The Data Stack Show - 206: Reviving Old-School Customer Experiences Through Modern Data Strategies, Featuring Edward Chenard, Seasoned Data Leader and Analytics Officer

Episode Date: September 11, 2024

Highlights from this week’s conversation include:Edward's Background and Journey in Data (0:44)P&L Ownership Discussion (1:15)Challenges in Profit Ownership (3:38)Data Team Dynamics (5:52)Role Clarity Between CFO and CDO (7:31)Nuances of Data Leadership (11:24)Focus on Relevance in Data Work (14:05)Best Buy's Personalization Project (18:39)Building a Data Stack (21:00)Crowd-Driven Algorithms (25:26)Event-Based Personalization (28:12)Corporate Politics and Implementation (31:00)In-Store Experience Innovations (33:16)Impact of Data Science at Best Buy (37:14)The Importance of Data Teams in AI Implementation (39:19)Using AI Conversationally (42:09)Book Recommendations for Data Leaders (44:24)Final Thoughts and Takeaways (47:05)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.

Hi, I'm Eric Dotz. And I'm John Wessel. Welcome to the Data Stack Show. The Data Stack Show is a podcast where we talk about the technical, business, and human challenges involved in data work. Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the show. We're here with Edward Chouinard,

who has had an extremely illustrious career across both large enterprise and startups in data. Edward, I have so many questions. Can't wait to dig in with you. Thanks for joining us. Thank you for having me. All right. Well, can you give us a quick background, sort of, you know, just a brief overview career and what you're doing today in data? Yeah, I've spent most of my career in analytics. I was a market analyst. The last 13 years, I have been building and running data teams that includes engineering,

data science, analytics at Fortune 500 companies and startups. Currently right now, looking for the next big thing to do. Talking to some companies right now, I think it might be getting close on. Awesome. So before the show, Edward, we talked a little bit about P&L ownership for data teams. So I'm really excited to dig on that topic. And then what kind of topics do you want to talk about? Yeah, the building of teams is really important. The change in how like AI is changing the dynamics of the space, as well as, you know, areas of like personalization. It's interesting. It's one of those things that every few years comes back to the surface as an important topic. Awesome. Well, let's dig in. All right,

let's do it. Edward, I'd love to start out by talking about your experience as a data leader in the large enterprise. And one of the things that you've talked a lot about publicly, and I'm sure privately as well, is this concept of P&L ownership by the data leader. And so the first question I have is, you get a ton of pushback on that. Well, actually, before we start, explain that concept to us. Why are you so big on P&L ownership for the data leader? Because to me, I think the data leader is really a business leader in a lot of ways. And just like any business leader, they should own a P&L. They should be looking at profit and losses. They should own the tools and the relationships around those tools.

That then makes them a true business leader. Because what happens is, when I was an analyst, you're spending your days creating the analytics reports and finding the insights needed. But then as you move up into a leadership role, that becomes a very different role. And I think that's something that a lot of people don't understand is the leadership role is not really a role that represents the analysts, the data scientists, the engineers in their day-to-day function. They're the ones helping to expand the market, expand what the team does. So a KPL is really important because you need to show like, hey, this is how our team is impacting the organization. This is how we are engaging customers. This is where our money's going and why we're spending it that way. And if the leader isn't able to articulate that,

then the team really does not look like a serious team to the rest of the organization. Yeah. So from a cost standpoint, nobody's going to fight you as a data leader to own the cost part, right? Of the tooling and infrastructure. but from the profit standpoint i'm curious to dig in on that what are the oh yeah the things on that side because that's harder right yeah that's where the fights always are and this is why i often take a product approach to how i run the teams in terms of we need to build our own stuff and i've done that at all the companies I've worked at because we need to show it's like, Hey, we're actually contributing here. So like in my last company, we actually were like, it was interesting.

They were doing all this custom reports for customers for free. And like we had one customer that said, we want all our reports in cases of wine because they were a wine distributor. I was like, you know how much time that's going to take? Why the hell aren't we charging them for that? Right. Yeah. We could bring in like tens of thousands just on these custom reports every quarter.

Why aren't we doing that? Now all of a sudden it's like, hey, I just paid for my analyst based off these custom reports being made. Right. And then it was like looking into other things like, well, now all of a sudden they want consulting too because they get these reports and, you know, people in logistics often they'll put somebody that doesn't have the experience

and they're learning logistics. So then you give them a report and they're like, I have no idea what I'm looking at. Well, let's set up a consulting business and charge them like, you know, Hey, for this project, we'll charge you 15,000 for it. Now, all of a sudden I've got another revenue stream coming in or it's like, we'll take, you know, there are times where we've had to take products from the product team because they were just stretched thin. So we take the ownership of it. Now we're building that product down and expanding the revenue for it, working with sales. And this is where also the dynamics of the team changes because I'm just being honest here. A lot of

people that work in data and analytics are introverts and they like being in the background because they don't want to deal with people. But I'm like, sorry, you need to deal with people because you need to understand what's not in our database, but what's being communicated to sales and management. So I'd have OKRs saying you have to have X number of customer engagements each quarter. And that did really help because all of a sudden they were realizing, because I'm dealing with a lot of people that are like in their 20s or 30s that don't have a lot of that customer interaction, especially the last four years where we were going remote. And I'm like, hey, I actually feel sorry for these kids who are out of school.

Like when I was their age, I'm in the office. I could watch other people and see what happens when they do things. It works for them. What are they doing zoom call then when that's over they got nothing yeah yeah what pushback do you get on this concept because you've said that there's a ton of fights that develop around this and you know we've seen some of that online what pushback do you get i get told that you don't need to own it to be the leader i'm like well there's a difference between a big c

and a small c if we're talking like cheap data officer small c does not own the pnl big c does because now they're being told like hey what are you doing to drive revenue of this quarter if you're not being asked that question you're you're a small c as a chief data officer and a lot of the people that don't want the stress of that ownership are saying, well, I don't really need to own it. I'm just going to build dashboards, or I'm just going to make sure that my uptime on my data pipelines are good. And I'm saying, well, look, you want to have the respect of being a big chief data officer, then you need to have the responsibility

too. But a lot of people want that responsibility. And that's where the lights really come in. How do you carve out that role in between, say a company has a CFO and a CIO, CTO, how do you carve out that role between those two positions? Like as a chief analytics or chief data officer have you seen it done the cfo's never been a problem for me they tend to be like yeah what you're doing is different yeah yeah the cto cios yeah there there tends to be friction there and with their teams but what i like to point out is like, you know, if they're running software engineering teams, that's a very different mindset. Software engineering is deterministic. It's very much like, hey, we have our process here. We go through it. Data and analytics is

very much trying to understand what is the problem and then the solution. And then, okay, then you start to have some similarities with software engineering after that point. But there's a lot of work that takes place before that. And that problem solving is where I carved the space out because the mindset of your data engineer, data scientists, and analysts is different from the software engineer. And what they do and what they focus on is very different. And even the tools being used. And I think that a lot of places, they place the data team under the CIO, CTO,

because they think, well, they're both working with data, so it's the same. Right. But no, the mindsets are very different. Often the problems they're working on are very different and just the way the teams communicate and get work done is very different that that whole issue is not solved and i don't think it's going to get solved anytime soon until more companies realize really those teams are not the same it's like a sales and marketing is the same because they

talk to customers well you know if you go to any big company, you know, that's not the case. Over the past decade, have you seen any shift in the mindset around that? I mean, data has always been important, but I think especially, you know, just with some of these, you know, developments over the last decade, both in terms of infrastructure, right? So it's cheaper to store a lot of data, which means you have more data to work with, you know, there's, you know, the tooling has changed, but also the mindset around it, right? I think that there's also been, you know, and of course, the cliche is, you know, Gen AI has, you know, highlighted the importance of data, right? When, of course,

it's really been important all along. But in terms of understanding the unique nature of working with data and how to actually turn it into some sort of value for the business, have mindsets across the organization changed at all? Or do you think we're still in a similar place to 10 years ago? It's changed, but actually not in a good way.

Oh, interesting. Okay, I got to hear about this. So if you talk to, you know, like I go to, there's like this data leaders group here where I live in the Twin Cities and you'll meet up like once every three months and you can see like this big difference. And there's also another bigger one called mini analytics, which I used to be a part of. They put on data conferences.

But once I got married, got kids, I couldn't dedicate that kind of time. But I sometimes go to their networking events. And you'll see that the people that have kind of been in the trenches for a number of years, I'd say once you hit that seven-year mark, you start to change your mindset. And you're like yeah there's a lot more nuance here than before and you know i was like that too when i was younger you know i used to think like hey if i follow the process correctly and i do the analysis right then clearly you should listen to me right yeah know, when I brought that up to my dad, when I was starting out, he did that same

laugh too. Took me years to realize why he laughed at me. And what he said was, well, there are times that you're right, but there are other times I would rather trust somebody who has 20 years experience and knows the nuances. And that's just it. It's like if you've been in the trenches and you start to see the nuances, you start to realize, hey, some a lot of the stuff that we think is true is completely wrong. That's what I see when I go to these meetings. It's like the ones that have been in there a long time in the trenches, they're like, yeah, how I thought in 2015 is totally wrong today.

Right. Get into the news. That's why I'm saying like, but when I talk to the younger kids and like my last job, I had some of them critique me saying like, hey, you know, Edward's looking at the strategy and he's looking at like talking to customers. And I just want to know like, hey, how do I like employ this model here and i'm like well is that even the right model you went on google looked it up and decided well i'm gonna pick that one on on the list because i like it and i'm sitting there saying well does this fit our customers needs does this fit the company's needs how who's going to maintain this and who's currently maintaining this you haven't answered any of these questions yet yeah i think the thing is like the the deep thought just it's kind of lacking and i don't blame anybody for that it's just kind of like

our use of technology and like you know you go out there there's tiktok videos youtube shorts this shallow thinking and this ability to think deeply is just diminishing over time. And you can see it in the books out there too. I rarely buy a new book on data and analytics because they're just so shallow. I'm rereading my old books from like a decade plus. I'm like, why am I getting so much more information, better thought in this book that's from 15 vector of like, I'll call it like usefulness. Like, hey, what can we safely ignore and how can we focus on the right things? Because I think people can really get caught up on like, oh, this is right.

This is wrong. This is right. And the question might actually be like, is this even relevant or useful? I've seen that a lot. Yeah. Yeah. Yeah. And that goes back to like, like well who are you really doing this for and unfortunately i've seen like particularly data scientists doing this where yeah they're really padding their resume you know i've gone into companies where it's like

yeah i remember when deep learning was a thing and all of a sudden like you know i was at ch robson also a bunch of data scientists like we got to do deep learning was a thing and all of a sudden like you know i was at ch robs and also a bunch of data science was like we got to do deep learning like why you know the the problems we're solving here do not require that and then you just like well then you like start talking to them they're like yeah well you know i heard some guy who knows somebody who knows this person making seven figures doing deep learning like yeah i'm attached to you have him attached to that? It's just rumors at this point. Yeah. Yeah. I'll never forget really early on in the show. I mean, this is like three years ago, probably. That was probably one of our first handful of guests. Brooks can probably tell us. He can look it up. But it was the CTO of this company called Bookshop,

which is sort of like a, it's an online book retailer and they do a number of different things that are kind of cool. Anyways. Different from Amazon. Different from Amazon. Yes. No, they like give money to independent bookstores for everything. Oh, it's cool. It's really neat. And actually it's like pretty gigantic now, I think. Anyways, we're talking with this guy about their stack and he's like,

I'm going to be really honest. Like it's really boring. Like it's very boring. It's pretty simple. Like that's it. You know, like we have a couple of pieces here and like, you know, like there's one really hard problem.

Episode eight, September 30th, 2020. There we go. Brooks. Thank you very much. Mason Stewart's actually, I guess, great episode. If you want to go back into the archives, 2020. There we go. Brooks, thank you very much. Mason Stewart's actually a great episode if you want to go back into the archives.

But he's like, you know, we have one really challenging long-tail data problem to solve around some sort of classification ID or something, which is data that they got from book classification, some public

classification system that's just a nightmare to deal with, right? Because it's a public data set and whatever those things are. And he's like, that's kind of hard, but we figured it out. And he's like, it's boring, but it does the job extremely well. And that's what the business needs. So anyways, that really resonates. And it just always reminds me of that, I'm not going to do something fancy because we don't need it. Like, we just don't need any fancy things. Yeah. You know, when we built the personalization platform at Best Buy, you know, we were like updating our catalog like every 20 minutes.

And for most products, that's a total waste of time. You know, when you get the new Apple, whatever that comes out. Yeah, that's a total waste of time. When you get the new Apple, whatever that comes out. Yeah. That's what's useful. Or during, you know, like holiday season. What's funny when I went to target their personalization team was, you know, struggling on some stuff and they were like, we're going to go to solid state drive servers and update every six seconds.

I'm like, what the hell for up to the minute, you know, instant results. I was like, well, okay, but your customer works in human time. So they're not going to make a decision in six seconds on, do they buy that beach towel or not? And the cost that you're going to put into that is just not worth the the squeeze so it was just like you know but they wanted that they were so into the tech they were in love with the tech not the customer experience yeah well and i think you just made a great case for the pnl ownership right there because because if you have pnl ownership then that matters to you directly

right like the cost benefit but if it's if you're just part of a group part of a cost center then it's like yeah i don't know this is how much it costs yeah well you know and it was funny uh so i'm very much into telling my teams like everything i know uh when i said when i was at best buying we're going over the roadmap and of course I tell the team first, one of the developers, he just raises his hand. He goes, what's ROI and why do you keep mentioning it? Hey, I have an MBA. So yeah, I'm thinking like, well, everybody knows what ROI is and why it's important, right?

Cause you know, it was drilled into me at school, but here I am dealing with somebody who maybe he took a bit you know general business class in school and that's it and so explaining it to him and he's like thanks now i actually know why we're doing what we're doing here oh yeah that's huge that's awesome can we dig into the best buy project a little bit so that was uh you know, before the show, you're talking about how there was very little in the way of, you know, sort of true personalization. And then it became an extremely large source of revenue. But just take us to the beginning. When you say personalization, I mean, that's such a hot topic. And, you know, marketing, you mentioned that it

comes up every couple of years of like, okay, personalization is like the new thing. Right. And it's like, well, no, like it's been around, you know, since before computers.

Yeah. Since the beginning of time, you know, since, since at a cafe, somebody wrote your name on a cup. Yeah. It was,

I'd always tell the team, I was like, we're actually bringing what's old back. Like we're trying to recreate when the shop owner knew their customers, knew Mrs. Smith comes in on Tuesday and always makes herself a chocolate. That's what we were recreating. So personalization to me, I actually had somewhere I've got this presentation, like 18 ways to personalize.

But for most companies, it's recommendation engines, which isn't true personalization, the email marketing stuff. And then what we were really driving towards was we actually had three levels of personalization, which was crowd-driven, persona-driven, and true one-to-one personalization. When I started at Best Buy, it's 2011. And they were using rich relevance. And basically what happens with a lot of the algorithms is they got what we call flatlining. You'll see this period of revenue growth

and then all of a sudden it just flat, flat. So Best Buy had hit that and they were like, hey, rich relevance relevance help us get more and rich relevance like hey our stuff's all proprietary so they were like hey okay we'll just do our own stuff then right yep so that's how i got hired on to run that now when i started it was literally just a team of me oh you worked i worked in a group called Emerging Technologies, separate from IT. That matters down the road. When I got there, IT's like, what are you building? I was like, well, I'm going to go out, research what everyone's doing, and come up with my own approach to how we should do this.

It's best for best buy. So I was able to reverse engineers other companies at the time the guy who ran amazon's personalization platform had a really big ego i found a forum where he liked to hang out i intentionally started saying stuff wrong so he would correct me he literally like told me how personalization is run at Amazon. That's amazing. That is really funny. And so what I did was I was looking around and I realized, hey, we need some kind of big data thing.

Play with MongoDB, React, RabbitMQ, and then settled on Hadoop. Because that was the only thing that did not break when we threw our test data set, which was throw the holiday testing data set at it and see if it choked. That was the only thing that didn't. So we built a really simple stack. Now at this point, I got on a business analyst and a database engineer to help me build this. And we went over to the data center in you know across the street and just on commodity hardware built it out ourselves now there wasn't like you know any coursera courses or stuff that nobody was out here you know we couldn't even get

like you know hortonworks or cloudera reps to come out to visit us. It was like nothing. I basically bought a book on Amazon by this professor at Stanford on like building big data sets. And that's what I used to build it. Nice. Wow. We got it working. And I was like, hey, I'm going to do all open source.

That was sacrilege at Best Buy at the time. You don't do open source. But I said, hey, I've got stuff that works on commodity hardware. The data center's about to get rid of all these servers. Why don't you just give me the servers so you don't have to pull them out? Data center guys were like, yeah, we're all good with that. And then IT was like, no, you can't do that. You can't use open source. So they brought in SAP Teradata to bid on it. It was actually kind of a good thing for me because these guys are at 20, 30 million, 18, 20 months

just to build the big data structure. And I just came back and said, give me one quarter. We'll be ready for production. Wow. And I said, give me half of what We'll be ready for production. Wow. And I said, give me half of what they're asking for. So they did. And yeah, we were ready in 60 days, had our first algorithms out there, you know, by the end of that quarter. And as we started and gave me the most restricted view of how I can credit a sale to our products In the session, you had to have bought the product.

So if you came back like two weeks later and bought it. Oh, wow. That's really rigid. Yeah. And hired some data scientists. And again, well, now we're into 2012 and I'm like, I have no idea what data science is. You have to help me figure that out. And we were doing that now.

It's the year of the three CEOs at Best Buy. So in 2012, they had three CEOs and that's important because they basically were ignoring us. I was like, Hey, everyone's worried about what the next CEO is going to be interested in. I'm just going to build this. And yeah, I hired my own team because IT wouldn't help me. So I basically hired a bunch of contractors to come in to, to build it for me. And we were just cranking stuff out every two weeks, which was unheard of at Best Buy at the time. They were, you know, a sprint for most teams was four to six weeks. We're doing it too. And we got the process down where we could build, test, launch an algorithm, a machine learning algorithm in one month wow well and then

we just start put them out there and i go to the call center and i see like hey they could use these algorithms too i go to best buy for business geek squad even the distribution part of the business we're just spreading our algorithms everywhere yeah and we're collecting all this data we actually got to the point, 2013, we collected more data than the rest of Best Buy combined. Really? Yeah. Wow. And what, Edward, dig in a little bit to like, when you say you deploy these algorithms and

you talked about those three different types of personalization, what was the algorithm doing? And I think it was you know crowd we had crown persona and one-to-one right so to get rid of the cold start problem we were using the crowd-driven one so when you go online you know people who bought this also bought that type of algorithm what that would do is give us a pattern so back then we could tie it to a device and we're saying hey i don't even know who this person is but i've created persona profiles based off of different behavioral patterns i've seen and i'll match them in and start recommending products based around the persona so we use the corporate persona because, you know, I talked to the teams that built that

in the UX team and cut in CX teams. And I was like, they did a great job. They'd go out, follow people, sit down with them. I mean, it's kind of, kind of weird. They're sitting there, like having like dinner with these people in their homes. Not the type of thing for me, but they did the real work. Yeah. They did the real work that's yeah so i was like hey i'm gonna use these as our personas because they they did the good they did great work and you know then they would bring us in because they had what we called we nicknamed it the interrogation room best buy has a as one of these rooms like you know you're seeing like the cop shows the one-way mirror thing somebody asking questions it was nicknamed the interrogation room. You could sit there and

watch how people would interact with the algorithms and see what their responses are. It was great. So we would figure out the customer journey and see like, hey, where are the points where they're dropping out and what algorithms might help them to stay in and get to that purchasing point and make the purchasing decision? Because when it comes to electronics, your average person looks at like 10 different websites before they make a purchase. Sure. Our own research was like, you could put like Bob's Electronics on the Best Buy. Nobody cared. What they were interested in was the product they were buying.

Now, we were also like, hey, how do we take it from a commodity purchase to an experience? So that's why the platform was called the Experience Platform, because we were trying to make it so that the act of purchasing was an experience in and of itself and a positive. So that when we do the follow-up remarketing, trying to get you to buy the accessories, you're going to be like, hey, it was a good experience buying the main product. I'm going to go buy the accessories there too. And then on the one-to-one, we were trying to get to that, like, hey, who are you buying for? You know, if it's not for yourself, what events are happening? Like, if you've got students in your life, hey, August, we're hitting you up with back school stuff, all that sort of thing.

Yeah, yeah, yeah. The Christmas thing, anniversaries. Making it a true, we're looking at your past purchases, we're looking at your current searches, and we're trying to figure out your life events to see like hey what's going to be hitting you because most people they except for appliances their purchasings tend to be pretty much the same across all product lines appliances are different because hey if your refrigerator goes you're just going in and say hey i need a refrigerator. Interesting. Or if you're remodeling, you might be looking at it for months before you make a decision. Interesting. And so, but if you think about something like

audio equipment or, you know, you know, a TV or something like that, those behaviors, your purchasing behavior and research behavior tend to be the same. Yep. And then you have to look at, then you have to look at like little nuanced things too

so this was a fun one the analytics team was struggling because new hampshire which has the lowest population when you look at main new hampshire massachusetts during black friday and cyber monday they would have the most sales in the store, but only on the borders. So you look at Portsmouth next to Maine and Nashua next to Massachusetts. Those were the highest performing stores. And, you know, they're scratching their heads. I'm sitting there going, well, you know, I grew up a few years in New Hampshire as well.

They have no sales tax. Yeah, that's what I grew up a few years in New Hampshire as well. They have no sales tax. Yeah, that's what I was going to guess. I'm like, everybody's crossing the border because they just save themselves 10% just by crossing the border. But we were sending them emails. Hey, go back to the Nashua Portsmouth store for like if you bought a DSLR for the training classes, like take their home address and then map it out to the nearest. Yeah. Now it's like, hey, well, it's closer.

So it's more convenient. Or like when we were looking at locations. So somebody who's in South North Dakota, they'll drive two hours to go to a store because they kind of have to. Yeah. Right. Atlanta, Georgia. they won't drive five miles because of the traffic yeah so so like for the call centers if you're talking to somebody in north dakota you say well hey go to bismarck go to fargo if you'd like to pick that up today

for somebody who's in atlanta if it's more than five miles, say, Hey, do you want us to ship that to your house? Yeah. Interesting. So that's where the organization would, would come in. You're using that, that, that location in like the environment in which they live in, like what your recommendation is. Was it hard to, so there's the algorithm piece and there's getting, piece, and it's getting those things right, where it's like, okay, you're solving this problem around those cases that you just talked about to create some sort of great experience for someone.

But then you have to put that data to work in that, okay, there's probably a website component to it because it's in session. There's probably some sort of messaging component around email, but it sounds like Best Buy was a really interesting environment at the time. Was it hard to go to those teams and say like, hey, you need to actually overhaul your email campaigns to use the outputs from this algorithm or to work with the like user, you know, website team or user experience team? Yeah. So that's where like all the corporate politics comes into play because, you know, again, like I said, a year of the three CEOs,

everyone's like buying and jockeying for position. I'm really an upstart. So there are established teams like the dot-com team that runs the main website i mean this is a team that takes up like two or three floors and sure yeah yeah and here i've got like a small section on on one floor that like you know if you blink when you walk past us you totally miss us so yeah it's but it's learning like what motivates them? So like when emerging technologies, we, you know, me and a couple of others, we actually migrated on and became the omni-channel team because we went from just purely digital to digital and physical. And they're, you know, like with the stores. Now, if you talk to the stores, they've got two, three year roadmaps. So they're like oh great

idea yeah we'll implement that you know yeah what i would do is like hey i need to test something out here i go to the store manager's like hey i've got something that can help you make more money than the store manager in town over they're like yeah i'm listening yeah that's great so what would be like a implementation for in a store because we all think i think of like the dot com and the website obvious implementations but what would be a store well the like the vending machines you probably saw like airports that would oh yeah okay well the one thing... What, the stock in the vending machine? Yeah, we're deploying the stuff, but the actual stores themselves, the thing that was really interesting

was the tablet experiment. So a lot of people, again, talking to the customer insights team and so on, they give us feedback like people don't trust the high school college kid on this ten thousand dollar electronics they're about to buy it's like well hey let me give them a tablet that gives them

like the reviews the specs you know everything you'd want to look up so then the employee can be like hey don't just listen to me here Here's all the information. Customers loved it. The employees loved it. The CFO hated it. Really? Why? She just thought people were going to steal the tablets.

It's like, oh, okay. They're formatted for us and it's not like you just walk out with it and just start using it because, you know, it was stripped down to just the Best Buy application. Yeah. And so that was one of those things where I was like, great idea. But yeah, it died because somebody higher up had an opinion and she was just too stubborn to change her mind. Wow. Wow.

Sure. Like the fact that the customers and the employees loved it. The other one was we had this big, like, so when Best Buy had the small format stores that you would see in malls, we put this, this kind of kiosk machine in there. It was basically like a big touchscreen TV. I'd go to the store managers. They'd be like, don't get rid of this. We like this.

This helps us a lot. Again, they were like, finance was like, nah, too expensive. So I do think that's one of the reasons why the small format stores died is because they were putting costs ahead of the experience. And my logic was, if you create the right experience, you'll create enough revenue to cover the cost as long as you're managing your cost. But don't sacrifice the experience just for cost savings so so in that case what was the experience then you said there's like a touch screen and like customers would interact so because it's a small format store it didn't have a lot of products so what they would do the employees would go to the touch screen work with the customer figure out exactly what they want, place the order, and then they could have a shot.

Super interesting. There were minor things. We also did the in-store pickup and curbside delivery. Sears and Kroger's were the only ones doing that before that. If you went to a Kroger, it was like two hours before sears and kroger's were like the only ones doing that before that but they things like like if you went to like a kroger it was like two hours before your food would be already right wait a minute what's this experience like for most people it's like carry out pizza yeah yeah right let's order some pizza and go pick it up how long does it take to be ready about 20 minutes so why can't it be

ready in 20 minutes for us to yeah out target when i went to target they were just like doing like a brain dump out of me to like how to do that there too i'm like they hadn't gotten that in place before lockdowns that would have been a very different experience for target oh target's really good i think in my opinion they're one of the best at that experience yeah yeah and they basically just learned that by you know that's how i ended up at target they basically like threw a number out i couldn't say no to there you go and to close out with the best buy story is amazing but of course we have to talk about ai before the show's over you know we would have failed everyone

but what how do what's the conclusion of the best buy story so you know obviously some things worked some things you know died but what was the impact on the business oh the i mean that the whole data science data engineering personalization one when joely came in who was the guy credited with the turnaround you know i get a i i get asked to like present what we're working on and they're just like you know the executive team's like is this live i said yeah everything i'm showing you is live you're like hey you're the first person to show us something that's live wow so the whole idea of two-week sprints took hold. Open source, lots of other teams started doing that.

The idea of focusing on the customer was my takeaway, not most people's takeaway. But to me, I was like, technology for the sake of technology is a waste of time. And I saw many fail because they did that. But if you focus on the customer and their customer experience, that's what really drives it. And then the technology becomes the easy part in that because people are not easy. I mean, most people make an emotional decision and then look for a rational excuse for it.

Yeah. But the end result was that whole group was bringing in over a billion dollars a year when I left. Wow. I mean, from going from almost nothing and one person. Incredible. No wonder. No wonder Target wanted you. Well, it's not just the technology.

It was also the way you manage things. Like I said, I use a very emergent strategy approach i'm very proud that a number of people who work for me have gone on become vps c-suite managing directors yeah because they were in an environment where i allowed them to think critically solve problems and get polished in terms of how they present themselves. Yep. Love it. All right. We have to fit AI in. All right, John, what AI question do you have for Edward? So I think we talked about this before the show. We talked about software development, the history there, very deterministic. And now and then we started talking about data and how data and data teams

are less deterministic, right? They're dealing with fuzzier problems and fuzzier outcomes. And then AI comes in and you've got a bunch of like historical deterministic technology teams being said, hey, implement this AI this ai thing right and i think it's safe to say it's often not going well so my question to you is a and and sure there's probably going to be some changes on you know on the traditional you know deterministic side anyways and and like traditional it my question to you is it's our data teams may be better suited for some of these ai implementation ai problem solving because they're used to the less deterministic working style? Yeah, I do think so.

For me, data teams should be problem solvers first and foremost. most. Whereas I know a lot of software engineers will say they are too, but the way I see the team's work are very different. A good example is, so when I was at Best Buy, we were asked to be part of the beta test for a new version of Power BI. And they brought in IT, they brought in my team. We're sitting there asking questions left and right and it's just like next step okay next step i'm like no aren't you guys asking questions that to me was the difference in the mindset right there it's for us when we're given a problem it's hey is this even the right problem we're solving was this frame that's where we start out with. And, you know, Scrum process, Kanban, they're great at a certain point.

But in the beginning, there's really a big difference between software engineering and data and analytics. And when it comes to the data engineering side, you see some people like, oh, well, data engineers should really be in IT. And it's like, well, it depends on the org. But if you're solving problems on the analytics side, you need those data engineers sitting day to day with your analysts and your data scientists. And when it comes to AI, like I mentioned before, I do not use open AI or Copilot or Gemini. I experiment with Claude, but the way I use Claude is really conversational. Whereas I find a lot of people, when they start using AI, they're just like, do this, now do that. We have an ongoing conversation on various threads

because, to me, that's the best way to use it. And I think people that work in data and analytics, they're used to that asking questions, wanting to find the answer, having that conversation, going back and forth to find the answer. So that mindset I think works quite well if you use AI correctly.

Yeah, yeah. I think that makes sense. But since AI is becoming so prevalent, wouldn't you think that maybe that deterministic mindset at least to an extreme is not going to work for anyone like long term yeah but at the same time it's so entrenched it's going to take a while to go away yeah and there's and there will be years and years of like work to do and things for people to do of systems that are deterministic and need people to work on them. And so that's going away overnight.

And I think there are fields, you know, like you want something deterministic when it comes to like your finance management. Yeah, definitely. You know, you're in healthcare.

So there, there's always going to be that space. And like, you know, when I hear people saying, Oh, we're going to use like, you know, AI to be like a replacement for nurses.

I'm like, yeah, I hope I never end up in that hospital. Yeah. Yeah. I mean, I use it and I'm just like, if I use it for things that I'm familiar with, I just want it to help me speed things up. Yeah. So that I understand what the output is. But if it's something I'm not familiar with, how do I know to call it out when it's wrong?

That's what I see people doing. Or they just get lazy and they don't really look at the responses. I've used it when it came out. I was using it for writing cover letters and, hey, make my resume adapted to this job. And I ended up with PhDs from Stanford working at like facebook or google which i never have wow i'm like no i don't want you to make stuff up i want you to

use the key words that's in the job description right right yeah super interesting well i think we're at the buzzer here but edward, one more question for you. You mentioned that you've been rereading some of your data books, you know, from a decade plus, that are a decade plus old. Do you have a couple book recommendations for the audience on the ones that, you know know you return to most often i can tell you that what i'm reading right rereading again the connected company very interesting one on like it's by dave gray i like the book because he uses a lot of different concepts that so i believe i read this back in 2012 or 13 concepts i'm like they're still relevant today yeah let's see here another one i liked i actually met this guy about the same time, The Intention Economy by Doc Searles. So I read this one over the summer. Again, if you're looking at personalization, I think it's a great book. There's a companion book that's more technical

called The Live Web, written by a professor down at the University of Utah.'s it's a great book for like hey how do we actually create an economy that's much more driven by the consumer and i think it's actually very relevant today whereas you know we see companies that are really shareholder driven where you know they've got they'd record profits but it wasn't good enough so they lay a bunch of people off and i'm like that's not sustainable, guys. Right. You know, and I've been having these conversations with a lot of people. It's like, hey, the younger generation in their 20s, they don't want to be doing like what we're doing 20 years from now.

They want a work environment that's much more for them and much more, you know know satisfying in terms of giving them a rich life it's not all about like hey i'm just here so some shareholder can make money yeah i think it's a great book talking about like hey how could you do that actually i've been talking to a founder uh guy we actually went to the same school together thunder Thunderbird, and he has started a company, a company called Hire Humans, where he's putting a lot of those concepts into place about how do you improve the hiring process. So that's something we actually were talking this morning. Those are the kind of companies I'm looking forward to see becoming out there and driving

things. Because things change and i'm looking at those things like hey how do we adapt to you know the younger generation and what they're looking for not even the younger generation i mean i i want to work the boat i'd like to go work at a ski resort on my computer i don't want to have to sit in the office all the time so i think it's just new mindsets coming in. So I'm looking, you know, those books I look for. There's other books. Let's see here.

I don't have it with me, but some of the books on just like, how do you engage with customers in different ways? How do you look at the perceptions people have about what it is you're creating? So like I said, that whole product mindset I do bring to the table. So looking at everything, it's like, going back to this conversation on hire humans, it's like, hey, the job is a product. The person's a product in some way. So how do you make that work better?

Ben and I were just talking about, it's like, well, most people don't know how to actually hire people and interview them. That's part of the process of what's broken so that's what i'm how i spend my time what i look at what i've just found is like these older books just give me the details and information i need more than the stuff that's coming out today love it well we'll try to put those in the show notes for this show and in the upcoming newsletter. Edward, this has been an amazing conversation. What an incredible journey that you've had and

excited to see where you land next. Once you get in there and start causing trouble like you did at all these other companies, we'd love to have you back on and hear about it. Sounds good. Thank you for having me. The Data Stack Show is brought to you by Rudderstack, the warehouse-native customer data platform. Rudderstack is purpose-built

to help data teams turn customer data into competitive advantage. Learn more at ruddersack.com.