The Data Stack Show - 188: How To Invest in Data Infrastructure and Data Projects That Create Business Value with Matthew Kelliher-Gibson of Rudderstack

Episode Date: May 8, 2024

Highlights from this week’s conversation include:Matt KG’s Background in Data (0:35)Challenges in purchasing data tools (1:28)Early experiences in data analysis (9:51)Matt’s Transition to a subprime auto loan company (13:19Transition to RudderStack and software purchase decisions (17:36)Tech Problems: People and Process (22:02)Challenges in Purchasing Data Tools (22:55)Budget Constraints and Purchasing Decisions (24:46)Challenges with Platform Documentation (26:55)Metrics and Cost Efficiency (30:11)Risk and Conviction in Purchasing Decisions (32:53)Justification and Value Creation (38:17)Connecting Data to Business Value (42:03)Navigating Business Relationships (46:25)Empowering Analysts (49:54)Relational Capital and Team Competency (51:29)Final thoughts and takeaways (54:16)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Hey, Data Stack Show listeners. We were so excited that we got to meet a few of you live in San Francisco at Ruddersack's Data Modeling Workshop around identity resolution. And we have another one of those events coming up in London. It's on May 15th, and we would love to see you there if you're in London or close by. The workshops are great. It's super fun. We'll have some snacks and drinks afterwards, and you'll get to meet not only other like-minded data people, but also potentially a few show listeners. So you can register at rudderstack.com slash events. See you in London in a few days. Welcome to the Data Stack Show.

Each week, we explore the world of data by talking to the people shaping its future. You'll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by Rudderstack, the CDP for developers. You can learn more at rudderstack.com. Welcome back to the Data Stack Show. We are here with Matt Kelleher-Gibson, and we have so many exciting things to talk about. Matt, welcome to the show. Thanks for having me. All right. Well, I have the privilege of working with you every day, so I know a lot about you,

but our guests don't. So give us your background, your brief background, and tell us what you do today. So I've been in data and data science for a little over 10 years. I started as an analyst, data scientist. I managed data science teams, including and even ran the data science function at a public company before coming to RouterStack.

And now I am a technical product marketing manager here. We'll definitely have to talk more about that career transition because I think that's definitely an interesting one. Well, very excited to chat today. Matt, one of the things we talked about before the show was driving business value with data. So I'd love to hear more about that and then whatever other topics you want to cover. Yeah, I think that'll be a great one. I think also just looking at, you know, what the process is like trying to buy data tools

and kind of why it's so hard and what you can do about it. I think that'll be another great topic to dig into. Yeah, definitely. All right. Well, let's dig in and talk. I think the business side of the conversation is going to be really interesting. So let's dig in. Matt, there are so many topics we want to cover today, especially around sort of the business side of working in data. But first, how did you get into data? And actually, I don't even know, we work together. But I don't even know if I've asked you this question. So I apologize.

Yeah, so it's a little bit of a roundabout story. You know, the funny thing is they teach you, like when you come out of grad school, but always have like a very linear, everything has been leading to this moment and getting this job when you're in a job interview and that's not at all what it's like in the real world um so i actually started off i went to my undergrad is sports management which is a business degree really i was going to try to work in League Baseball in a front office. But I graduated in 08 when the entire world fell apart. So I almost got a job with the Seattle Mariners. I interviewed for a few other kind of internships that next year,

went to spring training, got to 09, and there was just nothing there. Like, it was just dead. So I was like, all right, I'm not doing this because trying to work in sports is actually costs you money because wait, what do you mean so much? There's so much over demand to want to work in there. Like minor league teams are like, we will pay you nothing. And by the way, you have to pay a college to get credit so that we don't have to give

you any benefits or anything like that. It's, I mean, it's just, it's all run on the back of unpaid labor it's it's an interesting thing so i did that and then i weirdly spent a year actually in politics so i ran at 24 i ran for a school committee in my hometown that's a longer complicated story but i i ended up not winning but i lost to the chairman of the board by less than 200 votes so i like came very close that actually led to me running a state senate campaign which once again we didn't win but it was a first time candidate and he got i think it was about 40 of the, which was very impressive at the time. For comparison, two years later, someone else ran in a more competitive district in my town and got 28% of the vote.

So I feel like we did pretty well there. That rolled into, I made the decision to go back and get my MBA, particularly because I had an old professor who was like, if you're going to go to grad school, that's the degree that will have the most currency long term. But my one condition with that was I needed to go somewhere where it was going to be hard because otherwise I would get lazy because that's what happened in my undergrad. So I originally only applied to places where I could do a major because I needed something really hard. So I eventually got into grad school at a university called Bentley university, just outside of Boston in the Waltham area, which is like a well-regarded like regional business school.

And I came in as a mba masters of finance dual degree and then i went they had a concentration night where you go and talk to everybody and the different advisors and the guy was like yeah there's no jobs in finance and we were talking and he's like you should really go talk to the business analytics concentration advisor i was this little french woman who was one of the math professors there. And she was describing what business analytics was because there wasn't a lot of good language around this field back then. And it was kind of like one of those where it's like, wait.

So it's like all the parts I like about some of these other majors, but like none of the bad parts. Because it was like, you know, accounting is interesting until you get to like Philo, Lilo, all that stuff. And I lose interest, right? Like finance was kind of interesting. And then they got into a lot of balancing stuff. And I just didn't really care. And this was like just the numbers part I like to do.

Yeah. So I ended up switching to being just an MBA with a business analytics focus. Took a bunch of statistics courses, what was essentially a SQL course and like a PhD overview of machine learning. So where we had to read papers and teach them to the whole class. So that was interesting. Wow. But my assignment was actually neural nets. So I got a pretty good foundational understanding of them back in like 2012.

Oh, wow. Okay. Yeah. I have to start to interject really quickly, but so major leagueball and politics, I feel like both of those, while running for a race, or running a campaign, I guess running yourself, but were you already predisposed to statistics?

I mean, Major League Baseball and then running a political campaign, to me, feel extremely predisposed to statistics because both of those, you know, have huge statistical underpinnings. Yeah. So I've always been predisposed to math, so to speak. I actually wasn't huge into the statistics for when I was trying to work in baseball because this was like post money ball. Everyone was trying to work in baseball because this was like post-Moneyball. Everyone was trying to do that.

So I tried to be a little bit of a jack of all and kind of ended up being, you know, I didn't have something to hang my hat on. But then when I was actually running the state Senate campaign, I had access to the state voter vault, which was run by the party, which had all this information on who voted and if they had answered previous like phone surveys and stuff of affiliations and things like that.

And I actually, just using Excel, I had tracked previous elections. And based off of that, I was able to make some predictions of where I thought turnout was going to be. Which the interesting thing was, like, like politically oriented person in the district I was in was like, Oh no, that's not going to be the case. Cause we had just had, I was up in Massachusetts and there had been a special Senate election earlier that year. And I was like, I feel like, you know, it's 2010, it's an off year. It's going to be somewhere between like Oh six and like a special election.

Everyone was like, no, it's going to be a presidential election. It was like smack dab between the two. So I got that one pretty much right. And I was able to then use it to basically be able to say there was like one district that was like 40%. There's one town that was like 40% of the whole district. And I'm like, if we don't do at least like 40, 45% here, we have no chance. Which ended up being true.

So that was kind of the thing. And that also kind of pushed me when I, part of what pushed me when I went to get my MBA was because everybody thought they knew what drove voters. And they were all wrong. And if you would talk to people who would run like stuff for the state party, they would tell you a very different story, right?

Like they actually knew more about what it was. but it just, it put me down this path of thinking about how there's like what people think works. And then there's what like reality actually reflects and data was a way to get to what that reflected reality was. Super interesting. Okay. I love that. We could keep going down that path, but we still have

several years of story to cover. Okay. So you finished your MBA, you dug deep into neural nets, you know, 2012. So sort of at the beginning of mass modernization, like very early innings there, I guess to use a, an appropriate analogy. What happens from there? So from there, I ended up taking a data analyst job with a, like a marketing consulting company that was focused on like direct marketing, which one of the big reasons I took that was because I would actually get to build models like right off the bat. So what kind of models? They were like response models.

So think like, think like checking acquisition campaigns, right? We send you a letter. It says, if you open up an account, you keep at least a $500 balance. We'll pay you $50 after.

Oh, interesting. Okay. Yeah. So I, that was kind of what I did. So, I mean, I made like over a dozen models in the one year I was there, including I think in my last month,

I made three or four for Medicare Advantage companies, which was interesting. Wow. But that was also kind of like a crash course. There's school and then there's like the real world. Because like, I mean, there were processes in place that like did not work. And where there was like, they had these like special proprietary, this is how we, you know, can project who's likely to respond. And when you dug into it, it was actually, there was like a ton of data leakage in it.

So they actually weren't that good. And then they had to layer on top of them all sorts of filters and stuff like that. And the tough part was explaining to people because whenever you're talking about like, hey, you're taking future data and you're putting it in your training data, it's very hard to explain that.

But so I did a lot of that. I mean, I also, that was my first time when it's like hard to explain that. But so I did a lot of that. I mean, I also, that was my first time when it's like, okay, we had a process to match customer files to our consumer file. And it was like, all right, so where's the standard? This is how we do it. And for the first time, but unfortunately not the last, I got the answer of,

well, everyone has their own version of it. And I instinctively was like, that doesn't sound good. Yeah. So I became an early proponent of like, we should all have, like this needs to be standard. That way if it fails, we all fail and we all know it fails. And we don't have these hidden failures kind of living in there, which was a problem we did have there.

But so I did that for a year, which was just, it was a good experience overall. It was, you know, it was a lot of work it was a consulting company so you were working a lot of hours from there i went and worked for a company that did construction data so it was like kind of a tech company but not really in a lot of ways. And I was there for five years and I was their first, I started in marketing, but then I moved over to the, like the, what they call the data team, which was really like a research team. I was their first data scientist. That was also where I became a manager and did some interesting stuff there.

We did a lot of stuff around kind of like we were kind of almost internal consulting the team i was on so anytime people had issues that were like with efficiency or optimization or anything kind of manual stuff we would try to work to make it more automated and things like that including we did some work on just helping move away from a hundred percent like human research for like material like construction material prices and stuff like that which sounds probably a little more glamorous than what the actual results we were dealing with were so i was there for five years from there i went and worked for a subprime auto loan company where i was a senior manager there and that was really that was a big one because it was the first time I was not on the modeling team. I was managing a risk team and it was a place it to determine what the pricing should be. Like, do we accept

this deal? If we don't, what do we counter offer? Cause it was all automated. So I owned all that production code, which funny enough was all written in R, which is not necessarily something I would recommend, but it was an interesting experience. So I did a lot of work there. Like the big projects that I worked on there had to do with like kind i did a lot of work there like the big projects that i worked on there had to do with like kind of optimizing a lot of how we our processes and how we got stuff done i oversaw a complete rewrite of our production code because it was just very interconnected very spaghetti code-ish when we did a whole refactoring and rewrite of it we found like 22 minor bugs and like 10 major bugs including like a test that was giving people better pricing that it was like no

one could figure out no one was using the test no one could remember when like when they stopped using it so they had a couple years of better, like artificially better pricing for no apparent reason. But Matt, I have to ask you, where did that start as far as running this production load? When you came in, how was that being run? And then what did you guys move to? Just from an infrastructure perspective, I think that's always interesting to hear what people inherit.

So right before I came, they had started the process of moving to AWS. So I kind of came in right at the very beginning of like they had just gotten, they had been using this like custom built system for all of the pricing and loans. And I got in there right as they were in-house rewriting everything into AWS, right? So it's all like Lambda functions and, you know, call processes and stuff like that. So by the time I was touching it, it was just, it was all in the cloud in AWS.

During my time there, they actually did probably the only successful on-time transition from on-prem databases to a cloud database. Because I remember my second week, there was someone and they announced

like, okay, so we're doing this project and we're going to migrate it over to Redshift and we're going to be done. I think it was like eight months. And I just thought, yeah, good luck with luck with that but amazingly they actually pulled it off so they even got financed to move over which shocked me to no end yeah that's very impressive so we were doing that i went from there i had an opportunity to go be senior director of my title was senior director of advanced analytics i was really like it was just data science for a public company. And that was interesting because that was really where I went from.

It was in aftercare, aftermarket auto care. And it was really like, this was a company that was like, we're trying to transition to a lot more digital and data. We've got some stuff and we really want to accelerate it. But we honestly, we never got very far. There were so many data issues.

And it was really eye-opening also to just how I had always been able to attach what I was doing to business value. And this was like the farthest I had ever been from it and just how hard it was to try to push us in that direction. So I did that for about a year and a half and then came to Rudderstack. So just always a weird transition to explain to people. Yeah. weird transition to explain to people yeah well just give us the brief just give us the brief explanation because you went from you know a decade as a data practitioner into a product marketing role yeah so there's probably like two or three main things that were going on there one of which was i was just kind of getting burned out. I was, you know, it was I was in a fully remote role. And I wanted that, so that I could have certain things in my

life. And it was like, I looked back, I got about a year and a half and I looked back and I'm like, I sit at my desk in front of a screen on meetings for five hours a day, I have less freedom than when I had to go into an office. And it was just, I was just getting into like just a certain amount of burnout with that too. Another reason was, so I've done kind of like, I've built dashboards, I've built tables. I've kind of done a little like parts of everything

in kind of the data ecosphere, but I don't have like formal, like I've never had the title of a data engineer, even though I've done some of that stuff. And going to work for a company that was like, makes tools for data engineering, I thought would be also kind of a good step to round out some of my experience.

And then finally, like, you know, you showed me, I remember when we were talking before, like I officially even like kind of applied and you were showing me like kind of profiles and stuff. And I was thinking, this is the tool I needed two years ago. It's like, if I can help get this out here, I will help do this.

Yeah, I love it. Man, so many things to talk about. I do want to return a little bit later in the show to your question about increasing distance from business value, right? And you as like working in data and that being really difficult in your last role before you came to Ruddersack.

But I'm going to ask a very leading question to dig into a topic that I'd love to get your insight on. What in your job, I guess actually, I mean, I'm somewhat curious on a personal level because i know you nerd out on software a bunch like i do but in terms of like your role as data practitioner data team leader function owner what's the best software purchase decision you've made in your career like when you look back and you're like that was awesome it made a huge difference like both for the company like it was good for you optically like in your career does anything stick out or any like one tool stick out well so the first thing you gotta realize is that like the first five, six years I had, there was no paid tools.

No one was giving me paid tools. Everything was open source. I mean, I remember trying to get a license for something that was going to cost like 250 per person per year. And they were like, I don't know about that. Like that was hard. How big of a company? Out of curiosity.

People are revenue. Are you there? It's like a hundred million dollar revenue. Yeah, not a startup. Not like a little startup. Yeah. Not a startup. No, not a startup. That really actually,

that's such a good question, John, because that really puts it in perspective. Yeah. I mean, not that you shouldn't be cost-conscious, but like at $100 million in revenue, you know, a couple hundred bucks a year for a seat seems... Yeah, and then there was the internal frustration from my team because it felt like hr rolled out

some new platform every year and we're like how did they have money for this and we gotta we're using like r and python and that's it well actually so stop there like and john you probably have questions here too but like why do you think that was culturally right because i mean of course we talk about it on the show all the time like it's second nature to us that you would like invest in data tooling to make things more efficient but like what was the dynamic of the company where you think that was the case well i think part of it was that they kept investing in different tools that were supposed to do like you know like training and like you know development and stuff like that none of them actually really accomplished much of that i mean it was always it was the classic problem of kind of like one of the things i always tell people is

like there are no tech problems they're all people in process problems and these were like vp of hr coming in and being like well we're going to solve all of our like you know internal development with this platform and it's well, that didn't work. Okay, well, we're going to do, we're going to change our quarterly reviews and our process of checking in because we're going to buy this tool. We're going to tell everyone they have to check in four times a year.

Nobody checks in four times a year, right? Or they put in like, talk to so-and-so, things going well, submit, HR, get off my back. But I think there's this idea of like we can fix this problem if we just get the right tool and it's like it's not a tool problem it's a process problem it's a people problem that yeah that was my experience too Eric and one of the things one of my memories with my first data job is they went and spent over a million dollars on this fairly advanced at the time custom analytic system it was for for a contact center and it was like well this tool

is supposed to do xyz fill in the blanks so then anything outside of that as well we bought a tool to do that and then any explanation about like why the tool didn't work or why you know it's like no we bought a tool to do that use that tool right so i think there's that like kind of a sunk cost fallacy that comes into play where it's like don't we we already have the the fill in the blank of the erp system or we already have salesforce you know i think that's a component of it. And it's hard for data team members to articulate why they need it when there's that perceived overlap with something that you already have

or with, say, an open source tool like Matt was saying. Yeah. I think there's also, if you go back to 2012, 2013, there was a little bit of this narrative of like, oh, look, you can leverage all of this data and it's going to be cheap and efficient to do it and like once you started digging into it you found out like okay well no once we've kind of done analyses after we've hit your database enough time and we need to actually scale stuff out like this is not a no cost thing yeah but there was kind of a narrative of like look, if you can get a data scientist in here,

they'll just work magic. Yeah. Yeah, interesting. Okay, so the first five years, you're using open source tooling, really hard to get budget. Did you get budget at some point? Kind of.

I just shared budget with like the rest of the data org. So it was a little nebulous exactly who had what at times, unless you got a line item at when we were doing our annual budget. So that was your time to, if you wanted something, you could get it. But probably when we go back to like what was the like the best purchase i made it was we were trying to we needed a way to kind of like productionize some machine learning models that we were building and we're anticipating to build and we were looking at the platforms we looked at a couple of them and there were some good ones

and there were some not so good ones but it was going to be so hard to get it in because they weren't vendors. There was going to be a new process. We had a new CTO who was putting in a new process and so I ended up, we were on GCP and I went back to Google. It was kind of like, all right, fine.

I'll listen to what you guys have to say because I was kind of like, all right, fine, I'll listen to what you guys have to say, because I was kind of dismissive of them at first. We ended up using their Vertex AI platform, which was like we basically met with them, made the decision to kind of go with it, and we started

the next day. Within 30 days, we had productionized a major model that was used by one of the major business units at the time so that was probably one of the biggest ones because it's i'm pretty sure it's still being used there too now like it still runs every day and still informs a lot of the decision making for them and why were you dismissive initially like because you had done your own research and you wanted a dedicated MLOps-type platform? Part of that, it was also like,

I felt like every time I talked with them, they were trying to sell me on something. Like, oh, you can use this service. You just get tired of hearing that. We have a weekly check-in, and all you're doing is trying to tell me about these other things or asking me about that type of stuff. I think it was also because

it was really unclear what this was. If you look to Google's documentation at the time, they didn't have good documentation for Vertex. All the stuff they did have was basically here's how you stitch seven GCP systems together

to build your own. And it's like, I'm not doing that. Nope. Hard pass. Yeah, yeah. I mean, there are some great things about GCP platform, but that is sort of a, you know, I remember being part of a startup

that was like building something on top of Google Ads. And it was like, when we really got into the APIs, it was shocking. I was like, wow, there's teams who are building parts of Google Ads that are literally completely separate. And these parts of the same system literally don't even talk to each other. Yeah, there was a lot of stuff. And to their credit, they were building it up really fast. But the problem was, is that none of their kind of documentation and even like the people who were supposed to be helping you, they quickly couldn't keep up with

the speed they were developing the platform. Yeah. Yeah. Okay. So let's dig into why. I'd love your thoughts on why buying data tooling can be difficult, right? And I mean, I think it's worth acknowledging that some companies is probably less difficult, right? Depending on the context, right? If you're a startup in, you know, 2020, with like a really good growth rate, like buying tooling was probably extremely easy, right? But it's getting hard again, I think, for a lot of companies and a lot of data teams, no matter what type of company you work at. But like, what are the dynamics that make that difficult? Yes. I think talking to your point about it gets, it's easier in some instances and harder in others. There is this point at which there's kind of a push to be more kind of

like formal and professionalized. It usually hits, you know, it has to do with either like cost controls or it has to do with like security and making sure that you're doing that, you know, it has to do with either like cost controls or it has to do with like security and making sure that you're doing that. Because, you know, there's a point where you'll hit where suddenly there'll be this corporate thing of like, we don't want to use a lot of SaaS platforms. There's too many security risks with it, stuff like that. So I think that's part of it. You just, there's a lot of kind of, for lack of a better term, like bureaucracy that goes into it too. There's a lot of choices, like there's just a lot of kind of for lack of better term like bureaucracy that goes into it too there's a lot of choices like there's just a lot of checkpoints are they an approved vendor

if they're not okay what's the process we got to go through for that we got to get them to fill out a form you know this whole like set of forms they got to do it's then got to go back to your infosec team you got to do that and it just becomes this thing of like do i want to spend the time to do it right so that becomes one part of it the other part is that like you know i've been at multiple places where either from just a financial point of view or from kind of a a pe investor point of view they started looking at it and one of their metrics was how many licenses do you have per employee? And they had a goal

of having it down to a certain number. Like that was a way that they kind of tracked part of this. So especially when they were getting ready to sell the number of vendors and the number of licenses per employee was a big deal to them. Like across the entire spectrum of software.

So like Salesforce, State, whatever, like the whole thing. Whatever it was. Yeah. And not dollars. Like we're just talking number of licenses regardless of dollar value. What a fascinating metric. I've never even thought about that. Yeah, it was literally...

For good reason. Yeah. I mean, but it was one of those things where you know you're partially cleaning up like hey we have 20 licenses and only five people use this why are we paying 20 right you've got some of that going on there's also just kind of a feeling of like the more vendors we have the more overhead it takes to kind of manage all this stuff, the more kind of risk you're exposing yourself to, because quite frankly, it's hard to keep track

of where it all is. I mean, that's the other thing is you get all of this. When you give autonomy to like every department to buy all of their own technology, you end up in the state where you're like,

well, wait a minute, you know, we're IT or we're security. And we didn't know that people were using this, that, or the other. And so there's this move of fewer vendors and fewer licenses. I think it's also because it's kind of a proxy for like what's kind of your ongoing cost

for technology per person. It's kind of an easy proxy for that too. Yeah, yeah, yeah. I mean, yeah, it it's i don't know i'm not an expert in like you know pe like cost efficiency but you can see how it would serve as a proxy for that right like under utilization is certainly a problem you're paying for you know half the seats and you're not using them i guess the thing that gave me pause is that pricing models are so fragmented across SaaS companies that that can certainly only give you part of the picture. But, and that is super interesting. How, so how much conviction do you

need to have? John and I actually, well, I have a question for you, John, after this, but like, so how much conviction do you need to have in order to go through that process? Because it's not like you have all this extra margin that you can spend navigating all of your procurement process and all those sorts of things. Because to some extent, you have to go convince the company, which creates risk for you. You want to go spend money.

You want to go spend money you want to like allocate more budget like those are risky things for you to do yourself from a career standpoint yeah and especially if it's going to have a long implementation like the longer the or more complicated the implementation the riskier it's going to be i mean i've seen you see several of them and i think you learn quickly in large companies where it's like yeah that guy put himself out there and you know was going to buy whatever thing and it was going to have a five month implementation schedule and we're now on month 12 and we still don't have anything and that's usually when that person takes a job somewhere else before it gets to be too late and so it becomes a thing you kind of internalize like well i'm not doing that

so i think part of it is yeah you talk about you gotta have conviction in it it's got to be something that like you are like we cannot live without this it can't just be that it's going to make your life a little bit better. It's kind of like it has to make your life like 5 to 10x better. Interesting. And people will put up with a lot to not have to go through that or to not kind of put the risk out there for it. Yeah.

When I was running the data science function, we had a 200 grand line item for a graph database. They were like, well, tell us when you think it could be useful and we'll go use it. And I never used it because I was like, I am not paying for this when we're like, there is no chance we will be able to fully utilize this. I'd have to train people on how to use it. We don't have a good use case for it yet. And was like like that money was already allocated and i would not use it interesting like had you already purchased like had you already procured a graph database or it was just sitting out there someone had yeah it was a budget line item like you know 200 grand

graph database and it was like well when you ready, you can go and go procure it. I was like, oh, right. But you don't want to, yeah, you don't want to hit your wagon to that. And then you have to justify 200 grand of database spend. You know, they're like, well, what are we getting out of this? And it's like, oh, well, you know, we've started training people in the query language

and it's totally going to be useful like the next year. Yeah. I'm not doing that. Yeah, yeah, yeah. That's super interesting. John, you have had the CTO role before. And did you control all the tech budget? Yeah, I did.

We had some small things. I think you mentioned like HR systems. Like a lot of We had some small things. I think you mentioned HR systems. A lot of companies have those small systems. But yeah, the vast majority of it, we had centralized into a one tech budget. And what was your experience with this? I mean, was it still hard to buy software? Data infrastructure, sorry.

Software is a really broad term sure it it wasn't it wasn't a huge company so there weren't a lot of like drawn out formal processes for the procurement but each purchase was like me as an investor like putting money into something. I'm like, this better go up in value. If you make enough of those... It's like hiring people, right? Each hire and each procurement, both of these need to produce value.

Or eventually you're done, if that doesn't happen long enough. Probably my riskiest one that turned out really well as we were on a custom e-commerce platform that have been developed over a lot of years i think they had upwards of 80 000 pages out there on google many of them were hand-coded HTML. Lots of effort over like a decade had gone into this site. Lots of legacy SEO. And for various reasons,

security reasons, scalability reasons, etc., I decided to move to Shopify. This was seven years ago-ish. So that was like, I felt good about Shopify, but that was a super high-risk thing because if something had happened where Shopify was like, well, good about Shopify, but that was a super high risk thing. Because if something had happened where Shopify was like, well, I can't do this crucial thing, where maybe Shopify is having scale problems and not as stable as we hoped for.

If something had happened along the way, I mean, that would have been a huge deal. And to be honest, there were definitely some bumps in the road as far as seo with that many you know pages out there that from the legacy app but all of that eventually got worked out but i mean it was a huge deal and i can think of several other examples where like i'm investing and this technology like being good and this working and then continue continually improving especially if it's a newer technology and not getting bought and then killed or stuck for whatever reason and then having to switch the startup risk factor right like yeah right there's like 20 people at this company and if they run out of you know vc cash like so did you ever then have to deal with like you know you get someone on your team

and they're either like i don't know why we can't just buy whatever or they pitch you i think we should do this and you kind of have to be like i hear you but no yeah all the time yeah yeah you know especially i don't want to pick on marketing people but especially marketing that would come up a lot because this is the right this is the right venue to do that this is the right venue i know and i've been involved in marketing groups i led a marketing team for a time so i identify some with marketing for sure but yeah especially in the marketing side there's always a new tool right of the month that somebody was excited about sometimes for good reason yeah but working through that like progression with people like okay and and all it was is like we're working through like what i'm gonna have to go do with our cfo of like okay what are we

going to use it for what's the expected roi do we have tools that already do this are the free tools that can do this you know just working through that progression work people usually ended up in a fine place. I didn't have too many situations where somebody was just so diehard. Like we have to use this email tool or whatever tool and like nothing else will do. I don't remember too many situations like that, but after you got through that kind of progression of justification and value

creation, people, people typically got it. Yeah. Cause I know at least on some of my teams, you get some frustration when they're like, but my life would be easier if we could have whatever. And it would be like, I hear your life could be easier, but, and if I could just flip a switch, that'd be great. But you're talking about a three, four month process that may or may not end up successfully

and that I now have to invest political capital into. Right. Once I burn it, it's gone. Right. John, before the show, we were talking with Matt a little bit about business value. Matt, you even mentioned distance from business value. Would love to know what questions you had around that specifically. Because I think that's a really, in a cost-cutting environment,

which we are in right now, that question is way more, there's way less appetite from the CFO to gloss over things that maybe we're sort maybe we're like sort of drawing the story out a little bit on ROI for this data infrastructure. Yeah. Yeah. I think Matt, one, one piece of that I'd love to hear your perspective on is you've got data teams in this cost cutting environment right now that I think a lot of them are scared, right? Like you mentioned

earlier in our conversation about that kind of like data science savior mentality of like, you've got all this data, like hire a data scientist. They'll, you know, fix all of your problems, create all these predictive models. And now you've got like, you know, various AI things. I think people are past that I mean I've even there's a lot of people out there calling them like recovering data scientists there's like a thing I've seen yeah out there so now we've gotten past that like data science these data scientists are kind of going to save the day now I think because that hasn't materialized and lived up to some of the hype you've got all these systems that were installed and put in place and lots of you know

investment and infrastructure and things like that it's like we have to get business value out of this or we're going to kill some of these programs we're going to you know downsize some of these initiatives so like tell me about your experience with that. And like, what are some tips if you're somebody in that situation? Like what, what can they do to try to, you know, connect the data to business value? And you wouldn't know for everybody's circumstance,

but, you know, speak to some of your, your past circumstances. So I think one thing to remember is there's two ways you're going to generate business value, right? You're going to either cut costs, you're going to like efficiency, something's going to be cheaper to run,

or you're going to generate some type of new revenue, right? Whether it's making a process, an existing process be better, or if you're going to add something, you know, sometimes like on the marketing side, you can sometimes add in new processes that will generate more revenue and things like that i think the biggest one of the biggest things is to always start with that

in mind when you're doing things so like don't talk about what you're going to do in terms of like oh well the data will look like this or you know will look like this or we'll be able to be this much faster as a data team or whatever. It's really got to come down to like, we will reduce customer churn, right? Or we're going to reduce the cost of acquisition or we're going to increase the number of leads or something like that.

Like you've got to handle it in that sense. I think the other thing with that is specifically kind of with the infrastructure side where I've seen people get tied up is where you get kind of this push and pull, right? You've got one group that's like, we want to build stuff that is like, it will handle this one thing that the business is yelling at me about and it'll fix it. But you can't build off of it. It's completely non-scalable.

It doesn't fit in with anything else, right? And that's when you get into like, we've got a thousand different pipelines that all do one thing and it's impossible to manage because there's no organization around it. Then you've got this other group that's kind of like that we need to build like the grand unified data model for everything. And there is a middle ground there.

There is an 80% where it's like, what are the biggest needs that the company has? What are the biggest things? What are the things that are making everybody's life harder? Or like, what are their metrics that they have to obtain, right? Every department has some kind of financial goal they have to obtain, right? Every department has some kind of financial goal they have to obtain. Figuring, just talking to them and saying, what is that? And figuring out where you can tie that into is where it's going to be. And a lot of times you can figure out, hey, if we build a more flexible process for this 20%, it will get us value, right? And it'll be visible and we can

see it and we can talk to it. And it'll leave us open to be able to build off of it afterwards. So I think that's part of it, like one way to think about it. I think the other thing is to remember that busyness is not business value. So it's very easy to get caught up in the idea of like,

well, but they keep asking me to do things and I keep doing them. Yeah. And when they're talking to each other, they're saying, I don't understand what we're getting from this team. I don't see any real value coming out of them. Because they're not really,

like a lot of times they don't know what they want. So they're going to ask you to do stuff that's at least visible, right? Can you pull this? Can you go grab that? Can you fix this? Can you do that? And you got to, it's harder, but you've you gotta do more of that work to figure out like what is it you actually what are the big things you're actually working on what are your initiatives

what are the places that are harder that shouldn't be right where is it hard that it shouldn't be where are you struggling to get this and trying to work into that and unfortunately it's like there's not like a good one size fits all answer or like it's clearly do this but you got to be able to tie those things in i mean you know thinking back to stuff i've you know i worked with projects where it was like we've got two people in finance who are just wrangling these worksheets and they do this for a week every month and we turn that into a 10 second sequel query well there's like definitive value we can draw from that and a savings from a human cost point of view we've all i've also done things like you know there's a lot of models that i've

built where it was like hey look we increased the conversion rate x amount that leads to x amount of dollars things like that but But it's really tying it into those types of ideas. And I would also say not trying to bite off too much. There's a temptation to go big and going big usually means you go home. So I like that.

I see what you're doing. I see what you're trying to do. Good. So one of the things that I always come across, Matt, with the business value question is you're right on, like giving in the weeds with the spreadsheets, with the business users. But how do you do that in a way that's not threatening

where you can get real answers? Because it's a really delicate situation sometimes where people throw up walls. People don't like, they get afraid for their jobs. It's like, are you going to automate my job? Burn across that a lot. Like you need strategies for people that want to create the business value,

but they really want to do it in a way where they're not freaking people out and not losing their job. Yeah, job automation, that's super interesting. Yeah, I think part of that is don't come in and be like, oh, this is so efficient. We could just replace this with a SQL query. Right.

Like walls are going to come right down, right? Right. Like those walls are there. They're not going anywhere at this point. I think a big thing with that is more you've got to kind of come up alongside them. And so it's really kind of getting an understanding of like what their view, what their pain points are.

And that's where I think like, if you talk to places, you know, if you talk to people within a department and you're like, what is difficult that shouldn't be right. You're kind of getting towards like, you know what they're doing, or it's kind of like the

where you know you can even start with where do you need help the other thing i would say is that if you can like places i've worked my teams we built up a reputation for like we were good at getting stuff done so we were typically brought in when things were going bad yeah um which frustrates the team to no end because they're like well you know if they would have brought us in at the beginning this would be really simple and i'm like yeah i get that but we're gonna kind of we're gonna do more than we should it's not gonna be the right way but we're gonna get them through this and they're gonna see how we're helping them and then they're gonna come to us next time because they know that we're here to help them

so it's that it's a people problem. You know, you've got to go talk to people. You've got to build up kind of relational capital with them. And you need to build it up before you're trying to kind of get them to do what you want. I think that's the other big thing. You got to go out there like this idea of I'm going to sit back and the business is just going to come to me with all these kind of data projects like that doesn't really

exist in most places. You got to it you gotta go build relationships you gotta build that trust with them so when they're like hey this is really hard why don't we go talk to matt's team or like john was really good at i you know i heard that john really helped this other group out let's go get them because the other thing you gotta got to realize is when I first managed a team, I noticed this, where it was like, they announced it, we've got this team, they're here to help you, it's a center of excellence, blah, blah, blah. And whole groups would go like, oh, that's great. I'm sure that people could need it. We know how to do our job. Yeah. Yeah. And it was only when things were going sideways that they'd be like, hey, all right, we really need help here and we're willing to take it now.

And those were where you built the relationships moving forward. Yeah. Yeah, that relational capital is really important. effective was trying to be working with analysts for different groups like financial analysts marketing analysts and being an enablement person for them and caring about them helping them yeah helping them develop career-wise like if they wanted to learn a little bit of sequel if they wanted to know some excel tips or tricks the more advanced stuff like most of them especially in analyst type roles were pretty hungry for that type of thing and that seemed to like work really well as far as yeah winning if you analysts are a good kind of door into a lot of departments because they usually feel

overworked they usually don't feel as supported as they should be and a lot of times when you're coming in to help them you're not coming, hey, we're going to automate your job away or something like that. We're saying, we're going to kind of get beside you and either give you kind of like some tools or some knowledge, or we're going to help build something with you that's going to make your life better

and allow you to focus on things that you're more interested in. Yeah, super interesting. Probably a good way to like find, not, I mean, this is a good way to make enemies as well, but find potential people to poach onto your team. If you find someone who's really good. So the opposite is also true. I always wanted my teams to be ones that were so competent that other departments wanted to poach them. Because every time they poach one of your people and out there,

you now have someone who's in that department who knows what's going on and knows your language and what you're capable of. I mean, I had one instance where we put, we embedded someone in another department and it was like my knowledge of what was going on in that department

where they could actually use real help went up like tenfold. Yeah. I'm using really aggressive terms here, but that's like the mole, you know, like you plant a mole. Okay, we're close to the desert here, but Matt, so in your personal life, we nerd out about software all the time and productivity hacks or whatever. What's your favorite piece of software or app or whatever that you just use, that you

use in your personal life? That's a hard one. I mean, I try to not over optimize my personal life too much, to be honest with you. So that's a wise, that's a really wise disposition. Yeah, we get to be a bit too much with that, but I would say probably, and this is going to be honest with you. So that's a wise, that's a really wise disposition. Yeah, we get to be a bit too much with that. But I would say probably and this is going to be

really basic. I have used been using the Notion calendar app. And I have just because I can hook it up to I've got a different Google calendar for like every person in my family so that I can kind of

see everything there. And it's worked better than kind of like the Google calendar app of like, I can see that I can see work. I don't feel like either one is trampling on the other. Interesting. And it has a really nice feature. I have one calendar that's called work block. But if I need to put something on there that I'm like, hey, I'm going to be at the doctor,

I can put it on my personal calendar and it'll automatically block my work calendar for it too. Nice. Oh, wow. And that's like a Notion feature? Notion calendar, yeah. Yeah. Very interesting. You know what's interesting to me about that? Brix is going to get mad at us for going a couple minutes on, but you know what's really interesting to me about that is that experience doesn't map to a lot of the like marketing or the benefits that they market, which is basically that you can see, you know, your work notions like a knowledge repository, right? There's all sorts of documentation or whatever. And bringing that together with a calendar is a very compelling value proposition. But yeah, that's interesting. Maybe that's because they acquired a company who did that well under the hood already.

Yeah, it's basically they kind of rebranded that with, I think, some other internal improvements in there. But it has been helpful just to kind of get everything out there. Because, I mean, otherwise, I am one of those tech people that, like, I have a notebook with me. I am writing within that a lot. I will not. I kind of have this mix of analog and digital. Paper and pen, not like a Jupyter notebook, just to clarify. I don't. Well, as both of you have probably heard me rant on there, I am not a Jupyter

notebook person. You are definitely not. I just had to clarify for the listeners. No, no, paper notebook. And actually, I use a fountain pen that someone who used to work under me gave me when I left the company. Wow. Yeah. I was very surprised when they gave it to me. I got to pay closer attention to your script. That's pretty cool.

All right. Look, we went from buying enterprise software to talking about analog writing with an analog fountain pen, just a great narrative arc. And so with that, I think we're going to wrap the show. Matt, thanks so much. Man, what an insightful episode and great insights from you and John on just the challenge of navigating an organization and acquiring and getting value, I think more importantly, out of software. So thanks for the time. Thanks.

Yeah, thanks, Matt. Great being here. We hope you enjoyed this episode of the Data Stack Show. Be sure to subscribe on your favorite podcast app to get notified about new episodes every week. We'd also love your feedback. You can email me, ericdodds, at eric at datastackshow.com. That's E-R-I-C at datastackshow.com. Thank you.