’t had to learn everything that we need to yet, I’m sure there’s many intricacies, as you say, that we’re going to have to learn as we continue to grow and scale but by starting with it as a high level idea in mind, then I think you can go a long ways.

26:07 Matthew D Edwards: Policy, legislation, law, all of these things change, they don’t change at the speed of technology, thankfully, or none of us would keep up. But one of the things you mentioned earlier was that you don’t claim perfection, which is cool, and you don’t even believe in the idea of perfect, that’s cool. When I reflect on my own career, there are many chapters, many seasons of attitudes that I’ve gone through, whereby I believed that I was amazingly intelligent and skilled, and I had answers to all questions that had not yet been thought of to all the way to now, when I talk to people, I’m like, “Don’t give me permissions I don’t need. If you have to give me permissions take ’em away yesterday. I only need to know what I need to know in order to get the job done.” Whereas in the beginning, my first question would have been, “I need root. I need access to all things because I can do all things.” That journey, I believe, a lot of people go through those journeys and some people make them through variable velocities.

27:08 Matthew D Edwards: Some of the things that you’ve said, for example, not believing in perfection, knowing that you don’t have the whole data set yet, and then you need to keep going and knowing that by principal, security should be a first class citizen. However, the actual implementation of that idea is, gosh, you got to figure it out while you’re on the journey too. It’s not just a snap of the fingers Lego-fit. So mastering your craft, whatever you define your craft to be, I imagine it’s become multi-pronged through the years and heat-mapped in different directions, but you had to learn to be a useful technologist, you had to learn to be a useful solution provider, you had to learn to become a useful entrepreneur, and every one of those are their own journey. It sounds like you’ve done multiple of them at the same time. You’ve probably had varying velocities of learning and getting schooled. Do you have some highlights from your career where you’re like, “Hey, man, when I was 23, I thought this, and when I was 27, I thought this, and now I think this.”

28:13 Jesse O’Neill-Oine: What you described sounds very familiar, I do remember much younger days when I thought I knew everything. Now I feel like I kind of know nothing. I feel like as I’ve aged and done so much, I just see the world more nuanced and I always assume there’s some other better expert out there, and I want to be cautious not to overstep or say anything with too much certainty, because there’s nuance in everything. So I don’t have a lot of individual “A-ha” moments, I think it has been a journey. I think from very early on for me, part of what helped me be a better technologist and solution provider and just more all around, is that I’ve never really been afraid to ask questions, and I didn’t really stick to my lane, and I don’t ever do that in a confrontational way. I’m a pretty pragmatic guy, but I’ve never been afraid to ask like, “Well, how does this make sense business-wise?” Or like, “What are the real financial drivers? Or, what are our users actually really want regardless of what we want?” So I think all throughout my career, that attitude and that willingness to ask the question, but in a non-controversial and confrontational way, has helped me a ton. To just listen, and if I have a question, I usually am willing to just vocalize the question, and that’s helped me a ton because I’ve learned so much. But by asking those questions, I’ve also, I think, shown myself to be someone who thinks about it…

29:41 Jesse O’Neill-Oine: Even though I was really obsessed with technology when I was younger, even then, I was thinking a little bit more about like, “What does success mean?” I guess. Do you know what I mean? It’s not just this technology, we’re not building technology for technology’s sake, we’re building it to address some problem or make some money or whatever our goals might be, and so I think I’ve always had a pretty strong openness to asking those questions and digging in, and I think that’s helped me a lot. On sort of a personal level, if you want to frame it as an “A-ha” moment, I think it’s mostly… I don’t know when this happened exactly, but somewhere along the line, I accepted that I am more skilled in a more of a managerial and director-type role, and that I’m pretty good at bringing people together and listening to people explain something and then be able to explain it with slightly different language so that this other guy or a gal in the room can understand it. And so sort of an “A-ha” to me was the point when I finally realized, “I’m not a programmer. I’m something else, whatever you want to call it, but I’m working at a slightly different level, sometimes it’s scrum master, sometimes it’s CTO.”

30:55 Jesse O’Neill-Oine: But the way I can be more of a multiplier isn’t by being a 10X coder, it’s by helping solve business problems, making sure that our technology solutions then match and line up to those business problems, and there wasn’t a breakdown in communications where we build a solution to the wrong thing. So that for me, that was a big more of an “A-ha”. There wasn’t a specific time, but it was just coming around to this understanding that that’s probably more my sweet spot is helping people and listening and directing rather than being the builder, which is what all I wanted to do when I was younger, I wanted to be a rock star coder, and I was obsessed with it and all that. Organizationally, I would say that “A-ha” moments, again, not so much an “A-ha” moment but I’ve just, you’ve sort of brought it up, I’ve always really believed in mutual respect, transparency, collaboration, and so I think that those basics can really make a team successful. People are shocked sometimes when I interview that I don’t ask a lot of technology questions, I really don’t. Scott is the same way, I reference him over and over again, because we’ve known each other since college, so it was Scott Vlaminck, and Ben Edwards was the other one that were my best friends to this day and instrumental in starting that very first company.

32:19 Jesse O’Neill-Oine: I always tell people, I’m not… I kind of came… It’s not quite fair, but I’m not a natural entrepreneur, I’m not a huge risk-taker. If it weren’t for Scott and Ben, I wouldn’t have probably been on the same path that I have been on, because while I had the desire to be involved in the room where it happens, if we want to drop a Hamilton reference, I wanted to be a decision maker, but I wasn’t naturally drawn to entrepreneurship. But Scott and Ben were more and so the three of us, we went and formed Refactr. But we’ve, throughout our careers, worked together and had people be shocked at how we interview, but we’re looking at personality, we’re looking at, Are you a lifelong learner? Are you interested in things? Are you curious in things? Our basic thesis has been a lot of time as well, regardless of what you learned in college, we got to teach you the job once you get here, because every company does it a little different and there’s so much internal tribal knowledge, etc., that we look for the people who have the right personality characteristics, much more than amazing coding abilities or engineering abilities. So that’s led us to looking for people who show lots of mutual respect, curiosity, transparency, things like that.

33:37 Jesse O’Neill-Oine: I think people get hung up on individual skills when really it’s team dynamics that tend to make or break projects or companies even. It’s always a people problem, it’s never a technology problem, you can do anything with technology, it’s just turtles all the way down. You can build almost anything, but it’s always the people side of it, it’s like, “Are the requirements I’m hearing you say really what you mean? And when I then implement that in software or hardware or what have you, is it really solving the problem you thought you wanted to solve? Is the problem you thought you wanted to solve really the problem that your company has?” It’s all of those softer things that tend to make or break things in my experience, and it’s rarely like a big tech fail. When it’s big tech fails, a lot of times, it’s people that are obsessed with the tech and they want to build and own every last drop of it, and they think it’s all about the tech, not about solving whatever problem you’re ultimately trying to solve.

34:37 Matthew D Edwards: You know, the name of the podcast is, Long Way Around The Barn, and our initial premise was actually exploring different ways to solve problems and how sometimes getting to a solution for the problem may take unnecessarily long or be unnecessarily complicated, or it just took longer than it needed to, and so sometimes we’re looking for a shorter path, less complicated. But a lot of the things that you just said, I think, is worth calling out that there is no shortcut to the journey, there is no shortcut to becoming a master of your craft, there is no shortcut into becoming more. It’s just a path you have to walk, ditches you have to be in, speed bumps you have to trip over. The long way around the barn to becoming a master of your craft, there is no shorter way. It is the long way.

35:29 Jesse O’Neill-Oine: Right. The only thing I would add is that you’ve got an integral component of that is the feedback loop, it’s something that comes out of Agile. But I think it applies to so much more. You’re right, I don’t think there are a lot of shortcuts, you have to take the journey, but all along that journey, you’ve got to be getting that feedback loop that’s helping you redirect a little bit, helping you expand your horizons a little bit, what have you. I’m not maybe putting that very eloquently, but I think it’s a key component.

35:58 Matthew D Edwards: No, it’s good. The feedback loop, that’s super critical. So as we part then, you have talked about just so many things in such a very short period of time, but the most important thing that I want to call out is that you guys are in a new chapter of your journey together, and that’s One Tap Away. And that’s what you’re building and testing and evolving and is generally available now in moderated exposure. That sounds like an awesome chapter. Is there anything that you would want to add or that I didn’t ask or you want to amplify about your journey individually or as a team or as companies? The parting thought for us? Right off the bat, I already like the feedback loops.

36:38 Jesse O’Neill-Oine: I’m sure there’s much more that if you keep prodding me I could say, but I think we’ve hit on a lot of it. As you can see, I value the people and the relationships a lot, and I think that’s what generally leads to success. For me in some cases, that has been selecting who I work with, and that’s been this chain of startups. And it’s also even once you’re in a company, it’s the relationships with the vendors that you have, and there’s always partners involved, and so it falls into all of that. So I’m certainly happy with where I am in my journey and I’m excited where One Tap Away is. I would love for people to check it out, but unless you own a multifamily property, it’s probably not going to be something you’re going to necessarily go grab yourself, but maybe you can ask if you live for someone to check us out. And yeah, we just continue the journey and have as much fun as we can while we’re doing it.

37:38 Matthew D Edwards: Dude, this has been a pleasure. Thank you for taking the time to teach us today, this has been outstanding.

37:43 Jesse O’Neill-Oine: Thank you, appreciate it.