The Data Stack Show - Re-Air: Data Teams at the Crossroads: Proving Value in a Changing Business Landscape with Ben Rogojan

Episode Date: February 11, 2026

This episode is a re-air of one of our most popular conversations, featuring insights worth revisiting. Thank you for being part of the Data Stack community. Stay up to date with the latest episodes at datastackshow.com.This week on The Data Stack Show, John welcomes back repeat-guest Ben Rogojan, Owner and Data Consultant of Seattle Data Guy. John and Ben discuss the evolving relationship between data teams and businesses, highlighting the challenges of proving value in a cost-conscious environment. Ben explores the impact of technological advancements, the rise of AI, and the critical skills data professionals need to succeed. Key insights include the importance of understanding business context, being proactive, and focusing on delivering tangible outcomes rather than just producing dashboards. Ben also emphasizes the need for data teams to communicate value effectively, show rather than tell, and be willing to take calculated risks. The conversation provides practical advice for data professionals looking to advance their careers, with a focus on developing business skills, understanding organizational needs, creating meaningful impact beyond technical expertise, and so much more. Technical Freelancer Academy & Consulting Community (1:21)Evolution of Data Teams and Technology (2:52)Data Team Growth and Output vs. Outcome (4:47)Internal Optimization vs. Client-Facing Data Work (7:23)Audience, Delivery Mechanisms, and Actionability (12:40)Proving ROI and Prioritizing Work (15:27)Practical Tips for Data Team-Business Alignment (18:31)Dealing with Vanity and Security Blanket Metrics (23:39)AI’s Impact on Data Workflows (27:07)BI Tools, AI Integration, and Dashboards (32:25)Top Skills for Data Professionals (37:27)Career Growth: Technical, Communication, and Business Skills (42:02)Show, Don’t Tell: Prototyping and Feedback (44:37)Taking Initiative and Risk in Data Roles (50:21)Parting Advice and Closing Thoughts (51:16)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hey, everyone. Before we dive in, we wanted to take a moment to thank you for listening and being part of our community. Today, we're revisiting one of our most popular episodes in the archives, a conversation full of insights worth hearing again. We hope you enjoy it and remember you can stay up to date with the latest content and subscribe to the show at datastack show. Hi, I'm Eric Dodds. And I'm John Wessel. Welcome to The Datastack Show. The Datastack 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. Before we dig into today's episode, we want to give a huge thanks to our presenting sponsor, Ruttersack.

They give us the equipment and time to do this show week in, week out, and provide you the valuable content. RudderSack provides customer data infrastructure and is used by the world's most innovative companies to collect, transform, and deliver their event data wherever it's needed, all in RutterSack. real time. You can learn more at rudderstack.com. All right. Welcome back to the Datastack show. We have Ben Rogajon here with us for the third time, I believe. So welcome back, Ben. Hey, yeah. Thank you. Thank you so much. What'd you been up to? I mean, you know, some of the same, some different, you know, continuing consulting, helping companies, sell up their data stacks, untangle messes, and also just trying to build some communities for both data leaders than

consultants as well. Yeah. So I got it to you up for this. Tell us more about technical freelancer academy. I think you've started that since we last talk. So I'll give you a minute. Let's talk about that for a minute. And then we got a lot of topics to cover. Oh, yeah, for sure. No, I just kept having people ask me about consulting.

And I was like, all right, let me create a space where people can go. One, I can put content that's specifically around consulting, people can learn from it. But also, you know, for people who maybe need some accountability or just want to see what other people are doing. It's a space can go, ask questions. people like to do like weekly updates where they're like, hey, here's things I did well, here's things I did maybe poorly. Here's some things I'm learning about. And so it adds out accountability.

It has that information. So that's what I'm really trying to do. It's just create a space that, again, if you're deciding go consulting, you kind of see what it's like or if you haven't made the jump yet, you can kind of see what people's problems are. You know, there's no glitz and glamour there. No one's saying like, I'm making 10 years and knowledge a year. People are like, hey, sometimes this is art.

And so that's really the goal of the space. Yeah, it's a little bit different than a lot of the, the ones that I've seen where people, you know, you've got the screenshots of the Shopify store or the marketing, you know, campaign with the like, however many million dollars. It's a little bit different space than that. But I can attest to, you know, great community. I've actually met some people through the community. So it's a good place to hang out. It's been cool to see different people grow. Right. I think we just saw, what is it, Jeff and you both kind of go back into full time. But because sort of they were consulting. So it's kind of interesting to see that.

Yeah. Bet, switch too is like, oh, we're consulting that we get pulled into some sort of full time role because we were consulting. And so it's interesting watching that email. Yeah. Awesome. So one of the topics I want to dig into, and I want to start with like where we've been, and that's like the, this data business impact, data leadership, the, let's just back up actually.

Let's call it data people's relationship to the business. I'd say that I'd say it's been maybe a little bit of a rocky couple years. But why don't we rewind all the way to like 20, 19, the glory day, you know, 2018, 2019, 2020. Rewind where we started because I don't want to assume that all of our listeners, you know, we're in the space even then because it has been, you know, over five years since then. So where did we start for people that maybe weren't in the space yet? Yeah, I mean, I'm asking years.

I feel like the business data relationship has been rocky probably. I mean, decades. You know, since the first person asked a question to an iTunes team and was like, hey, can we answer this question? And they're like, oh, it's kind of hard to like, why? We write the data, like, why is this so hard? I think, you know, going back to like this far back is, we're not far, going not that far back to 2018. Yeah.

We just come off of like the Hadoop high. I always use this example where he built up Google trends on Hadoop. 2015 was peak addupe. It's literally like peak and then it just falls off from there. Right. And so that means we kind of come off of like Teradata and Nita and people are like, this is expensive. Let's find cheaper solutions. We went to Hadoop.

We're like, that's hard. and we then got into this like Snowflake Cloud Data Warehouse kind of world. Obviously, some people are still on PostParis. A lot of people are still on-prem. I had a lot of projects recently that have been SQL server just Snowflake. So plenty of on-prems still. But I think a lot of the tooling that started coming out in 2018, 20-20, removed a lot of the technical friction, one, that kept people from maybe going into data analytics as much.

Like, big data became less of an issue for a lot of companies, because most companies don't have that much data. And so the tooling that kind of existed solved a lot of those problems. And then you solved a lot of other technical versions, right? Like you had tools like 5Train that came out and were like, hey, what if you didn't have to write as much code and just extracted data? And so like all this stuff happened and it became cheaper, right? Like something else became cheaper to like do this.

Instead of signing a $10 million contract for terror data, hey, let's use Snowflake. Let's, you know, maybe spend $10,000 or $20,000. I'm sure now for a lot of people's more, but, you know, especially back then, you're just getting started. It's not that expensive. And so suddenly people are kind of starting to build up this data infrastructure that's trying to spend a little money on it. And they're like, okay, we're getting this data warehouse.

And I think at some point where money was cheap, so like teams started building up. I remember like you talk to people like startups that were relatively small and we have a data team that's like 20 to 50 people. And you're like, what do you do? Like what are you going to do? Like even when I was at Facebook and this is something that's more post hoc, you're like, yeah, our data team for what we did were like 30 people. But if you look at what, like, do we need 30 people? Right. Maybe not. And so, like, teams started to explode.

And really, I think for a lot of teams, they were building a lot of stuff. And there was a lot of output. But there wasn't a lot of outcome, which didn't, like, you know, as per most cases, didn't really become obvious. Maybe until money started to dry out, then suddenly. Right. Oh. Okay.

What do you actually do? Like, before starting to chat, like, what do you actually, like, you're building these pipelines. You're building all these models. but do we use these dashboards? Are we getting any outcomes? I think a lot of things started to get questioned. And I don't think that happened before. Again, like, you're going through the motions and you're like,

okay, we're building the things we're supposed to build, you know, based on the article we've read, right? I think Lakehouse and the Dalian architecture was like 2020, 2021. Everyone's like, okay, let's just look, we're building that. And we got so caught up in the motions that I think a lot of companies forgot there's a reason we're doing all this. I think that's kind of where, like, if you want to see, like, the trajectory of all of this like starting somewhere in 2018 and coming until now is why we're now here and we're like,

okay, what do we actually need? Why are Dave, you know, I think you're seeing data teams start to shrink down and as other things are going kind of up in other ways in terms of costs for tooling. Yeah, I think one of the things that's easy for me to forget and I actually think about this a lot when I'm talking to clients is when you have all of your data efforts essentially focused for internal improvement, that is pretty expendable, right? It was like, like, think about like lean manufacturing and like six big, six sigma, right? That's like a really big popular. Like, oh, we're going to have all these like lean initiatives and six sigma initiatives.

And some companies still do that. But because it's like process optimization focused, that's an easy thing when like maybe money's a little tighter. Like, we're efficient enough. We're just going to cut that for now and just roll with what we have. And I'm afraid that some data teams like can kind of get caught up into that, especially if they're really just focused on like internal metrics and like kind of driving driving cost out for example. I'm thinking like maybe a manufacturing, transportation, like industries like that.

Whereas ironically enough, IT teams or engineers that are working on apps, if customers use the app, like there's going to be somebody that's they might reduce a team size, but like there's a lot more like business perception of need. If like, oh, our customers use this. Like we can't like get rid of that team. So that that's the part where and now there and there's a lot of businesses that like have a lot of that I've actually worked with that have a lot of like client facing data stuff. And those I've seen have not had as drastic of a swing because they're like, well, this is literally like our clients trust us to help them optimize their business. Like we need these data people so that we can make sure that we're delivering value for our customers.

That's that for me, at least my perception has been more stable. but I feel like the internal optimization stuff is the part that's a little bit been more volatile. Yeah. Yeah. I think it's looking at it. And it goes like, before we started this, I was talking about an article that I wrote recently talked about like why data teams don't have a seat of the table.

And one thing I think I really wanted to kind of hammer home there was the fact that, you know, if a business can afford a data team, they're already doing well, right? Like you're a data team of five people all costing $200,000. And again, maybe you're a data team. Maybe you're only getting paid $150,000, but there's other costs that are associated with their health care, et cetera. So you just round it probably up to $200. So it's a million dollars there. You're spending another $300,000 on tools, whatever it is.

You know, it ends up being $1.3, $1.5. That's money that the business is spending somewhere, and they're getting it from somewhere. Right. Either they're funded, well, or they're successful business that are spinning off enough cap to invest in you. And it's like, that means they could just take that money as profit or they could invest it somewhere else. and instead they're investing in you. So the business is successful. It's kind of kind of the bottom.

Right. You can be an amplifier as a way I've used as a data team, at least if you're doing that like internal metrics approach. But you're not really added to the business in that world, right? Like you're not like the business is already valued at one. You can maybe, you know, one point something it. Right. If you add some efficiencies or something on that side.

Yeah. Okay. Yeah. I mean, one of the things I've always thought about in that context that is the, there's a finite amount of money. And let's say they can invest it four different ways. or three different ways. One in the data team,

two in scaling paid media, three and hiring more salespeople, four, and some other thing. Like, not that they're mutually exclusive, but if the answer is like, we're going to pick a few of those things,

paid media, there's a perception. I'm not going to say reality. There's a perception of clear ROI, right? Like if you use, especially if you're using like Google or Facebook, there's a perception of like, we have this clear like measurement of ROI.

We actually have a three episode series on attribution like back in the data stack show in our archives if you want to look at some old episodes we went really deep on this it's it's not always very accurate is the tl d are on that but there's a perception there salespeople if you have tight metrics you can clear oh hire a new one like here's what they should be able to hit you know that can be pretty tight and then but a data team like the ROI is hard like it always has been and I think even if the value probably actually is there and maybe even is in reality pretty much equivalent to one of those other options, it's harder to prove.

And if you don't have a data leader that's good at essentially selling what they're doing with the value is, then you, and there's limited budget, then it's probably going to go to sales or paid me another like cleaner, clearer, clearer decision. That's almost why it comes out of hotel people. If you want to work for a data team where you're likely not going to get fired or if you want to like, in this is probably also you see a lot of consultants aim at these spaces, marketing and sell them's a great place.

100%. Under if you're a data team or if you're a consultant to target, it's like, there will be money there. There might not be money on the data side because, again, it's hard to prove that ROI. But like salespeople want to know how they're tracking you better, how it'll be better, go to market motions, all this stuff. Like there's always value there because if you can tie it to, hey, we were able to land contracts, people are going to do more of those activities.

And then going back to the efficiency argument, right? Like, if what your data team is doing like, oh, let's try to target sales better, well, if it costs you or marketing better, if it costs you $300,000 to get that improved marketing, you know, return on an investment, but you could just spend $300,000 in marketing and be a little accurate. Right. Then the question becomes, okay, how much are you actually worth if you're not actually improving it by that much more or just doing the same?

Right. Another thing that I've seen is an audience problem. And I don't mean audience in the marketing sense. I mean like internal teams. I think you actually touched on this maybe in your article. But my view on this is like, who are you making this for ultimately? And the easy answer is typically like some kind of like internal stakeholder that like you maybe interact with. The harder answer is like who do they show that to and then who do they show that to and then who do they show that too?

And then how does it impact a customer? I think people miss that a lot of the time. And then the other one is the delivery mechanism. We like to pick on dashboards, right? Like, could be a dashboard. Could be a really valuable dashboard. I don't know. Could be a report.

Or the delivery mechanisms that I find that can be easier to track ROI on is like a reverse UTL to create an audience in a paid advertising tool or some kind of like audience you create for the email team. like those are actually because I mean then to be honest you're not as dependent on a human to do something with it so like I always if I'm talking to data teams like as close as they can deliver the data like into the value layer and make it like as automated as possible so like if you're making the audiences better for marketing and you can just feed it in there and then their campaigns are better and you can go grab that number and say hey we contributed to this that's amazing because but when you have like a human that's supposed to act

act on optimizing something based off of a report or dashboard, that's tough. Yeah. You have to have, you really have to have the right humans, which is just not always the, you know, yeah. That's a good way of viewing it. Just it makes me think of when I worked on one of the early companies I worked at, we like built like fraud detection models and like, that's really what we did.

We just like created lists of like, here are the providers in terms of like health care, the other providers that we believe are providing fraudulent care. Here's how much we think it's costing you. Like here's the list of claims. like yeah yeah yeah yeah yeah right versus like here's a dashboard showing you like the cost that fraud is had like what am i going to do with this number right it's great for tracking of the effort of that list or the effort of the marketing like the target retargeting audiences yeah you need a dashboard like are we getting better if not we need to improve the segments somehow

that part makes sense but yeah like it is that like connecting into the business and that's then is where like that's where the degree for sometimes tracking but not always taking action on of it be much harder. Right. Yeah. So I want to talk more. I'll throw out a hypothetical scenario. So say I work at a company. My team was downsized. We had like 10. Now we've got like five in the last few years. Like they didn't take any work away. You know, it's still a lot of work. And we're struggling with, you know, say my team's struggling with trying to prove ROI, trying to like be understood by the business. We've already kind of touched on this like ability to sell. But say you're like trying to guide me on how to sell better. Like, what are some things that you would tell people to have like,

hey, like, I'm like, things are not going great. And I at least perceive that there's kind of a disconnect between business perceived value and what we're doing. Like, how do you bridge that gap? Yeah, I think one area is to figure out like, are like, I know you, you, you, in theory, you have the same amount of work that you had before. I do think there's something to be said about, like, going through and figuring out, okay, is this work driving anything? Figuring out what work doesn't and just, you know, being brutal and be like, like we don't do this anymore, right? It does like, whoever the stakeholder is that's going to be upset, you know,

you figure out who the stakeholders are that matter and which ones are not. Right. Kind of going back to like in that article I referenced, like the stakeholder matrix, figure out who has influence, who drives impact in your business, figure out who you want to be aligned with, pick those, you know, one or two that you really think you can drive something with and start getting close to them and having conversations with them and see like, what can we deliver for you that actually drives value? And then you start having that conversation about, okay, what are the two projects we can do?

And then making sure you're actually, one, working with them to make it successful. And then once you have, let's say, successfully delivered these projects that drag some sort of impact for them, making sure you don't stop selling it. I think a lot of people deliver a dashboard and then that's it. I'm done. They don't talk about it again. This is the same thing if you're selling a product or, you know, or trying to sell a service, right? Like once you've, let's say, written an article, made a product, you now have to talk about it. all the time.

Right? Like, it's the things like, yeah, okay, are people engaging with this dashboard? Are people engaging with these segments? You know, are we improving whatever it is that you're, you've built? If not, is it because it's the wrong thing? You know what this product, so to speak internally? Or do we need to help people engage with it more? How do we get like engagement up so that people see that value?

Because if people, if you build something, if you go through less effort and you went through the whole process, understanding what the business needed, built what they needed and now it's not getting engaged with. It really, like, it doesn't matter how well that product is. or how well in theory it could help the business. If the business isn't using it, they're not engaging with it. You know, you have to figure out, okay, what going to do. Starting with, like, just talking about it more.

If you need to have the more like one-on-one meetings with leadership, to be like, hey, we built this, here's what we think we should be doing with it. Like, here's how we think people should be using it. We already maybe haven't even come up with our own strategies that we think the business should implement. I really think it's about being willing to be proactive and go beyond just like, hey, here's the dashboard, now we're done.

Right. I think a lot of people just kind of drop the dashboard off and think like, this is the end of me as the data person, right? Like, I've delivered the thing, but there's this whole other side where it's like, you can be proactive. You can like give recommendations that like go beyond just like here's the data. You can give business thoughts and interest to your leadership. Yeah, I totally agree. I've got two or three like, I want to call them hacks, but like two or three things that that I have seen are just practical. for me, one, and this isn't, sometimes you just can't do this.

Like if the whole team's remote, like this first one I'm going to say, like, it doesn't work. But like one of the things for me, like I supported a sales group was for a couple years, actually, like doing analysis to help land clients essentially. So number one, like I was positive. It was high value. Because if it went well, so the analysis goes into a presentation, goes to a pitch. And then you land a client and these average deals. were in the millions of dollars. So like great. Value is absolutely there. But a lot of ambiguity

because like you get the data and it wouldn't be exactly what we wanted. And like I don't know what especially when you first start like I don't know what I didn't get. You know. So there's a high level of ambiguity. So one of the things I ended up doing and it wasn't even intentional I don't think, but I would actually like hang out a lot because we had the space was kind of segmented like this physical office space segmented by teams. So I just hung out a lot like they had this like table in the sales area and I hung out a lot there and actually did some of the work like tried to get like quick feedback loops with people on the sales team. I asked to be on the sales calls to see them present. I asked for the presentations that my data went into. And then the

hardest part was internally dealing with because people will essentially try to protect your feelings. So the hardest part was being on the sales calls looking at the decks and seeing the 80% of my work that didn't make into the deck and never saying anything. about it, just dealing with it and understanding like, okay, this is part of the process, not complain, not ever express because I never would want to be cut out of the conversation just so they like, don't let me know that I wasted 80% of my time. So that's the one that like, I think is challenging because a lot of times, especially like salespeople will like intentionally like, like you won't actually get to see the final product. And it's not typically because

there's any reason you shouldn't see it. But sometimes it's because they use so little of what you did. they just want to like kind of like brush out under the rug. So I would say, and I know this is like specific, but I think, but it's happened to me in a couple circumstances is like really fight to see the end result if your data is an input into something else, essentially. Yeah, I like that. And I think it kind of leads to something else that I often think about. This is more going towards like the IC level, especially when it comes to like delivering an analysis or something. One of the places I just see repeatedly is that people will often be like, hey, here's everything I did to, you know, like get you these numbers.

Like, they'll literally put that all in a report. When honestly, like you just said, like, most people just need like two numbers, right? Yeah, right. Every time I talked with like someone in the C-suite or VP, they're always just like, they're constantly looking for a couple things. They're looking for a narrative. So, like, you're in a project. They're like, where are we?

What can we do now that we do yesterday? Right. That's what I need to know. What's like the number I'm giving to the CEO say, hey, we're up or down. You don't want to see like. Up or down. Yeah. Literally.

yeah maybe why but not always yeah not only like are you fixing it cool right yeah that's all we do fixing it yeah it's bad so you know but we want to show like all this work we did right it's like that's like that is the that is you want like credit for it you know yeah yeah exactly exactly follow with it look they just wanted two numbers I know you did all this other work I know you want to talk about it I know you can get cool that you right you know duct d or or whatever like yeah right You are the only person or your data theme by the person that sadly cares about that. Yeah. So, yeah, I do think that's a great, like, bit of advice, too.

Yeah. It's like, yeah, look at the product. If they don't use it all, it's fine. They use something. And your process was required to get there. I think that's the big thing. Yeah. Because they only use one number.

It doesn't mean you didn't require the whole process to get to that number. Right. To figure out that's the number that's important. Well, but yes, I think that's totally true. But there's also a feedback look here of, like, learn what the output was and optimize for that output the next time. And I do think there's a number of data teams that like do unintentionally waste time because they don't really know what the final output like should be as far as crucial.

Like crucial like you said upper like two numbers is it higher or lower than it was last quarter or whatever. And I think there is like especially day teams that are feeling overwhelmed. Like I think there is a like for some of them like we really just need to cut and get to like, all right, they just want these two numbers. What's kind of the minimum, like, behind the scenes work that we have to get to the two numbers and have some, like, confidence that the two numbers are directionally correct. Start saying that number. Yeah.

I do. Especially when it comes to, like, final data, it's like, look, we can be direction. It's right. We can't be a hundred percent accurate counting every day because of, you know, a myriad of reasons. No, I think that's good. Going back to that initial question, you asked, like, if your team has been cut back and, I think it's worth looking at the efforts you put in and being like, is half of this stuff even doing anything?

Right. Because I wouldn't be surprised if you found out that a lot of the work you're doing is just performative. Like, yeah, we're giving these numbers. Nothing happens. Like, no one really makes any decisions off in many ways. But they like looking at them. Right.

Right. Right. Whatever. But, you know. And the toughest part of this is what I, which vanity metrics or what I call security blanket metrics where like there there are people that like they need one or two numbers but they want 20 like just in case like somebody ask about it or this or that and those are the toughest ones where the toughest stakeholders that just really want a ton of information they only actually

need like a little bit of it but they're going to continue to ask for all of it just in case and those sometimes there's nothing you can do but sometimes like really developing a relationship and having a level of trust of like, hey, I can get you that number quickly if you need it, but us producing that weekly port, you know, whatever thing with like 20 of these metrics actually like, here's what all goes into it. And if it's automated, sure, like maybe not that big a deal, but some people are in context where like that's not the current state, for example. And those will really eat it.

If you have a small team and enough things that are non-automated, then there is a little bit of a hopelessness, hey, we're stuck forever type of thing. And you need to buy some time to automate some things to get to dig out. Yeah. For sure. Yeah, it's kind of like when Starbucks, the new CEO just came in, the first thing he did was like, let's simplify them in.

It's like, yeah. There's a certain point where like, yep, and I think it's happened with like every business with every, you start doing too many things. Yep. Because you're like, okay, we got to add more. We got to do more things. And you start realizing, hey,

that's actually impacting our abilities to do the things that we were doing originally well, let's, you know, turn that little. I think that's part of the process. I go, like, what should we keep? What should we not? Yeah. I think it's, I mean, I do that with my life. I'll be like, I'll be like, I'll get to a point where I'm like, okay, I'm doing these certain things and I'll start expanding. I'm like, oh, yeah, I'm doing too many things. What I actually want to be doing with my time now? You trim off something. So that's just part of the process of growing into business, the person. Yeah. So, yeah. We're going to take a quick break from the episode to talk about our sponsor, Rudder Stack. Now, I could say a bunch of nice things as if I found a

fancy new tool. But John has been implementing Rudderstack for over half a decade. John, you work with customer event data every day and you know how hard it can be to make sure that data is clean and then to stream it everywhere it needs to go. Yeah, Eric, as you know, customer data can get messy. And if you've ever seen a tag manager, you know how messy it can get. So Rudderstack has really been one of my team's secret weapons. We can collect and standardized data from anywhere, web, mobile, even server side, and then send it to our downstream tools. Now, rumor has it that you have implemented the longest running production instance of Rutterstack at six years and going.

Yes, I can confirm that. And one of the reasons we picked Rutterstack was that it does not store the data and we can live stream data to our downstream tools. One of the things about the implementation that has been so common over all the years and with so many Rutterstack customers is that it wasn't a wholesale replacement of your stack, it fit right into your existing tool set. Yeah, and even with technical tools, Eric, things like Kafka or PubSub, but you don't have to have all that complicated customer data infrastructure.

Well, if you need to stream clean customer data to your entire stack, including your data infrastructure tools, head over to rudderstack.com to learn more. Yep. Okay. So the same hypothetical data team talked about some strategies around sales. what's we have this fun game we do on the show like how many minutes in do we get without talking about AI we're doing good we're doing good but but okay so now here's the other thing all right we we you know we caught the team but like and then you know your boss's boss is like but AI is a thing

like you guys are fine like just use AI so maybe talk to that a little bit just any hot takes you have on that I'm trying me might be specifically hot takes what I I can't like there are definitely benefits I think anyone who's gone to start to use AI definitely probably found some benefits. Like, if you're migrating from one tool to another, like, that is so much easier. Yeah. And so much

just cleaner. Like, you'd always make weird mistakes. Right. At the past, yeah, I still make weird mistakes, but less of them. Like, I remember I was like migrating some work from SQL server to stealth like, and there was like some things where it was like, oh, I need to make sure I always use I like and not just like. Right. Yep. Yep.

Some uppercase. So you just tell your, you know, whatever you're using at the time to make sure you make those changes and then it's pretty easy. It's suddenly you're moving really quickly. Especially when it comes to like code already existing and then asking me if first change, I found that AI is really good at that making those changes. And so I think that'll kind of continue. I think that will then start to eat into maybe some level of vendor lock-in.

It's like, hey, if I can switch code from SQL server Snowflake or from whatever it might be really quickly, the projects that used to maybe take a year is now three months, right? As long as you have everything well and set up well. So I think there's that aspect. I think there's other aspects that are less ideal, right? Like, can we stick AI on top of your dating warehouse? I haven't seen a core implementation of that. I've seen like decent, basic implementation.

Better than they were definitely like five years ago, whatever back then was in terms of like English or natural language. Yeah, right, right. It's better than that, but still heavily limited. So you can maybe do things a little bit faster, but that's a little clunky. I think another area I've seen that I like is. I've seen some tools that help you maybe get you some of those initial metrics. Like you can ask you like, hey, what are we, what are our sales currently?

And then it can answer some of those basic questions. And then instead of trying to answer the harder questions, it will like send you, it'll look through your dashboards and try to send a new dashboard that maybe could answer these questions better, which then it features that engagement problem a little bit as well. Like, can we get able to engage with these things a little more? How do we get them? Generally the process for anyone who's worked in a day team knows, it's like, well, even if a dashboard exists, the first person or the first place someone's

like, oh, it's probably you and ask, like, can you give me this number? Right. Before you're looking at the dashboard being made for them a week ago. 100%. Yeah. I do think there's something there where it's like, yeah, if we can get that problem solve or it's like, look, there's a dashboard that exists. Let's get a number and then let's get you there versus like, you know, interacting

with the day team who's like currently doing that. Well, I mean, that's one of the funniest things to meet with the AI conversation is at at least from my perspective in the data world, we've kind of skipped over the, hey, why don't we index and make the thing searchable. Catalogs are obviously a thing and they're out there and there's some good ones. But as far as like first class, like, oh, there's some catalog that everybody uses, that doesn't really exist. And there's a practical of a. And here's the funny part.

Like the business is probably never going to know what's out there. If once you hit the 50s, hundreds of reports, they're not going to know. Most BI tools I've used like search is not great. And so. so there's that but then honestly I can't tell you how many times I've seen where like the analysts don't remember either and they just make another one like like it's kind of crazy and to think like that maybe one of the like low hanging fruit solves here is like AI is just kind of kind of index and search what you have and that's actually pretty valuable I've even seen some of the approaches where there's like a like obviously there's semantic layer like approach but there's some of these like AI layers that like they have. have essentially like verified queries like where it really is like talk to it and then like ask it a question and it essentially will try to pull from a library of queries which like that's pretty that in terms of like the scope of like text to sequel on like one end like maybe

semantic layer in the middle and then like there is this other end of like I don't know just give me the queries and I'll pick between the queries that you have so I think I think there's going to be, and AI, you know, as it improves, maybe, like, there'll be, like, a broader spectrum of, like, what it can do. But I do think it can do pretty well with it'll just give it a library of queries and, like, tell it, have it find the right one. So, yeah, I think one thing that remains true is search is hard. Yeah. I didn't get something that's funny, because for anyone who's tried to search anything on Reddit on. Right. LinkedIn. Right. How long before you go to Google and you're just like, LinkedIn? Yeah. Yep. Yep. Yes. Yes. It's like,

It's like I you cannot find anything right in Twitter search. You just have to Google it. So yeah, I think that's that's an interesting idea there. Like we already have the queries. We know what the queries have been run. Why not just try to base off that versus trying to custom query from scratch? And I know that's something that I saw with. I think Databricks is AIBI tool.

I remember like you can kind of give it some preformatted. Yes. Right. That's like base layer. Right. Yeah. I think Snowflike and Databricks both do that. And then a lot of the tools, I mean,

there are some good third-party tools, you know, that have AI chat on the semantic layer. And I've seen a lot of progress in that world. But I think for all those tools, though, they have this challenge of like, we want people to work in a new way and we want to work this way. But everybody also wants all the old functionality. And, you know, I mean, let's talk like tablo. Like if you want the full functionality of like, say a tableau and you want this full new experience to have this wonderful like AI first thing like that's just this is not practical yet you know like it's either like tablo's going to be working backwards into like adding AI onto their stuff and they are and then these

new tools are going to be working the other direction like maybe starting a. Native but trying to include every little thing that's people want in a BI tool and I think and I think part of it depends a bit like if you're a startup then like yeah like go find an AI like native tool and like you guys and you're maybe pretty flexible on what it can do. Great. And then, you know, established business is probably more likely to pick, you know, one of these like a Power BI or Tabler, like one of the bigger names. And then like just go at their speed as they implement AI things.

They'll try them. But it's just so hard to be in a spot. And there are people trying to do it. And I think I've seen some success. But it's hard to be in that spot in the middle where you can like attract enterprise requirements where they want all this like rich feature stuff around a BI tool and have like a really good native like AI. experience.

Yeah, no, I think that's it. I have this article I haven't put out yet, but it's like trying to think on the whole why do we pick dashboards in the first place. Because obviously, it pokes of dashboards and makes fun of it, but I'm like, but we got there for a reason, right? There was reason companies like this is what we want. And so I often think about that even with AI, AI kind of approach where it's like, okay, you know, I've seen ones where like you can ask the questions,

it'll build charts too, right? Oh, nice. There's like your trends over time for the certain. metric. But, you know, I still think there's some gaps where maybe it's like, maybe it's because people want filters. Maybe it's because people want all of these things. And then even there, I think about that. I'm like, sometimes you give people all the things they ask for like filters and they still only want like one you. Right. And like I remember I was talking to someone. They're like, yeah, one thing I learned when I deliver reports to leadership is just put it in

the PowerPoint because like one time they're like, yeah, I was trying to like show it to leadership and something didn't work. Uh-huh. Yeah. Yeah. I was like, you know what? I'm just going to put this statley. They only care about this one view anyway. Right. Take a picture. I'm so glad you brought this up because the one thing that I've never seen that I think would be a killer feature in a BI tool is download to PowerPoint. Oh, yeah.

Like, I think they not try to do that some of the AI or something. There is a thing. Yeah, there is a thing. And maybe PowerBI can do it or I mean PowerPoint generically, like Google Slides PowerPoint. But PDF is a thing and a lot of them. But like it is interesting. That hasn't been more of a like. first-party support where like everybody decides support like some kind of slide format because that is like a

practical I mean I say this but like you know power be I you can like kind of do present like in tableau as well you can kind of do the presentation mode so I guess maybe I don't know stories yeah this too I don't think so but maybe but so yeah maybe the you know and of course there's so like you want people engaged in using your tool and you don't actually want to encourage egress out of your tool because you want to use your tool but all that to say yeah I think that is the practical, like, destination of a lot of this data is some sort of presentation. No, and I imagine, again, there are some more operational workflows where it's like, yeah, we want this dashboard. Right.

Right. I constantly ask for, why don't we end up on dashboard? Like, there's, I wish I mean where the first one started because it's just like, where, like, we make fun of it. It's an easy output, but there was a reason we got here. And so, I think AI fully answered that, like you said. Like, I think there's this functionality that dashboards provide that people want, whether they use it or not, is a different question. Right.

But that could be more, again, more of, again, more of on issues wherever we talk about, like, are we engaging people correctly? Are we making sure they can find things? Honestly, like, almost every time I worked at, or at least two companies I worked at, one was probably self-fared services, well, was Facebook. People were trying to build a website that could just make it easier to find all the dashboards. Yeah. So, you put, like, a three company, like, whatever, like, Tableau, whatever. We're trying to keep easy to search. And that is the goal of this website.

I mean, technically Tablo has that too. Yeah. I don't know if you're going to do better than Taplo. Right. Like, it's funny to see everyone try to build something like that. So they're trying to build a dashboard to find their dashboards? All right. All right.

Right. Right. Yeah. A lot of data catalogs try to allow you to not just do data pipelines and tables. Right. Right. Yeah. That makes sense.

Okay. So we talked about AI. What as far as like, we talked about this before the show, just, you know, you're working with your community formed. And you talked to a lot of different people at different levels in their career, you know, in the data space. What do you think some of the top skills are that like, if I'm sitting here, I see or maybe I'm like a team leader or something? Like, what are some of the top skills that people need to develop, you know, to get to that next level career wise? And really whether it's, you know, they want to progress. to be kind of an architect type role maybe

they want to progress to be some kind of leader they want to like do something on their own what do you think you know what are on your list of like top couple skills people need you know I'm trying to think the specific skills there I mean there's the generic statement like on like we got to be better communication I'm trying to work

to make that clearer on what I have been trying to improve the communication around that but like I'll often see people you have better communication which to me is like engineering statement It is. Yeah. And so like I wrote like in a recent article, I talked about like a few ways that you can do that,

whether it's like if you are a day leader and you're trying to get buying for things, you know, talk about what the cost of inaction is like and figure out how to portray that. In a way, it's like, hey, we don't do the certain project. Here's the cost to the business. Talking about like impact and framing it's such a way that the business understands like, hey, what is the impact of us doing that? Not in the sense of like, hey, if we don't do this like, you know, our dashboard will be slow. That's like one side of things.

It's very starting to get in the right direction. It's more on the technical side. But there are other things where it's like, hey, we're, you know, last quarter we realized that, you know, sales teams are, you know, not getting their numbers fast enough. We talked about, like, numbers for sales team. And because of that, maybe we lost a few deals or maybe, you know, things like that. And having those conversations and trying to think about, like,

what does the business actually care about? Which then also ties to, like, the building up your ability to understand your business, which, right, that is, you know, domain specific, right? If you want to do supply chain, be really good at that. and understand what people in the supply chain care about. I was talking to someone a week who's kind of, they've done supply chain their whole life. They did at the Home Depot and Amazon and a coupon.

And I thought it was interesting. They're like, they are clearly, they're not just technical, but they clearly understand that space. Right. After going through it into so many different industries, like different versions of that. And there was another conversation I had with someone here in Denver. I remember what they were in,

what was it was like telecommunications. And then like they're not. not just clearly, again, they're not just technical. They understand the products. They understand who the competitor and what their products are. And I think that helps you be better when it comes to like, oh, which we build in terms of like maybe data products that help the business. Well, if you don't understand like the threats currently that are opposing the business or like what your current business is really positioned as, you might not be as good to give that advice. Because like, well, you know, you're just going to be generic dashboards that

don't initially push me in a certain direction. So they think that aspect, you know, if you're, whatever your business is that you're in, understand it. If you want to be in it long term, you got to like it a little bit. Right. It's funny how much some people like the business that they're in. And so, yeah, you got to like it a little bit. I think also just if you are going down, like, let's say the routes. I mean, I guess this is kind of generically, whatever route you end up going,

you kind of have to learn to be a little more proactive and kind of make things happen. I just to say, if you see an opportunity, don't wait and wait for permission. I think that's kind of the skill. Yeah, that's a good one. Well, maybe some people you have initially and they kind of get beat out of you a little bit. So you're like, well, nothing really matters. If I do it, like, no one uses it. But there's a certain point where we have to come back and be willing to be like,

hey, I'm going to, I think we should be doing something here. Or I think I should build a product. Or I think, you know, I could, whatever, go out and I only do consulting and things that that nature. You can have to be, kind of to make it happen. And you have to be willing to reach out to people who will tell you you're wrong or you're dumb. Yeah. You need to reach out to people who ignore you.

And then what will happen whether you're in or out of the business, right? You're going to give ideas to the business. and they'd be like, yeah, man. I was talking to someone who was, they were having some challenges with some analysts who were like senior level and they wanted to be staff. And one of the blockers that they kept running into is like, well, no one's buying into your ideas. And like, that's not fair because then I can't do those ideas.

And they can prove that. But it's like, well, but that's part of the process. Right. If people don't think those ideas are good, what can you do? Like, if you generally think those are good ideas, what is wrong with how you're presenting it? Right. To make that good.

I recently kind of had a. chart that I put in talking about kind of the senior plateau, which is where a lot of people I think get stuck. It was a good chart. I'm trying to remember the newsletter I pulled it from, but listed in the article. And it just kind of has like different curves of growth. So like when you're a early in a career, it's a lot of technical growth. Right.

A little bit later kind of senior, it's like more towards like communication skills and soft skills. And then finally it's like the business skills. And it's like, you know, it's all little S careers. They're not built on top of each other, you know, side by side. And you just eventually plateau and all those. skills where they're getting more technical probably won't make you grow more at a certain point. At a certain point, getting more soft skills probably won't let you grow that much more.

And really for a lot of people, last step is like, okay, let me add business skills and apply all this. And again, that means you're going to do things where, and this is, you know, if you were to own a business, you're going to apply ideas, people are anything to sell on word and connect and you figure out, okay, you have to change our product, you have to change my messaging. What is it? And again, it's the same internal as it is external in many ways. If you would think it's, you, you, you, should go a certain direction. So that's something that I think I've been trying to also just communicate more with people. It's like, you know, kind of have to build up those skills. And you don't have to rush it to me. The other thing is like those skills will happen

or do them all at once. Exactly. Don't try to do them all at once. Like get really good technically. And then once you've gotten really good technically, then you can work the next than the next. And you'll keep picking up new technical skills along the way because you'll go for a new business and work on a new technology. Right. All that stuff happens. Yeah. Yeah. And yeah. And what you just said, I think is really true. Typically, And not always the case, but typically the technical skills will get, there'll be some forcing functions in your role to pick them up. And the ones that there might never be a forcing function is typically business skills, right? So I think if I had to pick for people often as the business

skills that you probably have to be more proactive and seek out. And a lot of times the technical skills just happen because it's like what you do every day. Not always. There's some companies that you may be in where like you're just really limited scope and you really do need it like, seek out more technical skills. So like there are people out there that are in that boat too. But two of the things that I really like that I've heard, one I've heard a lot over the last like couple years is show don't tell. Yeah.

You can get really stuck in these loops of like presentations and telling people about your ideas. And I, like, I still do this. I think I do it less than I used to. But I still grossly overestimate people's ability to abstract. Yeah. which what I mean by that practically is like if you don't tactically show them something in like a high fidelity detail, they don't get it. Even if you're crystal clear with your communication, even if you try to use examples, like it's really challenging for people to make that connection. And for me, communication like versus even doing this with clients, like, especially and the cool part is the AI part is like I will versus like,

trying to explain or like show you could use it in this way if I'm saying words like well you could do it like no like go make up some data with faker loaded into a tool in your environment and like actually tactically like show them something yeah that for me has just been a really like big like okay this is the way to do it and it's actually way easier than it used to be yeah so that's a big one and then the permission one you hit on like especially like early on in my career I had a phenomenal boss that essentially like told me like early on is like, don't go ask for permission. Just do it.

I'll cover you. Like it'll be fine, which was just a really good. You don't always have that as a boss, right? So you do have to be sensitive to your current situation. And if you have a very like cautious boss, like be careful with that.

But if you got a good boss, then like that is, that's huge. Because if you're constantly like asking for permission for things, like there's just a lot of stuff that like you're going to kind of get answers or kind of get like nothing you're never going to get clarity and essentially like as long as you have a like decent internal calibration of risk that matches the calibration of risk for your company which can be wildly different. I've worked for people that were entrepreneurs where like

I could do anything like one of my jobs like working for an entrepreneur. I got a credit card without a limit on day one. He was like, spend it like it's your money and like go. Like, And the optimization from there was like, just move fast. Like, I don't, within reason, I don't care what it costs. I don't care what it, you know, just go to like the opposite of working for multi-billion dollar company where there's like 12 layers of approval. So you have to know what context you're in. But even inside your context, like, like I think most people are apt to be waiting too long

for permission or for like, kind of do this or whatever. Whereas if they just like tried to like, like inside their context, be creative with a prototype, use some open source tool that's free and then whatever to try to like have something to actually show like is a better route for people. I really like the show, don't tell. I think that's, it's one of the things that also is easy to say, but it's so easy to fall back into tell.

It is. Yeah. Yeah. You will find yourself telling more often than you have to be disciplined. I was talking to another day leader recently. It's like, you know, because I've been running some cohorts to help road day leaders. kind of challenge them and, you know, some advice I give, they're like, yeah, no, it's all stuff. Like, in some cases, it's new. In some cases, it's like, you know, we know this, but it's like,

when it comes to having the discipline to do it, it's just, it's hard, right? Like, we know the right thing. Right. But yeah, like, with show, don't tell. It's like, okay, am I, like, you almost have to ask yourself whenever you're talking about. Am I showing or am I telling right now? Yeah. So I think, you know, for example, I think about, like, movies. It's like, if you write movies or, like, you know, scripts all the time, you probably know, to show, don't tell, And why do we still end up with so many movies that like tell us? Yeah. Versus like get us and get us and get it's like, right.

It's just easier. She's going to tell you, here's what I did or whatever, versus trying to move you with like an actual succinct story. Like you said, like take it out of this abstract and put it into something tangible. Well, and here's the other thing too. I don't know if this is an intentional decision for people, but showing is often more vulnerable because you make more decisions that are more concrete and therefore are more likely to get a reaction of like,

oh, that's not what I wanted at all. You know what I mean? Because I've had that. I've done one recently I can think of like show like very tangibly showing somebody like all the way through with like, like I said, done me like all the thing. And I got kind of got the reaction. That's not what I want at all. And it's like, oh man.

Like where if I just talked about it and like, hey, do you want? And then I gathered requirements and like kind of gone that route. It would have been like like I would have been more likely to be right the first time. Sure. but I've also learned that's way slower. And people are horrible at giving, most people are horrible at giving requirements. But if you can show them something and you can get a reaction of like,

that's not what I want at all, that's golden. That's great. Yeah. It's great. Yeah. Like, people will tell me what they don't.

Like, if you give them something that's so wrong, it's like the whole staff overflow joke where it's like, how do you get your question answered off stack overflow? Exactly, yeah, yeah. You make two counts, one that asks the question, one that gives an obviously wrong answer. Get that over there for a right. It's kind of the same idea, right?

Like, yeah, when you give someone something so wrong that they're right, no, it's not right at all. Suddenly the brain's like, I know exactly what. Right. What do you have to say? What do you want to? It's like someone where you want to go to eat. Exactly. But if you mention something, you're like, no, not there.

I don't want that. Yeah. I mean, I, yeah, I think that's. And the first time, a couple times it's kind of uncomfortable was like, oh, man, like, I didn't get that right at all. But like when you get used to the process, like it's so valuable because you can even, I even mentally think to myself of like, cool, I'm going to give them something to react against or like just like even internally think through like, I'm going to give them something

to react to or to react against. And even in my mind, like, assume that it's wrong, but it really doesn't matter if it's right or wrong at this stage. We just want to move forward and this is the best way. And again, go back to like, it's not necessarily a skill of a big part of half of the business or running the business is like, you got to be wrong. Right. You're going to be wrong.

Right. And it's okay. You just got to improve. Right. Yeah. And even like having managed people like the, you know, typically, you know, typically, like I said earlier, like people are going to be more on the cautious side of like, I don't want to do anything wrong.

And I've like intentionally told people before of like, hey, like I would much rather like rein you in than have to like always push you essentially. And again, there's probably some context that's not going to be. be true of and you have to know your situation, your manager. If you're in some kind of like highly cautious environment, like maybe some kind of like, you know, making this a medical research environment or pharmaceutical environment or like highly regulated like nuclear or something like it's different. But you know, generic business, which, you know, a lot of us work in, you know, and various generic businesses, I find that people really like just need to like take more risk

essentially. You see those charts. Yeah. Yeah. Yeah. Yeah. selling a product, a digital product. Nothing's the world. Right. Nobody's going to die and we're not going to like start a, yeah, some kind of nuclear event. Awesome. Well, our time is flown by. Any parting words for us?

I always like to give people a chance to like kind of, you know, give any advice or anything that's been meaningful to you in the last couple months. I think maybe the, at least recent things I've been reading, it's ego with the enemy. Oh, that's good. Yeah. So the one tidbit I took from that, especially because we live in a very social world, It's one thing to look impressive. It's another thing to be impressive.

Oh, nice. You're going out, I guess. Trying to figure out what to do. Sometimes it's okay to go through moments where you, maybe you feel less impressive and you're not like, I don't know, showing off online just to actually, you know, do something worthwhile. Right. Doing things worthwhile is hard. And again, it's going to have failure.

You'd be wrong. And the process sucks. But I've learned to love that. Yeah. Yeah. Awesome. Great parting words. All right.

Then, thanks for being on the show. The Datastack show is brought to you by Rudderstack. Learn more at rudderstack.com.