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.


People Operations Focuses on Bringing Even More Value to Team Members

Who you work alongside matters. This is a core belief shared by the people of Trility Consulting®. This shared value plays a role in ensuring they work as a team in delivering the highest priority outcomes to clients.  

Who you stand beside matters. I am grateful for each and every one of the people who have joined and committed to this journey together.

Matthew D Edwards / CEO

This journey has grown to include more contracts, more clients, and more team members, so Trility leadership realized this small startup was ready for a formal People Operations team and announced the following promotions and hires.

Jennifer Davis promoted to Vice President of People Operations.

As one of the first employees hired in March of 2017, Davis has reliably served Trility as needs arose and exponentially grew. “We worked with Jennifer before Trility as consultants and quickly realized she would always deliver and always with a smile,” said Edwards. “From the start, she has always embodied the spirit of People Operations – caring and serving others and putting people first always. She continues to make Trility a better place for each of us, so she was the natural person to step up and lead People Operations.”

Kori Danner promoted to Talent Delivery Manager.

“Finding new talent is a lifeline for Trility, and Kori has been at the heart of finding people who exemplify what Trility embraces – honor, professionalism, and keeping promises from Day 1,” shared Edwards. “Our ability to scale and not sacrifice how we deliver work is directly related to Kori’s consistency in creating connections and forming reliable processes that earn and increase trust with people considering joining our team.”

Megan Hanna joins the team as Talent Sourcer.

As the newest addition to People Operations, Megan is focused on helping future talent understand Trility’s culture. “Megan has a very natural way of connecting with others and will be integral in helping cultivate Trility’s newest teammates,” Davis shared.

We’re Growing & Hiring

While People Operations is now a formal team at Trility, its directive is not new. These three team members will elevate and scale how to create value for Trility’s existing and future team members.  As one of the Inc. 5000’s fastest-growing companies, Trility welcomes conversations with people interested in becoming more today than yesterday. View our current openings on LinkedIn or connect with a recruiter.


Trility Consulting Joins Inc. 5000 Fastest-Growing Companies List

When the founders of Trility realized they needed to form a company instead of individually contracting on extremely tough projects, they knew they’d bring value to clients and provide challenging work to those who joined the team. Little did they realize how quickly the teams and expertise they pulled together would make the Inc. 5000 list in their first year of eligibility.  

“Trility is a team of people always and only working towards one goal: Add the most value to our client experiences possible, moment by moment, each and every engagement. The by-products of that singular goal are revealed as satisfied clients, happy, healthy  teams, and cultural, company, and financial health. It is because of our teammates that we collectively, clients and Trility alike, experience value-based success.”

Matthew D Edwards / CEO

The Inc. 5000 is an annual list that ranks the fastest-growing private companies in America. Trility is eligible as a privately-held U.S.-based company with four years of sales with a minimum of $100,000 revenue in the first year and a minimum of at least $2 million revenue in the most recent year.

Trility opened its doors in 2017 with two Fortune 500 clients and has achieved consistent revenue growth by working with companies of all sizes and across 19 industries. Each of them seeking different solutions but sharing one trait – they view technology as the way to thrive amidst market, economic, regulatory, or competitive headwinds. Despite the challenges of a pandemic, Trility and its clients remained resilient. 

Edwards shared, “We are blessed to have great clients. We are even more blessed to have great people in our company who choose to become more each day than they were the day before. And they repeat this every single day of their journey with our clients and our company. I am proud to stand beside the people at Trility.”

Iowa-Based Companies

Trility joins 31 other Iowa-based companies who also made the Inc. 5000 list: VizyPay, MCI, Higley Industries, The Art of Education University, Pet Parents, Moxie Solar, Trader PhD, English Estates, Eagle Point Solar, Eco Lips, PowerTech, Heritage Group, Itasca Retail Information Systems, Heartland Roofing, Siding and Windows, Spinutech, MedOne, MediRevv, Trility Consulting, Dwolla, Highway Signing, Express Logistics, Schaal Heating and Cooling, Kingland Systems Corporation, JT Logistics, Clickstop, McClure, Peoples Company, Aterra Real Estate, GrapeTree Medical Staffing, Involta, and Ivy Lane Corporation.

Among the 31 Iowa-based companies, the average median three-year growth rate was 140 percent and total revenue reached $823.9 million. Together, this list of companies added more than 7,338 jobs over the past three years and remained competitive within their markets given 2020’s unprecedented challenges. 

About Inc. Media

The world’s most trusted business-media brand, Inc. offers entrepreneurs the knowledge, tools, connections, and community to build great companies. Its Inc. 5000 list, produced every year since 1982, analyzes company data to recognize the fastest-growing privately held businesses in the United States. Complete results of the Inc. 5000, including company profiles and an interactive database that can be sorted by industry, region, and other criteria, can be found at


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.

Product Design & Development

Distill What You See Into Action

On Monday morning, you receive a phone call from a Senior Partner in your company. A client of your company is preparing to go live with a new software system in sixty days. The Senior Partner, knowing you have extensive, successful experience delivering technology systems to the marketplace that meet or exceed functional expectations, are secure-by-design, delight customers, generate revenue, and increase brand value, wants confidence that everything will happen as planned and desired. You can name your price, but it doesn’t change the fact that you have three weeks to learn, assess, refine, and present observations and recommendations. You have no idea how deep the rabbit hole may go. After some back and forth in the conversation, you accept.

You’ve done this before. You know what to do. You have a battle-tested framework for assessing large volumes of data in short periods of time to determine planned versus actual deltas and risk+probability remediation plans. This framework that you’ve developed over the years and myriads of assessment/salvage operations helps you not only identify what, why, and when, but also what not. In other words, while you’ve found success making observations and recommendations, the real magic has been identifying what doesn’t matter, what doesn’t need to be addressed, and/or what can safely be ignored for now, and perhaps forever.

Identifying what does not matter is often harder than identifying what does.

The assessment framework you use for these types of engagements is structured to help you discover project health and corporate risk while eliminating noise. After all, folks pay you to figure out the state and health of their investment in short, thorough engagements. And what they expect is a concise list of observations and recommendations that guide immediate decisions leading to crystallized outcomes.

You know there will be one of three possible outcomes:

  • Outcome One: This project is working well. You may recommend some changes here and there to fine tune the performance, but overall, the effort is heading in a good direction and should render the desired outcomes according to the expected parameters.
  • Outcome Two: This project is not working well, but is correctable. You recommend a number of changes that will bring the project back into expected performance. You additionally recommend some key areas (health indicators) of the project to keep an eye on from today through end of project so they minimize the possibility of ending up here again.
  • Outcome Three: This project is not working well and is not recoverable in a manner that makes financial sense. Your recommendation will likely be to close the project down, perform a retrospective, and use the output to influence project structures and decisions in the future.

From experience, you also know that some projects classified as Outcome Two should really have been classified as Outcome Three. However, after reporting your results to the Senior Leadership team, they were not yet willing to accept the possibility of a sunk cost effort and instead chose to believe doubling down on the effort would pull it back into the green zone. (Where “doubling down” = “get more people, work harder, spend more money”.)

The Assessment Framework

For each category below, research and understand what exists, what doesn’t exist, in what state it exists, and what must, could, should, or will not be done accordingly.

01 Problem Statement

What problem does this organization need solved? Would you categorize this as a business, technical, security, compliance, team member efficacy, or client-driven problem? What is the known/perceived blast radius of this problem statement impact — the industry, your target market, your enterprise, or localized within an enterprise?

02 Desired Outcomes

What change must be realized as a result of this effort (expenditure)? What will this/these change(s) look like to the affected parties? Will they care and why?

03 Definition of Done

How does this effort, team, project, or program know when it is done spending money?

04 Constraints/Attributes

Are there parameters, boundaries, and/or attributes to which this engagement must adhere, meet, or otherwise evidence compliance. Examples: Financial (SOC), Health (HIPAA), Security (NIST), Privacy (CCPA), System Availability (5NINES), budget, time, capacity, risk appetite, traceability, auditability, etc.

05 Dependencies

What dependencies exist that may complicate, inhibit, or otherwise preclude this effort from successful completion? For example, will this solution sell itself into an existing market or do we need to create market demand along with introducing a solution? Are we innovating on existing things or green-field inventing? Do we understand the target market? Are teams skilled correctly?

06 Team

What roles were initially requested to make this effort happen successfully? How has this changed through the course of the effort? What exists today? What should exist? Is the team being simple enough? Are they thinking big enough? How frequently, if at all, is the team being given opportunity to assert, test, learn, and change? Is this an adaptive or inflexible project environment?

07 Work

How is work (deliverable) discovered, defined, prioritized, and realized? Is there more than one backlog? Is there more than one priority? Who are the stakeholders? How are they involved? What is the definition of done? What implementation choices have been made and how will they impact long-term solution viability, cost of ownership, staffing availability, training, and competency?

08 Money

Did there exist an idea of how much money would need to be spent in order to realize the desired value? If there is money awareness, are there planned versus actual details? A known run-rate? Remaining spend projection?

09 Commitment

What was the original delivery commitment? What is currently delivered? Is there a delta? If yes, why? If there is a planned versus actual delta, will this effort require remediation activities now, later, or never?

10 Risks

What are the risks which may impact successful implementation, daily operations, and customer delight? What is the associative probability of each occurring? What is the associative impact of each occurring? Particularly, though not exclusively, what are the elimination, mitigation, and/or remediation options for each?

To be a healthy, useful, value-driven and value-realized investment, projects of any size, in any size organization, all require these above elements in some way, shape, and form.

At the end of this effort, you typically have what you need to ascertain investment to return potential and make your recommendations to the Senior Leadership team.


Creating Value in Relationships is No. 1 Priority for New Role

As a longtime fundraiser in the nonprofit sector, Megan Hanna discovered she had a knack for building solid relationships and creating inclusive communities for donor and volunteer networks. This ability to connect with others and build a culture made her an ideal fit for the recruitment team at Trility Consulting®.

“Non-profit work is very relationship-focused and rewarding, but I realized I desired a position where I could work in a team environment – not just build one,” shared Megan, whose role as a Talent Sourcer is to identify individuals who are interested in delivering solutions with a team instead of being viewed as an “outsourced contractor” to clients.

Megan’s style of learning about others aligns with the values we seek in candidates. For us, it’s more than keyboard skills and expertise. We seek specific attributes that help ensure Trility builds solutions that consistently deliver values and achieve the priorities our clients expect in a predictable, repeatable, and auditable manner.

Kori Danner / Talent Delivery Manager

When it comes to success, Megan knows it’s “95 percent about building that relationship.” Her recipe for doing this is simple: Be transparent by being honest and forthcoming. “I want to know where I stand with others because this helps me grow and learn,” she shared. “So I look to provide the same experience for those who are interested in career opportunities at Trility.”

Along with the team environment Trility offers, Megan was excited about how the culture translates to team members who are geographically distributed around the United States. “I love the opportunity to work remotely but still feel part of a team on a daily basis with the communication tools and resources available.”

Megan’s two dogs, Mason and Kaylee, are also excited to have her working from home. 

Connect with Megan Hanna

Interested in joining the Trility team? Email or connect with Megan Hanna on LinkedIn.  

We’re Growing

Trility Consulting made Inc. 5000 list for fastest growing companies due to achieving 131% financial growth in three years and continues to have career opportunities for people interested in becoming more today than yesterday.


New Position to Bolster Successful Delivery Method

If investing time in others brings joy, then Cora Pruitt lives with a constant smile. “I love being of service to others and, to me, Agile coaching is about teaching people how to create solutions and perform in ways that work for them and make them successful,” Pruitt shared. Earlier this year, she chose to move from a contracted Senior Delivery Manager to a full-time team member with Trility Consulting®.

When the new role of Delivery Director was created due to an increase in projects and teams, she was the natural choice to lead and evolve this effort. 

A key factor is having people like Cora leading our teams and ensuring we deliver what is promised and that our teams observe more than they  are asked to do and offer options and recommendations from start to finish.

Michael Schmidt / Vice President of Delivery

“Part of Trility achieving year-over-year growth is due to consistently delivering value and tangible outcomes to our clients,” shared Michael Schmidt, Vice President of Delivery. “A key factor is having people like Cora leading our teams and ensuring we deliver what is promised and that our teams observe more than they  are asked to do and offer options and recommendations from start to finish.”

This new role provides Pruitt the opportunity to be a positive influence for all team members. “Trility leaders don’t hesitate to let us know how we are appreciated and that we are a part of the bigger picture. I’m excited to be a part of where we go next.”

Valuing productive conflict allows Pruitt to work with clients and team members to ensure observations and feedback align and work towards achieving the best, highest-priority outcomes. “I encourage everyone to keep an open mind for feedback. To succeed as a team you need the ability to embrace other opinions and have confidence to provide yours,” she said. “You don’t get anywhere if you try to sugarcoat things – the bigger the decision, the bigger the conflict.”

One of Pruitt’s finest moments serves as a testament for how she works to serve others. She shared having a former colleague shake her hand and say, “Thank you. Because of you, I enjoy coming to work.” This same contracting engagement led to the client nominating her for TAI’s Women of Innovation Award in 2012 where she was named a finalist.

Read how Trility Consulting's made Inc. 5000 list for fastest growing companies due to achieving 131% financial growth in three years. 

About Trility 

Comprised of technologists and business consultants, Trility helps organizations of all sizes achieve business and technology outcomes. Clients appreciate that our teams solve problems contextually and bring their people along to ensure a reduced cost of ownership long after the engagement is done. Areas of focus include:

  • Cloud and DevOps
  • Product Design and Development
  • Information Security
  • Data Strategy and Management
  • Internet of Things (IoT)
  • Operational Modernization

Trility is the only business and technology firm with a proven history of reliable delivery results for companies that want to defend or extend their market share in an era of rapid disruption. Headquartered in Des Moines, Iowa, with teams in Omaha, Neb., Kansas City, Mo., Denver, Colo., our people live everywhere, and we serve clients from all corners of the United States and globally.


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.


Never Leave ‘Em Guessing

The world according to Melissa Creger means you’re never left guessing. As someone who values and practices transparency, she has a history of earning trust, being flexible, keeping an open mind (and ears) so her thinking is always challenged. 

“I don’t ever want to guess how I’m doing, and so I never want my clients to be left guessing either,” shared Creger, whose career has historically been one of connecting and helping people. She got her first taste of “digital transformation” during her work with the Alzheimer’s Association when the organization realized it needed to move from pen and paper to online signups and payments.

“This experience gave me insight into how technology is critical for sustainable growth,” she added. This past experience with technology as a user, coupled with her tenure working in the technology industry since 2012, made her a great fit for Trility Consulting when she joined the team as a Director of Business Development in July.

“Melissa values transparency and seeks to set clear expectations when managing relationships, and the by-product of that approach is trust,” said Brody Deren, Chief Strategy Officer. “We are excited to have her join the team and support our growing client partnerships in Omaha and beyond.”

Trility’s outcome-based delivery method means clients receive observations, recommendations, and options to iterate for the best, highest-priority outcome. Creger will help build upon this proven approach and ensure we continue to deliver over and over again on our promises – meeting time, budget, and scope that aligns with business and technical requirements. 

Connect with Melissa

Interested in learning more about Trility, email or connect with Melissa Creger on LinkedIn.  

About Trility 

Comprised of technologists and business consultants, Trility helps organizations of all sizes achieve business and technology outcomes. Clients appreciate that our teams solve problems contextually and bring their people along to ensure a reduced cost of ownership long after the engagement is done. Areas of focus include:

  • Cloud and DevOps
  • Product Design and Development
  • Information Security
  • Data Strategy and Management
  • Internet of Things (IoT)
  • Operational Modernization

Trility is the only business and technology firm with a proven history of reliable delivery results for companies that want to defend or extend their market share in an era of rapid disruption. Headquartered in Des Moines, Iowa, with teams in Omaha, Neb., Kansas City, Mo., Denver, Colo., our people live everywhere, and we serve clients from all corners of the United States and globally.