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.


Podcast, Part I: Vulnerable Storytelling to Advance Data Science

Show Highlights

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

Key Takeaways

Success in data science means:

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

Three distinctions for roles and responsibilities:

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

Read the Transcript

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

0:20:51.5 Matthew: And iterate.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


Podcast: A True Process for Leading

Show Highlights

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

Key Takeaways

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

Read the Transcript

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

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

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

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

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

0:02:42.2 Matthew: Wow.

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

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

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

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

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

0:03:56.2 Matthew: Sure.

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

0:04:43.5 Matthew: Wow.

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

0:05:03.1 Matthew: Sure.

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

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

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

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

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

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

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

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

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

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

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

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

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

0:12:49.4 Matthew: Sure.

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

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

0:14:29.0 Matthew: Right on.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

0:25:50.6 Todd: Yes.

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

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

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

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

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

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

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

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

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

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

0:31:41.9 Matthew: That’s awesome.

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

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

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

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

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

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

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

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

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

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

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

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


A History of Making Life Easier

Ryan Skarin makes life easier. This was the common thread when asked about his past roles in the technology industry. This innate ability makes him the ideal Solutions Director for Trility Consulting as a large part of his role is identifying challenges and crafting technology solutions. With clients facing constant change, capturing, protecting, and growing market share requires innovative technology that allows for speed and agility.

His journey has always been to help those around him. One of Skarin’s first programming jobs was writing software to speed up the manufacturing process. He saw the need to teach the industrial engineers how to code so the whole team could support the outcome. When his father asked him to join his real estate appraisal company and help him, Skarin answered yes. Eventually, Skarin’s journey took him back to programming where he worked for Staples and he took pride in building, among many other solutions,  a simple, streamlined web-based tool that solved some frequent and frustrating ordering process problems – essentially building an easy button for the office supply store that sold itself as the well-known red “ easy” button. 

Skarin shared one aspect that must always be considered. “When we define a solution, we also have to understand how it’s viable for the business and enjoyable for the users and sustainable for the client’s IT group to own.”

Adding that a common dilemma that he always aims to avoid: An excellent solution is crafted for the users, but might be difficult to fit into the existing IT landscape. Or IT can maintain it, but it causes the users undue friction. “It’s easy to solve the direct problem a client asks you to solve, but it takes a great deal of context to provide solutions that work long-term for everyone,” he said.

According to Chief Strategy Officer Brody Deren, “Ryan’s experience spans multiple industries and from startups to large corporations. This exposed him to a diversity of problems, and allows him to more quickly connect the dots and do it contextually to better serve our client’s desired outcomes.” 

Skarin’s desire to grow and help others aligns with Trility’s core values and outcome-based delivery method. “He’s already bringing observations, recommendations, and options to ensure our teams achieve the best, highest-priority outcome,” Deren added. 

Need something made easy?

Connect with Ryan Skarin on LinkedIn or send him an email.

About Trility 

For those wanting to defend or extend their market share in an era of rapid disruption, Trility simplifies, automates, and secures each iteration and has a proven history of reliable delivery results. Our outcome-based delivery means you always know the status of your project’s compliance, quality, financial spend, and percent complete. Headquartered in Des Moines, Iowa, with teams in Omaha, Neb., and Denver, Colo., our people live everywhere, and we serve clients from all corners of the United States and globally.

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
  • Connected Things / Internet of Things (IoT)
  • Operational Modernization

Podcast: The Makings of a Great Agile Coach

Show Highlights

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

Key Takeaways

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

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

Read the Transcript

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

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

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

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

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

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

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

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

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

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

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

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

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

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

0:08:13.7 Matthew D Edwards: Wow.

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

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

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

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

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

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

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

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

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

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

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

0:11:00.1 Matthew D Edwards: Yeah.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

0:24:10.1 Damon Poole: Oh my.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Product Design & Development

Of Jellybeans and Elephants

Originally published on LinkedIn.

Imagine a world where you have the responsibility of building an elephant.

Will you start at the atomic or cellular level? Will you start at the skeletal system and work into the central nervous and circulatory systems? How about features, functions, capabilities, and constraints? Where will you start?

Elephant made out of jellybeans

How many attempts or iterations do you think it will take to get the job done completely, correctly, and usefully? How about just to get a first, working version?

Now imagine a world where the only time you learn the true value of the work you’ve done is at the end of each iteration. If all goes as planned, each iteration results in having more knowledge, experience, and value than at the beginning.

You make a commitment to deliver something, you actively work to deliver it as committed, and then you deliver. All as planned.

And as expected, after it is demonstrated to the stakeholders, you receive the feedback you need for the next steps. That feedback tells you one of three things:

  1. Let’s keep it.
  2. Let’s change it.
  3. Let’s head a completely different direction.

Given the size of an elephant, how frequently would you like to have feedback telling you where you are in relation to done?

Jellybean Patterns

In the below examples, look at each jellybean as a complete iteration containing Plan, Work, Demonstrate, Receive Feedback.

Each jellybean is viewed as a complete iteration containing Plan, Work, Demonstrate, Receive Feedback.

Consider these patterns: For someone who delivers daily, they can entertain change each new day. For someone who delivers every two weeks, they can entertain change every 14 days. And for someone who chooses monthly deliverables? Every thirty days. Remember, each iteration teaches you so that you can keep what you have, change to something new, or pivot altogether.

30 deliverables in 30 days compared to 1 deliverable in 30 days.

Author’s note: To keep this article simple, I’ve chosen to use simple numbers (1 and 30) in order to amplify the point through stark contrast. For the rest of this article we’ll discuss daily and monthly deliverables.

Since we’re building an elephant, I’ll take the liberty of suggesting it will take greater than one month. So let’s look at what a one-year delivery pattern looks like using 1 day and 1 month as units of measure.

360 deliverables in 360 days compared to 12 deliverable in 360 days.

Comparatively, if we accept that each jellybean is an iteration and that each iteration enables the same plan, work, demo, receive feedback pattern, the above pattern suggests that, through the course of one year, daily iterations enable nearly 97% more opportunities to learn and change than monthly. Likewise, if we assume weekly iterations, there are 80% more opportunities to change your mind than if delivering monthly.

Dial the iteration frequency into what makes sense for your company, teams, and client.

Remember, while you may feel you’re choosing iteration lengths, what you’re actually choosing is the number of times you develop, deliver, demonstrate, and learn across a period of time.

How many opportunities would you like to try something, learn, and change during a single project?

Let’s further explore the superpowers of delivering jellybeans instead of elephants. This time, let’s address unplanned change.

Jellybean Patterns and Mid-Cycle Change

Even with best intentions, small iterations, and high-performing teams, change happens mid-iteration. Not ideal. However, occasionally clients and teams experience new variables such as market shifts, attrition, mergers & acquisitions, products and services portfolio pivots, the team missed something, or the client realized they wanted a purple toaster instead of a red car.

Working to mitigate and manage change is good and normal. Eliminating change is neither realistic, nor healthy. Change will happen. Some change is good.

Plan for it. Invite it.

Imagine being in the middle of an iteration and you’re required to pick up work that was previously unplanned. Do you stop what you’re doing, reset the clock, and pick up the new work? Do you put it in the backlog and get to it when you’re done with the current effort?

Does your delivery pattern enable change while minimizing waste (**Where “waste” = time, effort, and deliverables that will not be utilized ‘as is’ or at all)? Change is driven by economic opportunity and client delight for the company, not project delivery convenience for you.

If you are practicing daily iterations, let’s say you started at 0800 hours and I asked you to pivot at 1200 hours. The potential project waste due to the pivot is 4 hours.

Similarly, if you are practicing two-week iterations, you started on Day 01 and I asked you to pivot on Day 6, the potential project waste due to the pivot is 5 days.

So it may make more sense to consider, if you are practicing 30-day iterations, you started on Day 01 and I asked you to pivot on Day 15, the potential project waste due to the pivot is then 15 days.

**Note: A more accurate waste calculation for these examples will be to consider (total_#people_involved) * (total_#hours_expended) * AVG(labor_costs_per_hour) + (overhead_costs (i.e., operational expenses))

30 deliverables with change and improvement inside 30 days compared to 1 deliverable in 30 days.

Now, using the same math argument mentioned above, consider what change looks like spanning an entire year.

360 deliverables with change and improvement inside 360 days compared to 12 deliverables in 360 days.

If value is realized by what is delivered and waste is calculated by what is not delivered, how important do you believe iteration sizes are to realizing value and managing the economic impact of waste?

3 Questions to Ask Yourself

Imagine this is your company and your personal money.

  1. How long are you willing to wait to find out if your financial investment was worth it?
  2. How long are you willing to wait to find out if your idea is even a good idea?
  3. How much will it cost you to change?

5 Steps to Make Sure You Get Results for Your Money

  1. Use a small team who strive for simplicity and thrive in change
  2. Use small iteration (batch) sizes to provide feedback now, not later
  3. Use a Plan, Work, Demonstrate, Receive Feedback pattern PER iteration
  4. Focus upon constant delivery (flow) over start-stop behaviors
  5. Plan for change. Stay small. Constantly deliver, assess, and change.

What Are You Going To Do Next?

Start with the elephant. Break it down into jellybeans. Deliver a predictable, constant cadence of jellybeans where each jellybean includes plan, work, demonstrate, receive feedback. Invite change.

It may surprise you to discover along the way, the client told you they wanted an elephant when they really wanted a platypus. How would you discover a platypus met the need if you only delivered an elephant sized iteration?

And this is only one scenario.

What if you don’t know for sure what you want? How much money are you willing to spend to discover it?

Imagine we’re going to spend $5000 of your money.

Do you prefer 50 iterations of $100 where you can change your mind every $100 and find out you wanted a platypus, not an elephant?

Or do you prefer 2 iterations of $2500 where you can change your mind twice, but remain committed to some form of an elephant?

I know what I prefer. Jellybeans enable me the opportunity to change when I need to, and to manage my risk, spend, waste, and time to market.

What does this look like for you?

Defining the Elephant

What do you do if your team, your company, or your client says they need an elephant but you believe they need a whale? Read my previous article, How to Know if You’re Adding Value.

Cloud & Infrastructure

Terraform Managed AMIs With Packer

This article was originally published on Geek and I, January 14, 2021, and has been republished with the author’s permission.

I have been working with a friend on learning Terraform to manage his new and growing, AWS environment. One of the challenges I gave him was to use Terraform to manage the AMI updates that Packer creates or to initiate an update if the source AMI is newer than the current state.

Terraform doesn’t have Packer provider so this requires using other resources built into Terraform to accomplish a working and trackable state.

Problem Statement

Maintain current AMIs based on source AMI and userdata updates and rebuild the AMI as needed when the source, or gold image, AMI is updated, or you update your userdata, using Packer to accomplish customization.

  1. Figure out our source AMI via data; lookup(s)
  2. If source ami-id has changed, then initiate new AMI build
  3. If userdata has changed, then initiate new AMI build
  4. If source ami-id and userdata have not changed, do nothing (idempotent!)

Terraform built-in resources

I accomplished this by abusing the null_resource provider and local-exec provisioner.

First, let’s go find the AMI we need as the source:

data "aws_ami" "ubuntu" {
  most_recent = true
  filter {
    name   = "name"
    values = ["ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-*"]
  filter {
    name   = "virtualization-type"
    values = ["hvm"]
  # Canonical
  owners = [

This returns an ami-id of ami-0c007ac192ba0744b (as of 20210114 in AWS region us-east-2). These AMIs are updated by Canonical periodically, and there will be a new ami-id.

Now that we have an ami-id, we can add that as a trigger to execute changes to null_resource. This has a second trigger to check on the userdata file that will be used to do customization:

resource "null_resource" "build_custom_ami" {
  triggers = {
    aws_ami_id      =
    sha256_userdata = filesha256("deploy/")
  provisioner "local-exec" {
    environment = {
      VAR_AWS_REGION = var.aws_region
      VAR_AWS_AMI_ID =
    command = <<EOF
    set -ex;
    packer validate \
      -var "aws_region=$VAR_AWS_REGION" \
    packer build \
      -var "aws_region=$VAR_AWS_REGION" \

So basically I have the following directory structure that is relevant. You will probably also have backend resource, perhaps some requirements, etc.
-> packer-configs/
---> custom_ami.json
-> deploy/

Implementation via Jenkins or other CI/CD systems is left to you to figure out.

What are the variables used for in local-exec?

I have items running in multiple regions and each region has its own AMIs (and resulting ami-ids). The above has been pared down a bit for brevity.

You can use the aws provider to connect to multiple regions concurrently:

### per region provider info using provider listings
provider "aws" {
alias  = "region-us-east-1"
region = "us-east-1"
provider "aws" {
alias  = "region-us-east-2"
region = "us-east-2"
provider "aws" {
alias  = "region-us-west-1"
region = "us-west-1"
provider "aws" {
alias  = "region-us-west-2"
region = "us-west-2"

Then you can build AMIs in each region. This example code is not complete but the concept is very straight forward:

data "aws_ami" "ubuntu-use2" {
  provider    = aws.region-us-east-2
  most_recent = true
data "aws_ami" "ubuntu-usw2" {
  provider    = aws.region-us-west-2
  most_recent = true
resource "null_resource" "build_usw2_ami" {
  provider = aws.region-us-west-2
  triggers = {
    aws_ami_id      =
    sha256_userdata = filesha256("deploy/")
  provisioner "local-exec" {
    environment = {
      VAR_AWS_REGION = "us-west-2"
      VAR_AWS_AMI_ID =

Of course you can do other things to make it even more dynamic using data calls for aws_caller_identity within the region you are working against and applying it programmatically but I’ll leave that to you for now.

Join the Team

We are always looking for people who love problems and welcome the hard work required to solve them.

"I ran my own business for years, and I worked hard to be overly transparent with my team. To me, people are what make the business. Not the customers. Not the revenues. It’s the folks doing the work.  Trility provides a level of transparency that I appreciate. It’s a focused, yet laid-back culture, where I can count on my team members when needed." 

– Mike Horwath
Product Design & Development

How To Know If You’re Adding Value

Originally published on LinkedIn.

Helping a client get to where they want to go may be different than where you think they should go.

Consider the following: Your company has an outstanding reputation in the industry in which it operates. And folks who work at this company are considered to be some of the best and brightest. Personally, you are a highly educated, experienced professional in your field and one of the top in your company. A potential client contacted your company to explore a project idea they have in mind and you are tapped to vet the opportunity.

Your responsibility is to understand what the client wants and explore whether your company should pursue this opportunity.

For the purposes of this article, a client is anyone to whom you are to provide a product or service; whether internal or external to your company. Additionally, for this article, value is defined as value proposition, one or more desired outcomes; or otherwise stated, getting the client from where they are to where they want to be.

After listening to the client discuss where they believe they are, where they want to go, and their overall definition of done, you find yourself in an interesting position. From your experienced perspective, the definition of done is way-point #10 illustrated below. Based upon what you believe you’re hearing from the client, their definition of done is way-point #4.

Who is right?

Illustration of getting from here to there with a path up a mountain.
Illustration 1: Getting “from here to there” is relative to a client’s current context. Don’t just look at things stated and their implications. Look at the client’s current and historical social, cultural, political, market, and economic contexts. Looking at historical versus actual appetite for change informs the upcoming journey.

You both may be right. However, if your job is to serve the client and help them realize their vision for themselves, the client is right.

Rather than debating the merits of what you believe you know compared to what the client believes they want, meet them where they are.

Consider putting your predisposition(s) to the side and actively listening, learning, and discovering together. Given you’ve already seen way-point #4 on your way to #10 multiple times, you are equipped to counsel the client on options, priorities, risks, and decisions along the journey.

Get the client to way-point #4 as they desire. And while you’re working with them, you may discover they didn’t know about #10, didn’t believe they could get there, or #4 is actually what they need for now.

Consider the below steps to iteratively discover what is valuable to the client now, soon, and later.

Example Behaviors That Add Lasting Value

  1. Ask questions to understand where a client was yesterday, where they are today, and where they want to be tomorrow
  2. Provide observations regarding where they’ve been, where they seem to be currently, and where they want to go
  3. Provide multiple recommendations (options) to get from way-point #1 to #4 (e.g., 1>2>3>4 OR 1>2>4 OR 1>3>4 OR 1>4, etc.)
  4. Enable the client to choose what makes the most sense to them according to their culture, politics, timeline, budget, risk appetite, attitude, and aptitude
  5. Journey with the client by planning, achieving, demonstrating, and refactoring the solution together, iteratively (get them to #4)
  6. Help the client see and prepare for the future while delivering on their needs today (evaluate with them if #5 and beyond are valuable now, soon, later, or never)

What is described above is a purposeful partnership relationship between two parties where discovered and defined value drives decisions. It is a journey composed of goals, options, choices, and the ability to pivot, change, or otherwise re-scope along the way as more information is learned.

How Do You Know You’ve Added Value?

  • A 6-month engagement turns into 3.5 years
  • The client is willing to give you warm introductions to other companies and leaders who may need work
  • The client is willing to provide public/private references regarding the quality and value of work they received when working with you
  • The client is willing to stay in touch, contribute to, and/or be involved in other efforts driven by your company even after you’ve left

How Do You Get Started?

If you are not sure where to start, consider starting with the below three behaviors and the rest will follow:

  • Ask questions and then listen – Ask thoughtful, premeditated questions in order to learn. Clarify what you think you heard, then ask more questions to learn more. Favor questions over statements.
  • Deliver jellybeans not elephants – Practice committing to and delivering frequent, small, iterations of the larger planned deliverable across time. Do not agree to an outcome, disappear for awhile, work in a vacuum, assume nothing changed while you were gone, and return with what you believed was important two months ago. You’ll both be surprised (and perhaps disappointed).
  • Invite change – Frequent, small, iterative deliverables inform the client’s understanding of their reality and future. If your job is to help your client become more today than yesterday, then invite, and be prepared for, change as they learn and realize more with each small, iterative, deliverable.

Having a business, client, and revenue is a continuously earned privilege. To be the best at what you do, there will be no rest. Relationships, communication, and delivering value are hard. This article can help you tune your existing program of behavior, or even help you get started.

Challenged by Change Management?

If your teams struggle to pivot and adapt to changes, read my next article, Of Jellybeans and Elephants, which provides a path and new behaviors for your team to adopt and deliver value one jellybean at a time.


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

Show Highlights

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

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

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

Key Takeaways

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

Read the Transcript

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

37:58 Paul Clayson: Yes.

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

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

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

38:08 Paul Clayson: Yeah.

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

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

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

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

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

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

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

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

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