Building a Lasting Data Culture with Steven Willard

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: Hey, everybody. Welcome to another episode of the Making Better Decisions podcast. Today’s guest is an early adopter of modern data tech and has stayed bleeding edge. I would describe him as a strategic thinker. But with the rare ability, um, technical chops and interpersonal skills to actually drive tactical action forward still, which is pretty cool combo. He has a passion for extracting value and impact out of data [00:01:00] with a focus on building teams, whether that’s data, BI, engineering. Please welcome Associate Director and Enterprise Architect at Organogenesis, Steve Willard. Hey, Steve.

Steven: Hey, hey, good to be here, man. Thanks for

the so much for hopping on. So I’d like to get us kicked off. We have the same opening question for everybody and I’m going to hit you with it. What is the one thing you wish more people knew about using data to make better decisions?

 Yeah, so there’s three things. it really describes things that exist, things that happened, and how they relate to one another. And so, I always go back to that, and that’s something that I always teach any new developer, anybody that’s new to data, and really even seasoned developers, I think, can work with it, or seasoned data folks can work with that, that model, because it’s, it’s really [00:02:00] about, it reduces down what you’re, the complexity of what you’re doing within data.

Uh, to really those three pieces, and any data source that you’re looking at, so you could look at, uh, you know, a fully normalized database that’s coming in, uh, that, that has 800 tables in it. Uh, you could reduce that down into a star schema, uh, you know, that same data could be reduced down into a star schema that has, uh, let’s say 40 tables.

Uh, or that same data could be reduced down into, let’s say, one giant table if you’re using it for machine learning or something, or one big table, or a Franken table, or however you want to refer to that. Um, no matter how you’re structuring it, it could be a CSV, it could be JSON, no matter how you’re structuring it, all data is really only doing those three things.

So it’s, it’s telling you, it’s showing you, and describing things that exist. Things that, uh, kind of giving you a description of things that happened [00:03:00] at those things that exist and then how they relate to one another.

Ryan: Yeah, we’ve had a couple people on that have gotten to talk a little bit about modeling, you know, with the understanding, you know, that like going, you know, super nitty gritty, like meh, but like the, the, I, the general idea that you talk about is that there are different structures that are valuable. in different scenarios. So if I have a little, you know, or big application that, you know, it could be something that my business is running on, or it could be something like Facebook, a set of tables that make sense for that. And I might structure that, like you said, like fully broken out or normalized into being, you know, Hundreds of thousands of little tables.

And then we got history snapshots and all this stuff. And then when we [00:04:00] start thinking about different purposes, there are these different structures. And, you know, for those that might not be familiar, tell us a little bit about like, what is a star schema and how is that useful to like a decision maker at a company versus a developer?

Steven: Again, I think, and this is maybe how I arrived because I, I did a lot of, uh, I did a really deep dive into data warehousing and, and, uh, and star schemas. And. That’s maybe how I arrived at that model and that kind of deeper understanding of, of really all data is a star schema is really just taking your data and breaking it into the component part.

So the nouns and verbs of your data. So the facts and dimensions of your data, the things that exist and the things are in that order, the things that happened and the things that exist, um, yeah. So, a star schema is just taking your data and building it, uh, [00:05:00] and structuring it for analytical purposes. And I think it’s, it’s also in a, uh, it’s also a more logical, easily human understandable, uh, format, I think.

Ryan: Yeah.

Steven: Because it, because it’s like I said, it just kind of, I always go back to that and I feel like it maybe it seems like an oversimplification, but that’s literally all you’re doing with data is those three things. And that’s all a star scheme is. A dimension is a thing that exists. Um, so you have an account, you have a rep, you have a product, and then descriptive, descriptive elements of that thing that exists.

Uh, a fact is a thing that happened. So it could be a sales transaction, a transfer of something, it could be a, uh, you know, logging, uh, if, if you’re talking like IOT data, it could be, uh, logging a temperature, uh, of something. It’s, it’s really anything that’s happening that’s going to happen over time. Uh, and, and it’s going to accumulate.[00:06:00]

Uh, dimensions don’t accumulate. They generally, I mean generally, you may have, you may add additional accounts, you may add additional reps, uh, additional reps, but the rate that it’s accumulating is much slower than a fact table. Uh, and that’s really what, uh, Uh, you know, star schema is doing is composing it into those, uh, really those three elements.

Ryan: Yeah. Yeah, I, I love the point that you made there about kind of the, the ease of understanding, you know, yeah, sure. A lot of the kind of modern tools, at least the modern analytics tools that are out there, they do a really, really good job of kind of storing and, you know, all the, all the techno mumbo jumbo under the hood works really well.

With that structure, but I, I would argue that it’s really the most understandable format for, for data. It’s just kind of being like, Hey, where are my sales? Well, we put them in the sales table, right? What about, [00:07:00] you know, accounts? Oh, they’re in the accounts table. Um, and then you have all the information you need about accounts there. Um, so I think that as we You know, you mentioned the term data warehouse, which is just, you know, essentially a model that lives on a database, hopefully a big and comprehensive and great one. You know, I think that sometimes these infrastructure projects can take on a life of their own and they’re just like exciting from a technological standpoint. But being able to communicate to the end users, like, Hey, well now you have this like very clear, concise location where you can go look up information about your business. Like that readability matters. And, and to that end, one thing that, you know, I know that you’ve done Over your career has you have pretty diverse experience at what I would argue are pretty much [00:08:00] all of the levels of kind of like the the data and analytics maturity curve.

So starting off, it was kind of like we have You know, information and lots of these sources and we’re going to like hack something together and we’re going to get an answer right now. Hooray. And then moving from that into, okay, well that’s, that’s great if it’s a one time analysis, but no good deed goes unpunished and now I’m responsible for that. That hack job that now needs to be a monthly report and it was kind of painful. So I don’t want to do that anymore. And so then you, you kind of grew into changing that and then making processes more repeatable, starting to gradually invest in infrastructure. And one of the things, you know, that I know from talking to you is that, let me take a tiny step back.

One of the biggest pieces of advice that I give people when they’re getting into infrastructure is like. Don’t shoot the moon all at once. Like find something that’s going to have impact for the organization. Focus on [00:09:00] that, deliver that, and then. incrementally roll those successes. So for somebody else that’s out there that maybe the maturity level of the solution and the infrastructure that they have for data and they have for making decisions and getting answers, maybe that’s not where they want it to be. What’s some advice that you might give to somebody like that to up the game, but to not go all in and have it go sideways? Hmm.

Steven: I think, uh, I, another thing that I always go back to, I, I have these, all these models that I think I always go back to that I try to fit things into. And, uh, for that, uh, it’s really that everything compounds. So, everything compounds over time, and it’s either, it’s either compounding positively for you, due to your effort, or it’s compounding negatively for you.

Um, you know, like, recently, over the last few months, I may have eaten a little too much, and, uh, and I think my weight negatively compounded. [00:10:00] So, um, so now what I, uh, what I did is I started adding, I started shifting things and changing. And so the compounding is now sort of working in the opposite way and I’m losing weight now.

Um, so it’s, uh, I think. It’s that can be really applied to anything. And so if you look at, uh, uh, if you’re trying to build out an organization and build out, uh, like a, a data culture or start starting to, or trying to build and starting from nothing, you have to really look at what are going to be the bigger drivers.

So the Pareto, you know, the 80 20, what’s going to give you the most value for the minimum amount of effort. Focus on that. and then really just incrementally compound on that and, uh, and grow that. But find what’s going to make the biggest impact for the minimal amount of your effort. And then focus on that, and then just iterate over that over time.

And then compound over that and let [00:11:00] that, let that grow. And it just kind of naturally, it naturally happens. I think a lot of times people will. Look at, uh, you know, I think they start with this sort of perfect end state in mind, and they imagine that they’re going to build that, or they’re going to build this, they’re going to fully organize this project, and it’s going to, it’s going to have every, absolutely everything, uh, that we think that we could ever need.

Um, and we’re going to get that done. Uh, we’re going to get that overnight. Uh, it’s not a microwave solution. It’s like everything is going to, everything is subject to that compounding. And I think if you can work with that compounding, um, by starting really and then starting with something and then just growing and then just building and iterating over it, you can’t, I don’t, I don’t think you can fail.

Like, I don’t, I don’t, I don’t feel like that is, it’s possible to fail if you do that.

Ryan: Yeah,

So long as you keep moving, like eventually you’ll get there. You know, it sounds like a, [00:12:00] like a Yogi Berra quote or something like that. But know, I, I, I think back, I don’t remember the exact quote. I think I may have actually even mentioned this a while back on the podcast, but there’s kind of this idea. Um, you know, a lot of the, the interviews from Kobe Bryant, like people will talk to him about like, Hey, man, like, how are you so incredible at basketball? And he always talks about being addicted to the journey instead of the results. Yeah. And I, I very much, you know, maybe that’s like pie in the sky or a silly analogy, but I think it’s very much the same here.

Like the idea of making a computer flawlessly understand every intricacy of a business. When I word it like that, people are like, Oh, Whoa, that’s a lot.

And it’s like, okay, well that’s, that’s what it takes, you know, so it’s, it’s going to be this, you know, constant iterative journey of like always understanding the business better, asking better questions, which allows us to dig in further and find idiosyncrasies and keep [00:13:00] on and driving and driving.

And from, from talking to a lot of people on the podcast, You know, something that I had felt seems to be shared by most people that work in the field, which is even though it’s a very technical field and it’s very computer oriented, it’s still mostly about people. You know, the rubber meets the road for all analytics where a human being makes a decision. Um, and I think there’s also, you know, all of the questions of bias and buy in and is the person even reading this awesome report that we’ve made and all of that stuff, you know, how do we build a team? How do we build data culture? Over the course of your career, I’ve seen you do that. And in your particular case, I would highlight kind of two specific. specific aspects of data culture. One being, okay, how do I build a team of [00:14:00] analytics professionals that can, you know, understand analytics and stay on the cutting edge. And like some of the topics, you know, modeling all these topics that we’ve talked about before. Another thing that I would love your take on is how do you build the culture on the consuming side? I think that that’s something that. is really difficult is to kind of teach people the art of the possible and break them out of, you know, existing manual processes. How do we get large teams of people excited and using new analytics?

Steven: think again, so it starts with compounding. So you start small, you start with something and you start somewhere, uh, and you solve, uh, try to really rapidly solve some big problems. Um, and then you just iterate over that. So you grow and you build that. Um, and then, Because I think what [00:15:00] I’ve seen in tech in general, not just data, but, uh, you always go in, you always, there’s been so many times in, in my various different career lives, you know, my, uh, the various different parts of my career that I would launch, let’s say you send an email blast out, if we’re talking marketing, and you think this was, this email that I wrote was amazing.

There’s uh, there’s no way I’m not going to get a hundred percent click through rate on this thing. Everybody’s gonna love this thing. It’s, it’s uh, it’s phenomenal. We’re gonna make millions of dollars from this one email and then one person clicks on it. Or it’s just not, it’s the exact opposite of what you think.

So I think it’s, it’s You know, it’s about finding little things that, that really resonate with people and are solving their problems. And so I think that starts with really listening to them, genuinely wanting to help them. So genuinely saying, what is your, what are [00:16:00] your biggest pain points? Where are you struggling?

Um, and how could I automate that or help you get to that information quicker, uh, with, uh, with data? Um, and then you, and again, you just iterate over that. And I think if you do that, you you’re solving little problems over time. You’re solving one problem today, one little problem here, one little problem there.

Um, but it compounds over time. And eventually you look back and it’s like, this is a, these people are excited about this data and they, you know, they are banging down the doors for this stuff. Um, but it’s really because you’ve focused on. like these little micro problems over time. And that just accumulates over time and it becomes, uh, and it becomes a whole different, a whole different beast.

Um, and then, you know, I think, I think it sort of transitions and changes a little bit from, all right, you’re solving these micro problems, to then now you’re having to look at it [00:17:00] And it’s a slightly different beast, but I think it still starts, uh, it’s still, uh, it’s, it’s, I think you should approach it very similarly.

You know, I always go back to MVP. What’s the minimum viable product? What’s the minimum unit of value? that I can deliver for the maximum, or the maximum amount of value that I can deliver for the minimal amount of effort. So you do that, you solve the biggest problems, uh, right out of the gate, and then you iterate over it.

So that people are So that people get excited because they say, wow, they’re actually listening to me. They’re actually, they actually care about what I want or what I need. And then you just continue to grow that. And then it becomes part of the culture. And so I think then it just becomes a natural, uh, part of it.

It’s not something that you. That you went in and all of a sudden, you know, this week, nobody cares about data. Next week, everybody, everybody’s banging down your door for it. It’s, [00:18:00] it’s something that happens over time, but it’s all about just applying very, you know, incremental. Effort and incremental growth.

Ryan: I, I love that answer. I, you know, I’ve found in my own time getting to work with a lot of people that data is just everywhere. It’s a part of everybody’s job. They may not think of it as data or a data process or something along those lines, but it’s, it’s kind of like, I remember when I was first starting my career, There were still like a couple of people where we’d be like talking about a process and they’d be like, Oh, that person like doesn’t use computers.

And I’m like,

Steven: Yeah,

Ryan: you know, and you know, where, where’d all those people go? You know what I mean? Like, uh, especially with it, you know, like, obviously there are some jobs that like, just don’t call for it, but like in the standard kind of like, Hey, we make products or [00:19:00] services like in the, like. Kind of like business world, right?

Like most people are using some flavor of technology, even people whose bread and butter is with their hands. They’re probably still using technology to log, to invoice, to do all of these other processes. Like there’s just no getting away from it. And I kind of feel that data is the same thing because every single one of those technical things.

You know, tools that we’re using is logging data somewhere. And that’s the lifeblood. Like if I want my invoices to go out accurately and on time, well, I better have made sure that my data was in there properly. So one of the things that you mentioned that I find myself talking a lot about was you use the word automation and. I could imagine someone being like, wait, I thought you were like a data guy. What does automation have to do with data? So I wanted to put that question to you and give a question like, have you given an answer? [00:20:00] How is it that data people are automating things?

Steven: um, well, I mean, there’s a lot of people that are that are pulling together data or reports or information in very inefficient ways because they’re not data professionals. There’s a lot of like, Something that, um, I think maybe I actually heard from you years ago that I still read, still resonates with me that I always think about is, you know, I remember at, uh, at a conference that I went to, um, uh, I, you said that, You know, when you first became an analyst, you thought that most of your time was going to be spent analyzing things.

Um, you know, 80 percent of your time would be analyzing things, 20 percent maybe pulling together data. But it ended up being 90 percent pulling together data and 10 percent actually thinking about it. And so I think about within most organizations, you have tons and tons of smart people [00:21:00] with advanced degrees and years and years and years of experience.

And what are they doing? They’re They’re extracting five Excel dumps from various different places, they’re VLOOKUPing things, they’re building these, these mad, crazy, uh, these crazy reports that they have to do manually, and it’s, and, uh, you know, they’re spending half their week doing that type of stuff, and we have the ability to then automate that and give them their time back, and then enable them to actually utilize those years of experience and, and their advanced degrees and their, They’re knowledge of the subject of the area that they’re supporting, um, with, by actually looking at the numbers that they’re, that they’re generating.

Um, so I think, uh, that was something that I think I learned early on. And I think that that really helps you to, I think it helps ingratiate people around the business to you because you’re able to, you’re able to help solve their problems. You’re able to, uh, you’re able [00:22:00] to help them get to the answers that they need to get to.

Much quicker and easier. Um, And that’s, I think, that was, I think, one of the coolest parts about being in, uh, you know, working in data is you’re able to legitimately help somebody. There’s so many times, uh, you know, you walk around the business, any business, you can walk around and you talk to 10 people and I guarantee you could find a few people that you could, uh, you know, let’s say, I’d say half of those people.

Half of the 10 people have some process that you could automate, uh, with, uh, some sort of data tool. Especially the modern data tools make it so easy. It’s, it’s insane. It’s, it’s insane compared to, you know, just five years ago. How, how fast it’s, uh, it’s, uh, it’s grown and how much, how exponentially it’s grown.

Ryan: So one of the things that you had mentioned was really [00:23:00] actually listening to people, which, you know, Hey, crazy idea, actually pay attention to people and like great results come out of it. I think that one of the things that I have seen folks. struggle with is being able to like actually hear technological solutions in business pain. And that’s something that I know from knowing you that you do a really good job of. So if there’s someone out there that has the desire to be helpful and is learning more and more about the technological side of data, what’s some advice that you can give them so that When they’re talking to someone and this person is talking about a problem, how can they identify that that person’s actually talking about a problem and connect that to a technical solution?

Steven: That’s, I mean, that’s a great, that’s a great question. I think the, [00:24:00] I think it really comes down to staying current on whatever tool set you’re using. Um, you know, I think if you don’t know the capabilities of whatever tool set you have available, and you’re not constantly keeping up with it, uh, then, and you don’t really know it inside and out.

So I think it really is important to understand, uh, whatever tool set it is that you’re utilizing, um, or that you have available to you as best as you can. And really just, whether it’s reading books, watching videos, reading blogs, just constantly keeping yourself up to date on it, because then you can go into a conversation and you have this vast tool set, uh, uh, and, and understanding of ways that you could solve and, uh, ways that you can solve problems.

And I think that it also, uh, boils down to, um, really just kind of, for me, pattern recognition over time. So [00:25:00] once you’ve solved one problem, you always, I always try to think of the model of, or the structure of that problem. Um, and how could I, how could I I’m going to tease out what actually happened here, you know, like what actually did I do to solve this problem?

What’s the structure of it? And so then when somebody else comes up with a problem that may seem more complicated or may seem totally different, if you can deconstruct that problem and then and then understand the sort of component parts, then it’s, and then apply a model that you’ve already, that you’ve already applied.

And I think that, that could really help with that. It’s really, I think, just those two things, but I, and I think really having access to different models and understandings and, and really, uh, you know, a deeper knowledge of your tool set helps you to build those, build those models. And so, again, yeah, somebody comes up and says, well, I need to do X, Y, Z.

You know, I think [00:26:00] what I, you know, getting back to the, you know, data is just telling you three, three things. It’s really, I’ll decompose it into, oh, okay, so what are the things that they’re trying to describe? You know, what’s the thing that they’re describing? What’s happening? What, what is happening that they’re trying to describe?

Um, like, where is this data? Um, And I think if you can reduce everything, take, because, because they may be saying, well, I’m trying to log into this API and do, I mean, maybe not a business user, but, um, but, you know, if they’re, if they’re coming to you with very technical, uh, uh, technical terms, it’s easy to get lost in all the, the technical jargon and everything.

But at the end of the day, uh, let’s say you’re trying to move data from one place to another, It’s really just, it’s just, I’m trying to take, I’m, I’m. Taking data from here, I’m doing something to it, and I’m putting it over here. [00:27:00] So, if you can reduce it down to the smallest component parts, then I think that really will help you to model out how you could solve, how you can solve, People’s problems.

And then you just have to look at, uh, again, I think it just takes time of solving lots of different problems and then learning different models for, for how to, uh, for how to approach things and really diving into your tool set. So understanding it

Ryan: Yeah.

Steven: as deep as possible, um, because then you have, you have that framework to go with.

Um,

Ryan: Yeah. I, I I couldn’t agree more. I think, like, even as soon as you started answering it, I think there was, like, This hope in my head, like, hmm, maybe Steve has this like cool shortcut that doesn’t involve just like spending obscene amounts of time becoming an expert in

Steven: no, no,

Ryan: But it’s like there, there

isn’t, right?

Like if you want to be an expert, you got to go make yourself [00:28:00] become an expert.

Um, you know, and then one thing, if I can be so presumptuous as, as to speak for you, I think that one other thing that I’ve, you’ve talked about and I’ve seen drive you is the reward you get from impact.

Like, I know we’ve talked about how much you love working at Organogenesis because of the impact that your company has on people’s lives.

And I

would extend that to, you know, about data. We were, we originally got into this conversation talking about pain points. And I, I definitely identify with this idea of like, when I am talking to someone and I’m describing a painful way that a prosthetics, process exists. And I see this person like identifying. And then I tell them that there’s like a really quick, easy way out. And they don’t have to live in that anymore. Like the light

bulb that happens in their eyes is like the best payoff. Like that’s what makes getting up and coming into work in the morning worth [00:29:00] it. Um, you know, and I’ve seen that be a driver for you too.

I think that motivates some of that learning.

Steven: Well, no, I think yeah, so it’s it’s I feel like the two have played off of each other where I’m at Because I think that yeah in the early days It was super exciting to get in and solve somebody’s two hour a week problem, you know Spend a spend some time So, save them 2 hours every week, you know, and you think of the impact of that over the course of a month or a, or, you know, a quarter or a year.

Um, but then just doing that over and over. I thought the, the impact of that was so cool to be able to. But then I think if I thought of the larger picture as I zoomed out, um, it’s like me helping that person be more efficient within the organization that I’m at, that I’m in, and within the, you know, industry that I’m in.

That actually means that. Maybe their efficiency may lead to a patient, [00:30:00] uh, uh, to a patient being impacted, uh, to a patient, uh, keeping a limb or, or something like that. And I think that was, that was, uh, was a huge driver for me as well. And I think that was kind of my initial big driver. Um, you know, I think was initially what pushed me and then as I got on the grounds, uh, I got super excited about being able to solve those individual problems.

Um, and then now, I think, you know, I think the. Excitement. I think the excitement now comes from the fact that, so I have a much bigger impact at a bigger scale. So it’s a, that’s like, it’s amazing to think that, that I can impact and influence these things at this larger scale now. It’s like, it’s pretty cool.

It’s giving me chills.

Ryan: Yeah. Yeah, I, I had a similar, you know, kind of change in mentality over time. Like when I started [00:31:00] off, I was just like, man, I love this stuff. Like I’m solving like cool puzzles and it, you know, everybody else seems to be like hyped about the solutions that I make and like, they’re happy. I just get to be over here solving like.

Business Rubik’s cubes all day. Everybody’s happy. This is great. I’m never taking my hands off the keyboard. I just want to be like an individual contributor for the rest of my time. And then, you know, as I mentioned before, no good deed goes unpunished, right? If you’re a good enough individual contributor, they just somehow think that that will make you a good manager.

And you know, bang, you start managing people. And even though it had been something that like my own personal drive hadn’t Really been for like, in fact, it kind of intimidated me. I was like, I don’t want to be in charge of other people, you know, like computers, like follow instructions all the time, you know, um, I also found that payoff so [00:32:00] rewarding, right?

Like having the opportunity to actually build a team of people that are excited and can now make something more than the sum of all of the parts and getting to invest in another human being’s development

and see them succeed and see them get better and see them learn new things. Like see the light bulbs come on for them, whether that’s someone that You know, I’ve worked with or whether it’s a client or, you know, some, you know, pet project, whatever.

Like I’m totally with you. I get the chills just thinking about, you know, getting to work with people, building organizations of people and helping people make their lives and things around them better is one of the most exciting parts of data.

Steven: Well, no. Yeah. So I think that was the, the next step of, uh, so, uh, because I was talking about the, the scale of the, of the system, um, and, and how, the impact of that. But yeah, the, the scale of, uh, of being able to build a team and like you said, just actually build people and. and really find where they [00:33:00] fit within the organization.

What is this person’s individual niche? How do I, how do I help them to grow and how do I get them on a continuous, how do I get them, uh, to have the same excitement, uh, that I have about it every day? And how do I drive them to, um, really. You know, um, how do I drive them to, to want to continuously grow and, and continuously, uh, get better at their, at their skill set and their tool set is, uh, and then just become better people.

I think in the, uh, in the, you know, in the process is awesome. It’s

amazing. talked about a couple of things, but if I expand the scope a little bit and give you free reign with this question, what are some of the things within the data world that you have found most challenging [00:34:00] and what have you been able to find to solve those hard problems?

Most challenging. I mean, I think It would be that organizational, uh, piece. I think it would be around, uh, really finding, finding excited people that are willing to grow and that have the capacity to continue to grow. Um, because I think all of the, even in the, even in the last, I mean, if you look at the last 10 years, how much The industry has changed.

Um, it’s constant. It’s a totally different, like, the, the things that we have access to now that are just sort of, you know, a [00:35:00] baseline are, were, would barely even existed, or would take teams of people to, to build 10 years ago. Um, but, you know, I think the, the, the real, the, the biggest. You get the biggest value in finding, or I think the biggest challenge that I’ve run into is just finding the right people and finding people that are willing to, uh, to take it on and grow and go in with like a growth mindset of, I may not know this, or this is an entirely new modality, but I need to figure out how to, uh, how to, how to do it, how I may need to learn an entirely new language.

I need, I may need to, you know, just. Finding that and then driving that around, you know, building that into the, uh, into the culture has been, uh, has been, I think, the biggest challenge. And it’s, it’s all about just, I think, [00:36:00] again, just pushing a growth mindset. So just finding, and then really finding people that, and hiring people that, uh, have, have a growth mindset.

And It’s tough because sometimes you may look at two different people and then think, you know, on paper, one person, uh, one person has, you know, 20 years of experience doing all, uh, doing all of this stuff. And you’re like, man, this guy’s going to be a rock star. They have 12 degrees. They have all this stuff.

And then in actual practice, It’s like, what were you doing for 20 years? You know, uh, but then you got this other, you got another person that’s been doing it for two years and they’re at the top of the game just because their willingness, they have the willingness and the attitude to, to go out and, uh, and learn it.

Um, so I think it’s, it’s really been about, uh, about finding those people. And it’s, and that’s been the, that’s been, I think the hardest part because, because yeah, like I said, in the beginning, in my early [00:37:00] years, I would talk to a consultant. I’d kind of look them up on LinkedIn and I’m like, how am I even going to have a conversation with this guy?

Um, and then, and then you actually talk to them and you’re like, they don’t know anything about what I’m needing them, you know, what I’m asking them about. This is crazy. Um, so it’s, it’s about. I think my biggest, I think the biggest challenge has been finding the people that are willing to, uh, that are willing to go with you and grow with you.

Ryan: Yeah. Yeah. Well,

I, I.

Steven: like that, but let’s go ahead and get it started.

Ryan: well for what it’s worth, I can second that. Yeah, I mean, I think that it takes a really unique blend of, you know, experience, but then You know, how, how is it someone’s supposed to garner experience when the landscape changes and is [00:38:00] changing faster every single day? So it’s kind of this like slim portion of personality types that can do that.

And, you know, from having the opportunity to meet. a good chunk of your team. You know, I’d say that you’ve done a good job meeting that challenge. Um, one thing that we love to do at the end of the podcast is give everybody a chance to get to know you a little bit better. So I think we’ve pretty thoroughly established that, that Steve knows data, but we want everyone to get to know Steve.

So tell us a little bit about you. What makes you tick? What do you like to do outside of work? Open season.

Steven: I live in San Diego. I have, uh, I have two kids. I have a five and an eight year old. Um, most of the time I’m, uh, I feel like throughout the week I’m, you know, Now that I have an eight year old that’s in soccer, it’s pretty much my life now has become, uh, has become taking him to soccer games.

[00:39:00] think, uh, so that’s awesome. I think, uh, outside of, uh, outside of that, I like to golf. Uh, I think something recently that I tend to go down these little rabbit holes of, uh, of sort of, of different, uh, hobbies or interests. I’ve always been a musician to some degree, uh, back since, you know, since as far as I can, I think probably middle school or, or beyond.

Um, and one thing that just recently I got, I’ve gotten super into has been, uh, like, uh, synthesis. So, uh, synthesizers. Um, and I think what’s really Been cool about that is it’s something that kind of satisfies like both sides of my brain, like my creative side of my brain that is, uh, constantly wants to create.

And then my technical side of my brain. You know, what I th thought is when I first started learning about it, what I thought was really cool was, uh, synthesizer. you know, all sound [00:40:00] is just wave particles. So it’s really just, it’s just different waves. And so with a synthesizer, you just take, you just start with these base, basic waveforms.

So you start with, you know, like a sine wave or, or a sawtooth wave. Uh, and then you just, you just add different elements to it. So you add, you add a tact or decay or, or different filters, or you add all of these different elements to shape the sound. And really all sound is just. Uh, you know, if you go back to, like, thinking about the atomic elements of everything, it’s like, like, when we were talking about data as these three, these three elements, these things that exist, blah, blah, blah, things, things that exist, things that happen in the relationship, you layer on the complexity from there, um, it’s just like sound is the same thing.

You just take these basic sound waves, and then you alter them with all of these different, uh, you know, all of these different, uh Frequency oscillators and LFOs and all this [00:41:00] stuff. And you can literally create any sound. So I think that’s something that I’ve really, uh, that I’ve really dove deep into.

That’s been kind of like my, my most recent, uh, you know, uh, um, I guess obsession that I’ve had over the last few months that I’ve been diving into. So, yeah, so, um,

Ryan: I love it. So if anybody’s learned anything or love what you had to say and wants to reach out to you or check out some of your creative outlets or anything like that, what are the best ways to get in contact and check you out?

Steven: so, uh, you could send, send me an email, uh, steven. willard at gmail. com.

Ryan: Nice. I love it. Direct access.

Steven: Why not?

Ryan: Awesome. So Steve, Thank you.

for taking the time to make yourself into an expert. You know, so that you could come on here and share it with me and all the [00:42:00] listeners. It’s been an absolute blast having you.

Steven: Thank you, man. Yeah. It’s, uh, it’s exciting. And it was, uh, it was, it’s cool because I think you were kind of there to see me go from there to here and, uh,

Ryan: yeah.

At least pieces of the puzzle. Yeah.

You did a lot of, a lot of lifting on your own, but I did get to see a couple of cool moments. Yeah, for sure.

Steven: which I thought, uh, it’s been awesome. And dude, thanks for inviting me and, uh, I’m excited. I’m excited to, uh, to see it.

Ryan: Um, all right, let me, let me try that again. All right. I also want to make sure to thank the listeners. If you heard something that you liked or made you laugh today, please make sure to like, subscribe, share, tell a friend, do something to help us keep the podcast going.

It really helps. Steve, thanks again so much for being here.

And this has been another episode of the Making Better Decisions podcast. Thanks for listening.

Outro: That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our [00:43:00] sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make better decisions. Thank you so much for listening. We’ll catch you next week.