The Data Stack Show - Re-Air: From PDFs to BI and Beyond: The Future of the Data Frontend with Ryan Dolley of GoodData

Episode Date: November 5, 2025

This episode is a re-air of one of our most popular conversations from this year, 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, Ryan Dolley joins Eric and John to discuss his unique journey from playwriting to leading product strategy in the data industry. The conversation explores the evolution of business intelligence (BI), the growing influence of AI on analytics, and the shifting skill sets required for data professionals. Key topics include the challenges of adapting to rapid technological change, the importance of embracing engineering practices in BI, and the need for continuous learning. Listeners will gain insights into how AI is transforming data roles, why storytelling remains central to analytics, practical advice for thriving in a fast-changing industry, and so much more. Highlights from this week’s conversation include:Ryan’s Journey: From Playwriting to Data (1:05)Making a Living as a Playwright (3:02)Transitioning to BI: Night School and First Data Jobs (4:12)Storytelling and Data: The Art of BI (6:22)Early BI Work: Data Warehouses and PDF Reports (8:33)Moving from Utilities to Consulting (13:03)Building vs. Implementing: Product Strategy Lessons (16:37)The AI Shift in BI and Analytics (18:41)Automation Anxiety: The Human Side of Data Change (22:16)The Evolving Role of BI Experts (25:18)Adapting to Change: Learning Code and Experimentation (29:34)AI and the Future of Embedded Analytics (33:38)Capturing Intent: The Value of Modern BI Interfaces (37:03)Bridging the Data and Software Engineering Gap (39:13)The Historical Divide: Data vs. Software Engineering (43:06)Organizational Challenges: Where Does BI Belong? (46:05)Reflections on Self-Service BI and Value (48:46)If Not Data: Ryan’s Alternate Career Paths (49:04)Final Thoughts and Takeaways (50:17)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 datastackshow.com. Hi, I'm Eric Dots. And I'm John Wessel. Welcome to The Datastack Show. The Datastack Show is a podcast where we talk about the technical, business, 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, RudderSack. 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 real time. You can learn more at rudderstack.com. Welcome back to The Datasack Show. We have Ryan Dolly on with us, and we have a ton to talk about. Ryan, thanks for joining us. Yeah, I'm thrilled to be here, guys. Thanks for having me back.

All right, give us just a brief background. Where did you start your career? And then how'd you end up in data? Yeah, sure. So I started my career as a student in playwriting and acting, actually, but I couldn't make any money. And so I transitioned to data about 15 years ago and have been just going full steam at it ever since. I'm the VP of product strategy, a good data, which is an analytics platform. I'm the co-host of the Super Data Brothers show.

And I really have spent my career focused on kind of the intersection of data and the humans who consume it. Right. So that's really what drives me. Awesome. Ryan, excited to have you on the show. One of the things that I've been thinking a ton about recently is workflows, about what data people are currently doing in their daily workflows and then like what they will be doing in the next couple of years

as we see AI get more incorporated into those workflows. So I'm pumped about that topic. What are some of the things you want to cover? Yeah, I'm looking to hearing your guys' thoughts on kind of the experience, what it feels like to work in data right now. I think there's a lot of excitement, but there's also a little bit of fear or trepidition about like what do our jobs look like in the future. And I think it's mostly good. It's exciting. So I'm looking forward to exploring that with you guys. Awesome. Well, let's dig in. Awesome. Let's do it.

Ryan, welcome to the Datasack show, a fellow data podcaster. So that's always super fun. Yeah, that's unique. It is. Yeah, thanks for having me, guys. A big fan of the show. So I'm happy to be back. Awesome. Well, okay, I want to start out. We broached this subject when we were just chit-chatting before we hit record, but this is so crazy to me. Okay, you started your career as a playwright, and I actually want to hear about your transition from that, you know, going to night school, getting your degree, and then getting into BI, because it's a really crazy story. But how many playwrights do you estimate do playwriting as their full-time job? I think probably between 20 and 40 are people who make 100% of their income writing plays. There's a lot of people who make some of their income writing plays, but not enough. And so that's where you get the community of university professors in playwriting. There's thousands of them, right? But people who just kind of pen to paper, I guess typing nowadays, writing plays, and that's all their income.

It's very few. That's so interesting. I was trying to think of other professions where there's that small, you know, because there's lots of theater around the world, but that's a really small concentration of people, you know, who that's sort of their only source of income. That's absolutely fascinating. Yeah. A play has to be a huge hit for you to make a lot of money, right? Like a huge hit. And how many can you even think of off the top of your head? Very few. Yeah. Sure. Yeah. Okay. Well, thank you for entertaining that fun fact. So how'd you go from playwriting, acting into BI? Yeah. It seems like a crazy,

I mean, not a normal, you know, sort of, maybe like over a long period of time, but he went to school and then right into BI. Yeah, I, listen, it's really hard to make money in playwriting and acting. And that was ultimately, you know, that was okay when I was 22 and 24 and even 26 a little bit, But by the time 28 rolled around, I was like, hey, I, if I was working at a job doing data entry, actually, and I was looking around the department. And everyone in my department had been there at least 10 years. I was the new guy. And they all hated each other.

And I just saw that, like, if I don't get some skills that where I can get out of this, like, this is my future. And luckily, that job was at Northwestern University. And so I was living in Chicago at the time. And I was able to get a. crazy discount. You know, Northwestern's not a cheap school. Oh, yeah. But the employee did, yeah, I mean, it's really cost a ton, but the employee discount was great. And so I went to night school to study information systems and kind of cobbled together a course track focused on, you know,

kind of a mix of general computer science topics and topics related to database. There were no analytics or BI courses you could take back in 2008. It was all database stuff. It was just database stuff. So I took database stuff. And then I happened to know somebody who connected me through with a company in Madison, Wisconsin. So when I was wrapping up that night school program, I applied for grad school at Northwestern and I applied for this job in Madison. And when I got the job offer, I took it and moved. And it wound up being the best decision I ever made. I met my wife there. It totally changed the trajectory of my life in so many incredible ways. But that was really it. And honestly, what attracted me to BI is, look, you think

about all the things you could do in technology. And BI is really the closest to storytelling. And that's what I had studied, right? Was what moves people, what motivates people? And being able to kind of apply that lens in a business setting by looking at data and then saying, what's the message in this data and how do we communicate it in a way that will move business people to take the right action was what was appealing to me. Yeah, it is interesting. I hadn't thought about that because if you think about, you know, if you think about modern movies, you know, especially, I mean, we watch a lot of animated movies because I have young children. Yeah, me too. You know, but it's sort of, it's not that there are messages in there, right, but it's sort of transporting

you to a different place and sort of creating a world or whatever, but, you know, and I know the same can be true of playwriting, but there's sort of an integrity that you need to retain. with, like, the character and the, what you're trying to communicate and everything. Form of data integrity. Exactly, yes, right? There really is there, you know, which is super interesting. And it's pretty complex. It's a complex.

Right. So, yeah, that's super, that is super interesting. There's a DAG involved, in fact. Yeah. Well, you think about, like, when it comes to entertainment, take movies, you know, it's hard for people oftentimes to even pinpoint what it is that makes a movie compelling right but it is the stuff you're talking about like there there's an almost uh it's an art but there is to some degree of science behind it and it's and it has to do with you know the characters

the world are they consistent are their motivations clear do you understand the motivations right what do they i mean the art of drama is like two people want something and only one of them can get it right and and that's ultimately what it all boils down to and you know i mean business is a full contact sport, and oftentimes, you know, we, us and our competitors both want that next funding round, or we want that deal with that Fortune 500 company, and only one of us is going to get it, right? And Data is part of the story that you tell to lead to that outcome. Yep. I love it. I love it. Okay. Dick, so your first job, you go into B.I. Yeah. After nice school, what were you doing? What were you working on? And actually, maybe even start with,

what would your definition of BI be back then? Yeah, that's a great way. You know, because, you know, that's a, it's easy for us to look back and say, oh, I started in BI, right? But what we mean by BI now is very different, you know. Guys, this has changed so much since 2010. The, what I was doing back then was I got hired onto a team that it was a combination of a data warehouse and BI team.

And so we were building the company's very first enterprise data warehouse using an extremely rigid kind of waterfall project management approach where we were working on that data warehouse for, I don't know, four years. I had joined kind of towards the end of that process, really, but they had been working on it for a long time. They had invested a ton of money. And all to this magical day when we're going to flip the switch and the data warehouse is going to turn on and then suddenly value. will appear. And then the BI layer on top of that was really about the distribution, honestly, of PDFs more than anything. It was like, how do we, you know, how do we create? The form factor back then was it looked like a piece of paper. It was a standard report and it had a header at the top and a footer at the bottom and a table of numbers in the middle. And that was predominantly what we built. Dashboards were, like you could build dashboards in the BI tool. of the era. Most companies didn't. They were, if it's hard to believe now, but they were a cool new thing that like if you were rolling out dashboards, that was pretty cutting edge at a lot of companies in 2010. Now it's cutting edge to cut dashboards. Exactly. Exactly. They had to move back

to exactly what you said, like tables. Yeah. Well, and it's, you know, the funny thing about it in some ways, like a table is quite easy for a lot of the AIs and expert systems we're trying to develop to interpret in a way that a dashboard is not. And so there is some degree of kind of going back to basics here. But it was just such a different time. I mean, it was like, look, the job of data analyst, I don't know how many people even had that job title in 2010. Even that, like, you might be a supply chain analyst or a financial analyst. But the idea that your whole job was just to analyze data rather than to be a supply chain expert who, happened, who was really good at Excel. That's kind of what it boiled down to back then.

Yeah. And everything was done by a centralized team. Nobody in the company could produce these reports, but the people on my team. There was no, hey, I'm an analyst in a department and I have access to the BI tool and I can build my own stuff. That was, it's not that was an unheard of pattern, but it wasn't widely adopted and certainly not where I was. I was at a utility company. So, you know, right there, utilities are cautious about adopting new technology. analogies and approaches. Yep. Okay, so then fast forward. So what was your next move after that? And then how did you eventually wind up at good data? I was really frustrated working at a utility company. I was really, I mean, I just, I saw that there was a lot of cool stuff going on. And what specifically

drove me to make the move was I went to, we were, the product we worked with at the time was Cognos, which was a really huge BI tool in the 2000s and maybe the first early 20 teens. And Well, I mean, it might surprise people, I mean, how many people, how many companies probably still run it. Yeah. It's so, so I got to be really good friends with a lot of people on the Cognos product team. I became, IBM has this program called IBM champions for the people outside of IBM who are the greatest experts and advocates for their technology.

And I got that award five years in a row. Wow. So I got to know the product team really well. Yeah. And so I think, I mean, it's like many thousands of customers still and a lot of the largest customers on Earth still have huge Cognos deployments. So it's I talk about it like it's gone, right?

But it's really just I don't work on it anymore. It takes a long time for that entrenched stuff to go away. Yeah, yeah. Well, listen, if what you need to do is send out 10,000 customized PDFs to people, like there's still no better tool in the world. I mean, you can in fact probably make an incredible living doing Cognos consulting. Oh, yeah. You can. In fact, I think you might you can maybe like there was a,

if you go back six years, there was like a, a lull, but a lot of the people who are really great at that generation of B.I. tools are approaching or in retirement. And so if you're an expert in those tools and, you know, General Motors or Procter & Gamble, someone to come in and help maintain and build on them, that's actually a high demand skill. Yeah. Yeah. Ride cognos into the sunset, me. That's right. Anyway, sorry for Oh, yeah. So I got, listen, I got, I just got frustrated because I went to a user group and IBM was kind of showing the new version of the software. And I went back to the company I worked at and they were like, well, it's on the capital project schedule for five years from now. And so I was like, okay, so I'm going to be using this version for the next five years. I got to get out of here. Yeah. Yeah. Because I just like I got too passionate. I got too passionate about the work, you know. And so just the pace of work, I could see that there were big changes coming to VI. And I was a little worried that they were going to pass me by. And so I actually started a blog. And that blog got the attention of a consulting firm based out of Chicago. And they hired me on

as just a consultant there. And then I eventually became a partner at that firm. Oh, cool. Yeah. Yeah. It was, and I'm still really good friends with those guys. But it's the same story. I could see that there was another wave of innovation coming. And I decided in 2020, 2021 to move over to the vendor side of things. And so that kind of takes me. It went to a startup called count, which makes a really cool B.I. Canvas. It's like the world's only multiplayer BI tool. I don't know how to describe it. But and then from there to good data where, yeah, where I kind of, I'm kind of like, uh, in charge of, I'm almost like a executive individual contributor where what I focus on is go to market strategy, helping the product team and then representing the company in public. So big data London's coming up next week. If you're going to Big Data London, you'll come say hi. I'll be presenting there with the good data team. Awesome. Okay. I want to sort of skip up. over the first move, you know, where you saw the next version of Cognos and it was going to take five years and see you said, okay, there are changes happening. So then you started the blog. Yep. That makes sense. And then you moved on from the consultancy. What big changes did you see

at that, you know, at that juncture? Yeah. I mean, I think that was in the kind of the just before the explosion of the modern data stack, right? Yep. And so there were, couple things that I saw happening. One was that the innovation on the data front end had really stalled out. Tablo is a great dashboarding tool. PowerBI is a great dashboarding tool. But I was just asking myself, like there has to be something other than this, right? Like, this can't be the end state of the front end of data. And so that really set me looking for what that could be. And then there was also, you just, I mean, I could see coming, you know, that was also when the lakehouse was a cool new idea. Obviously, that's a pretty standard, pretty standard approach nowadays.

And then, and I got really interested, I started hearing about this thing called DBT. Yep. And so kind of that, that confluence of events really made me say, okay, I, and also I wanted to build something, right? I had spent so much time, I had spent so much time implementing. and you develop really strong opinions about what tools should do, you know? Yeah, yeah. And so then I said, listen, I've gotten good at implementing its time to build, and that was the final thing that pushed me to move into the startup world.

What's the most surprising thing to you having transitioned from, you know, a customer implementer to a builder? Like something where maybe if you look back to your Cognos days and you're at a user group and you're like, come on, guys. And then now you're on the builder side and you're like, Oh, really. Look, there's a lot. I will, if you're listening to this,

I want you to imagine the vendor you're really frustrated with right now. Okay, just picture it in your head. And now keep in mind that they have hundreds or thousands of other customers who are equally frustrated, but with something else, right, that you're doing. And you know that when you don't work for a vendor, But the complexity of juggling vision and customer requests is really, it's a really delicate balance. Yep. And it's hard to appreciate.

Even as I describe it, it probably seems like, well, yeah, like obviously that's what it is, right? But the surprise for me was really in the complexity and delicacy of that process. Yep. There's so much that goes into what features should we implement and why. and for whom and on what timetable. And so that surprised me, and I'd say the other thing that surprised me is just how often you wind up being wrong, you know? When you're an outside expert implementing these systems, you have very strong opinions

and you think, oh, I can step in and like, I will know exactly what to do because I've spent so much time implementing these systems. But when you actually find yourself in that chair, you learn that part of the process is placing bets and some of those bets are not going to turn out. That's okay. Right. Yep. Yep, man. Wise words for anyone looking to get into product leadership. Okay, John, I've been dominating the mic, but let's, because I know you have a bunch of questions about this. So here's a lead off topic for you, John. But I'll start by asking Ryan a question. So that was that shift. And now we're in the middle of another one. And I know that probably consumes an immense amount of your thinking as far as product strategy at good data. So what are sort of your core?

beliefs about the current shift that's happening. First of all, like, what are the major things that are changing? And then how does that influence the way that you think about product at good data? Yeah. The fundamental shift we're going through is obvious in a lot of ways. And it's, of course, driven by the advent of artificial intelligence in the industry, right? And I think that my fundamental belief has to do is really that the front end of data is going to, you know, to radically change the way we are we present data is going to change but that it's the change is going to be a lot more about how we facilitate communication with our end users and how we take that and how we turn that more and more into a two-way process you know in bi a lot of times

you're building shipping and moving on to the next thing and and that that is is to totally going to change. And I think it's going to be a lot more about, you know, co-creation with our consumers, iterating through problems, and being the front end of a combination of human and expert systems that are running behind the scenes. And I think a big part of that is that, you know, in BI, we have really struggled to embrace engineering practices and code. And that's just not going to work going forward. I mean, it just isn't. These AI tools, language is the second L and LLM, right? And so this purely UI-driven development process and UI-driven consumption process is not going to hold going forward. And that's something that we've invested a lot in at good

data, is building up the tooling around that as kind of the foundation of what we see going forward as AI becomes a bigger part of the data front end. Yeah, I couldn't agree with you more on that. But I think it poses a question. We talked about this before the show. I'll actually paint a picture. I've got a picture in my mind from when I first started in data and IT in general like almost 15 years ago. And then a conversation I had this week, which I think will illustrate this. When I first started, you know, however old I was, 23, 24, I remember sitting down with some ops users and some accounting users to automate some reports. Like distinctly remember they pull up Excel, pull up a chair, Windows XP, whatever it was at the time. Yeah.

And actually, like, watching the process in Excel. And I, you know, was newly equipped with access to the database and knowledge of SQL and, like, super excited about automating these reports. But I remember the emotional reaction of the people, like, one of the people I can remember specifically of, like, essentially, like, well, what am I going to do? Like, what if this gets automated? Like, this is what I do every day. And I've had some other reactions like that now. But this week, I sat down with a data team and went. down that road with like some AI workflows and you know some and this like data as code and then

using AI with BI as code and some other kind of as code things and I didn't get that explicit reaction but it was going there and it is a different experience for people where you're used to being the technical person to sit down and automate this other manual part of the business and it's really like some script flipping yeah to be on the other side of like, like, looking around like on this, there's like three, you know, like three analysts types on this call, like looking around being like, but like if they can do that, like thinking to themselves, you know, I'm kind of guessing here, but thinking to themselves, well, what am I going to do? They need all three of us now? Yeah. I think that's a big deal. It's a huge deal. And I will

say, look at who's excited about what's happening in data and AI right now. It's vendors and executives and directors are very excited, right? Exactly. Yeah. And how are data analysts feeling, I don't think the excitement is quite so strong when it comes to the people whose careers have been, have been built on implementing these systems. And so I think that's, look, it's a legitimate concern. I think if you look back in history, the idea that BI people in 2010 had this dream that we would, through BI, we would eliminate Excel. And like, did that happen at all? Like, we had the ability to automate a lot of what kind of users do in Excel. And if anything, there's more Excel today than ever.

And so, BI is an avenue to download to Excel. I mean, what's the most popular button in every BI tool, right? It's the export button. Yeah, for sure. You know, I'll tell you, good data, the export button itself is even an embeddable feature. You can embed just the export button into one of your other applications. Yeah, go all the way, right? So I think that, look, there will be an element of that, okay, that like, I remember having

that experience to sitting with end users trying to automate their spreadsheets and them clearly having this visceral like this is my job you can't take this from me exactly but let's be realistic there are many elements i believe of what if you're a tableau expert and your whole career is built on on your expertise in tabl your ability to manipulate that table user interface to get it to look just the right way i think there's a lot of what you do today that a machine is going to be doing in relatively short order right so my question question to that person, I get why that's really scary. My question to them would be, is working for a week or two weeks or a month on a dashboard

and then delivering that dashboard and maybe people use it and maybe they don't? Is that really actually that great of a job? It might pay pretty well, but do you really love it that much? Moving the pills around on Tableau? Like, is that really you want to do that for the next 20 years? I can tell you I didn't when I did a similar. role in Cognos. I got sick of it after about five years. And then I said, I want to move out of this. And I think that, look, what these systems are going to allow us to do, I think I predict that

as a B.I. person in particular, your job is going to become a lot more about interacting with the people who consume your analytics and understanding more deeply. What are they actually trying to do. And then in conjunction with AIs and other systems, figuring out the best thing to deliver for them. And to me, that's a more exciting job. I personally like digging into my consumers' problems and figuring out what they really need more than I like the technical question of how do we make this dashboard look just the right way. Right. Yep. So I think just really quick on that. I think that the trouble with the GUI thing and then, you know, and then AI changing a lot of that. And I think I can relate to some people that are in this

weird middle ground because it's really where I was 10 years ago. So I was doing BI work and got more technical and did DBA and like ops work after that. But you do if you, and Tablo was one of the, like when it very first came out like a decade ago was one of the tools I used. And you, and if you use it enough, you do get to this, like flow state with the tool. Yeah. It's like, it's special. Like, it really is, like, and people are like this with Excel, too.

And at one point, I was that way with Excel, probably not as much anymore. And there is something, like, really fun about that. And I think if you're there and, like, tablo's my tool or Excel's my tool or whatever, and you have flow state with that tool. And somebody's saying, like, look, the future of this was, we're going to split this. You're going to have to, like, learn all these command line tools and, like, get good at context engineering and, like, this, that, and the other. and to you it's going to feel like I'm not that person

or you're going to have to like go interact with users all day and do requirements and blah you know like that sounds terrible too I think there are some people in that middle ground that like it's going to be it's going to be a little rough because like they don't necessarily want to like do all the frontline requirements gathering like business interaction clarifying problems part and they don't necessarily want to like really dig in a little bit deeper technically like get good at prompt engineering context engineering tooling setup data platform stuff.

Yeah. And I don't know how to help that. Well, I would say, though, I don't, I mean, and I don't want this to come across as, you know, as overly negative. But I think that person would struggle in their career eventually anyways because the tools themselves would change. If you have that sort of attitude where it's like, well, I really just like flexing this one muscle that is like larger than most other people in this sort of very singular.

Yeah. And I don't really want to learn the business. and I don't really want to learn new technology. Like, it doesn't matter. You can replace AI with anything, you know? Yeah. Like, say the company had decided to like rip out Tableau and put some of it. Right.

You'd have to be the same learning. Look, Tableau, to be clear, Tablo itself is going to, is trying to replace you. Yeah. Sure. Yeah. It's not just about the tool. Yeah. But I think, but there's a class of people.

I think we've all probably worked with people. And I think that's going to be a challenge. And it's always been a challenge. like, for example, like the Cognos thing of like, man, you've got to switch companies a lot, only find ones that use Cognos, as Cognos may decline some. It's going to be tricky, but you can kind of do it if you really care about Cognos and just transfer companies and move to like a utilities industry for more regret. But like that class of person, which isn't like, not a ton of people that way, but I think it's going to be a harder for

it. It's going to be harder for them where you could have made it happen. And I now don't know if you can do it. Listen, I 100% understand how. those people are going to feel. When I made the decision to get out of the Cognos world, it was the hardest thing for me was actually leaving that world behind. Because that was my professional identity. Yeah, for sure. I was a, I was there, you know, it's funny to say, but there are like a few, there were a few famous Cognos people in 2019. A friend Paul, who literally goes by the name Cognos Paul, like he's one of them. But I was one of them too. I would go to a conference and people would write, because I had a YouTube channel that focused a lot on Cognos

tips and tricks. So people would be like, oh my God, I learned how to do this thing by watching your YouTube. And it was my whole professional identity. It's really hard. It's a really hard transition. And so I completely understand anyone who's looking at any BI tool or really any piece of technology that's looking like it's going to radically change in the next few years and saying, like, I don't want to do this. I 100% feel for them. But this is coming. So, you know, like you need to.

What would you tell yourself, if you give that version of yourself advice of like, hey, you're about to make this transition? Like, you got to do it. Like, is there anything that comes to mind of like, man, if I'd only known this, it would have been easier? Yeah. I mean, I think the first thing I would say is, don't stress so much, dude. It's going to be okay. Right. But I think, like, you know, there are things that could have made it easier.

I think, so one thing that that may be hard for B.I. people in particular is, and I think we, I said this earlier, but I, like, a lot of B.I. people are allergic to code. They just, you know, they're just allergic to it. And look, there's no way around learning at least the basics, right? Luckily, that you have now these AI helpers who are going to do a lot with you. I don't want to say for you because you're going to have to learn. Right. And if you can't tell. at all what they're doing that's going to be a problem but but they will do a lot with you and help you get there so it's just vastly easier if you're a bi person and you're like i don't know any python and sure right um i would tell myself to embrace the coding thing a lot harder and earlier in that process you know and i think the other thing would be to like to really try to spend more time experimenting with these technologies, it's just easier and easier than ever. If you're a BI person and you hear someone say like something about DuckDB, you know, you can spin DuckDB up on your MacBook and start messing around with it. Like the barrier to that is so low now. Like just go for it,

right? Yep. And it's going to be scary, but, you know, if you can learn some code and learn, like I think a lot of the future, you know, I think we're heading towards the future where there will be some degree of like guided assembly of front-end B-I systems, including, you know, like in-browser databases with in-browser visualization. And if you learn these fundamental technologies now, you're going to be in a pretty good place, I think, in five years. Yeah. Yep. Yep. Yeah. We're going to take a quick break from the episode to talk about our sponsor, Rudderstack. Now, I could say a bunch of nice things as if I found a fancy new tool, but John has been implementing Rudder Stack 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 rudder stack has really been one of my team's secret weapons. We can collect and standardize 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 rudder stack at six years and going. Yes, I can confirm that. And one of the reasons we picked rudder stack 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 rudder stack 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. I want to return to a statement that you made, and this can steer us a little bit towards talking about good data. Yeah. But you talked about, you know, who's getting excited about this. And we had a guest on recently, actually a former co-hosts of the show, Kostis from Tapef AI,

who said, you know, the interesting thing about this is everyone in technology is really used to disrupting other people. Yeah. And, you know, they're getting disrupted, right? And it's like, whoa. Wait a second. Yeah. We created the monster and it's attacking us.

But, you know, you said directors, executives, etc., are getting excited about this. Vendors. Vendors, for sure. Why are they getting excited about it? And I want to hear that, you know, put some actual feed on. that from the perspective of you know who's a director who's interested in buying good data why are they excited about it yeah yeah i think so good data in particular our lane in terms of analytics platforms is we're really best when you are building out a suite of analytics products that you're

offering your customers right so like this is what you would classically call embedded analytics but I think it's moved way beyond what people think of when they hear that term. They think, like, oh, I'm going to eye-frame some charts into my app. Yeah, right, right. Yeah, yeah, yeah. And that's not what it's about at all anymore. And so there's really two, I think we're lucky. The people, because our customers are using us in many instances to generate revenue,

what these directors and executives are seeing is the ability to build exciting new products. that have an AI component that help, you know, we do a lot in financial services. So we're talking with someone right now about like portfolio, AI driven portfolio rebalancing, right? And just looking at your portfolio and saying, okay, here's where you're, you know, having the analytics to, first of all, present the portfolio to the end user in a comprehensible way and then say like, okay, here's what your investing goals are. and here's where your current portfolio is out of line with those goals.

Here's a plan that we can execute over time to get you in line with those goals, you know, do you want to execute it, right? That's kind of their vision. And that's a revenue generating thing. Either directly through subscription services or a serious competitive advantage versus other investment management providers. And so that's really exciting. What I hear about, too, though, and I think I actually wrote about it on LinkedIn today

because I was a little frustrated with a cold DM I got from someone trying to sell me. It was like, hey, my AI product will replace your product managers. And so that's the other bucket that's out there. Like there are executives and directors who are like, oh, I can cut headcount. And I think that's probably true to some extent, and it will be more and more true over time. But I think the people who are going to win are not the people who just see this ability to reduce headcount, but rather the people who see the ability to produce new and better products and to be more competitive. competitive in the marketplace.

And at good data, that's mostly what our customers do. So I feel really lucky that those are the conversations I'm having, that it's not about like, oh, can we get by with 20% fewer financial analysts, but it's rather like, how do we use this technology to out compete our competitors? I love that. And I love also how, you know, it's interesting because one of the first really major pushes in BI when it comes to, you know, when it comes to AI and how it is changing the spaces, okay, you have a chat pod and you can ask

it all these questions. Yes. But I think hearing about the types of things that you're enabling companies to do where the ultimate manifestation of this is actually just an awesome product experience for the end user is right on. You know, it's like, okay, the AI kind of disappears and you're able to do something that kind of seems magical, right? Like, wow, this would have been really hard or would have taken a human, a long time or a lot of effort to do to sort of rebalance your entire portfolio relative to your goals. And that's really cool. I love that. And I mean, you think about because, yes, every BI tool has a chat bot that you can say, like, show me revenue over time and it'll generate a line chart, right? I mean, that's really table stakes. But the ability I

describe, so one of the things that's really exciting to me about it, actually as a BI person, when it comes to the interfaces we're building and in the chat interface I think on the one hand it's easy to say like chat bots whatever but on the other hand those chat bots capture something that as a bi person I always wished I had and I never did and that is intent right so so so the just knowing what my users were asking was something that I could you could never know you would sit down with a power user and say like please you know show me what you do but but actually the ability to have this auditable, queryable log of all the questions. Yeah, yeah, yeah. I mean, it's incredible. And in that particular example I gave, it's like, you know, the end user's ability

to articulate, or the consumer, I hate saying end user nowadays, the consumer's ability to articulate, okay, here's my rebalancing portfolio, and then you can look at it and say, oh, that's too aggressive for me, right? Or it's too conservative for me. You know, I want to have more in stock

and lessen bonds or whatever it is. That goes, and then to do it, That goes so far beyond what BI tools are traditionally doing that are just focused on that internal use case. That's just like, how do I get people to charts faster? That's valuable, but the real value is in these interactive systems that display data, help you understand data, and then take actions. Yep.

Let's talk a little bit more about the software engineering data gap. You mentioned this earlier, John, I know this is something you think about and talk about a lot. But what are the, you said, getting closer to code is some advice that you would have given yourself. But Ryan and John interested in both of your opinions because you're doing this work every day. What are the specific gaps? Like, what are the really, what are the big ones and what are, and maybe even zero on just a couple of technologies or tools that you think are the big ones? Yeah, I mean, so from the front end perspective, I think, I would look at learning, I mean, just bluntly, like I would, you need to understand what a YAML file is

and how to interact with it. You should have some understanding of like document storage and retrieval. You should know React. I would learn. And I would learn Python. I mean, it doesn't have to be React, but that's the most popular one amongst our customer base of good data right now is React, right? And the reason I would learn these things is because I think that I think in pretty short order, it will not be terribly difficult to develop a front-end application using technologies like these and with AI assistance. And so understanding them, I think, is going to be quite important. And if you look at the BI tools in general, they're all adding SDKs and React libraries and all of this sort of stuff. We got lucky at good data. We've had them for five, six years now,

but there's a race to add these to the front end. And there's a reason for that. It's because probably the future of the data front end is less about going into a BI suite, and it's more about someone has curated a data experience for a particular use case, and the end user is going to go into that. And what's that going to be built on? It's going to be built on the same fundamental technologies that the rest of our applications are built on. Yep. Yeah. So this is super top of mind for me based on some conversations I've had recently, and it's funny because I was walking through the solution with a team the other day. And I just found myself like just, and it was on a video call. So you're like trying to gauge. You're trying to like share your

screen and also like gauge like people's reactions. It was just challenging it in and of itself. I just had a suspicion like, I don't know if they know what I'm talking about. So like rewind a little bit. Like are you familiar with this concept? Are you familiar with this concept? Got some crickets. You know, and essentially what it ended up being is so this context was using AI tool. AI tools for analytics engineering, essentially. Yeah. So I kind of glossed over things like, so you know, you'd set up a lender this way,

and I'm like, do you guys know what a lender is? And then like, go back. Like, okay, SQL fluff, this is what SQL fluff is. It can, you know, format your code. It can, you can set rules for how you want. And just, like, went through, like, what is a lender? It needed to do that. And then, like, you know, fast forward some more

and then talked about, like, tool calling and MCP servers and had to kind of go down into that. with some rabbit holes, which they didn't have context for that either. So there's just a lot of things like that. Let's call it environment set up, which in Devland, like, let's say circa, I mean, it's been over 10 years, like, when Docker, like, really became a thing. There was a lot of, like, dev experience stuff, especially around that, like, Docker time where everybody's, like, reinventing dev experience around containers and then

further, plenty of iterations after that. And with, like, CICD pipelines, when those, like, were first a thing. But that missed data. Like, it almost completely missed data. I don't know why, whatever reason. Maybe because of all the, like, vendors with heavy influence with the GUI experience. That's probably a lot of it. Yep.

So since all that missed data, like, as I sit down and talk with teams, like, that's often where I have to start, especially for like that analytics and data engineer, like, some of them maybe are more familiar, but especially like that analytics engineer or like trending toward the analytics engineer and then trend toward BI. Like that, whatever skill set you call that, I don't know what the future of that is. separate conversation probably but that skill set like you have to go back and explain and talk about some fundamental things to get to like okay and then like now we're at a spot where you're excited at the beginning of this conversation like hey I want to use AI to like make things and

you're like well let's rewind here and then after an hour and I've completely lost you this is you know this is where we need to be so that that's a challenge and I think it's going to be a challenge for a while John you are 100% correct something happened in the early 2010s, where the entire tech industry zigged and data zagged. And it is just, I mean, just things like basic CICD on many data teams, they don't practice it. They don't know how to do it. Until very recently, the BI tools.

You can version control. We don't have to do CSED here. Right. Just version control. Well, you know, I, like, I mean, a lot of BI tools don't, there's no undo button. Right. You do deployment, and there's literally no undo button. Yeah, I mean, I just couldn't agree more.

Like, teams have to learn this. This is the, we are behind in data. And I will tell you, the closer you get to the end user, the more behind that person is likely to be. And I haven't, you know, I don't know that I have a theory that explains it. It might be just that the quest for self-service led the vendors and the teams that implemented those vendors to really make an assumption that everything's going to be UI in the future. and like we can't have any coding because the end user is going to build everything, the consumer is going to build everything, and they're not going to code. Yeah, I do think that's a big component.

I think cost and democratization of technology was a big thing as well. So let's just think of an example, right? So 2010's, early 2010s, the data warehouse is just entering the scene. Yeah. Right? I mean, today it's just like, okay, and, you know, everyone spends up snowflit. You can just do analytics without even really thinking about it. That wasn't true back then.

And so you had major vendors who were sort of driving the warehouse and BI. You had the huge UI push from the major BI vendors because all the other stuff wasn't democratized. But then on the software development side, you mentioned React, right? It comes around in the early 2010s and had a slow start but is over. open source, supported by one of the big fangs, and, you know, in a couple of years, become sort of the de facto way to, like, do software engineering and then by the, you know, by the end of that decade run, even though the hacker news comments would suggest that there are like wildly different opinions, you know, on how to do things right. Generally, it's like,

okay, if you're building a modern web application, like most of the world would say, okay, here's how you do it, right? You have a database, you have these APIs, you have React on the front, you have CICD, you know, we're using TypeScript, you know, just, it's like, okay, this is just kind of how you do it, right? And whatever the variations are is fine. But that sort of open source drive for all of that, you know, even if you just think about the React ecosystem, was a huge force, right? But it took a really long time for data warehouses and other tooling to get to the same level of ubiquity at a cost that made it really easy for anyone to do it. And it's maybe not really there in some ways.

no way. I mean, it is, it is, it's mostly there. But the other factor, too, that I experienced along the way was this, like, data's odd. Data at most companies, there's a lot of companies and it's funny to watch it. Like, okay, we're going to have centralized team and belongs in the IT org. It's like, okay, great. And then you've got, like, back in the day, you have like database administrators and some more technical people. And then like, and then you had some BI people, but they reported into IT, so they got sucked into that stuff. And we're generally bad at requirements, typically. Okay, it's like, oh, this isn't working. They're not getting things done that are valuable for the business. We're going to rip out some of those people, have them

report to the business. And then you're going to split it. You're going to have like, all right, the most technical people, the data engineer, modern, you know, DBAs back in the day, data engineers today, like they're in IT. Then we have these analysts floating around. They're in marketing and supply chain and blah. So we're going to do that. And then there's communication issues with that, with handoffs and blah, blah. Then you're going to like further decentralize it and we're going to buy like a gooey tool, go no code. we just want to IT you just like tell us where the data is we'll connect to it and we'll do our own thing in tablo land so you have all those iterations of it right and it's like no wonder we're in a we're in a spot that we're in yeah yeah we don't even know where its home is like we don't even know where this thing should live yes exactly and that's that's really the story of the front end right of data and it's just like the further the closer you get to the source applications the more it seems people are comfortable with this engineering approach but yeah I mean look that I I sometimes call that the 2010's theory of value for data,

is like, how can we get as many people as possible to learn how to use the self-service BI tool? And then it's like an emergent process. Out of that, value will emerge. Yeah, right, right, right. And it didn't find insights and discover new things. Exactly, they'll just, and it didn't happen. And so you asked earlier, well, why did you make the move

kind of out of implementation? And part of it was because I didn't believe in that story anymore. And I wanted to know what was coming next, but it was pretty clear that people had made massive investments in these self-service BI platforms that were still really only used by power users. And the idea that, oh, look, everybody in finance can just build their own dashboards and pull their own queries and all that sort of stuff, you know, it didn't catch on. It didn't deliver the value that people had thought. And I will tell you, I talk to a lot of executives at companies right now who are looking at the tools, the BI tools they bought in that era,

and they're saying we spent $10 million over the course of a decade on this, and what did we get out of it? For sure. Totally. Well, we are close to the buzzer, but I have a non-data question, or maybe non-data question for you. Okay, so from the stage to data, if you weren't working in data and you couldn't work in data at all, what would you do?

That's a great question. I mean, it's easy to say, well, I'd just be a successful playwright, right? It'd be one of those 40. One of the 40. It's easy. Well, with AI, that's really easy. Yeah. You know, I, it's a great question. I find, I mean, I always wanted to be an author of some sort, right, since I was a little kid. So if tomorrow morning it was like, you can't do this anymore, I'd probably take a run at that. That's a hard road. You know, the other thing, I mean, I've been really, this is kind of weird, but I've been really looking into like, I feel drawn to doing something in the real world. You know, like, I don't know what that thing would be. Like, I got it in my. my head recently that my wife and I should buy the laundromat in town and just run that you know or I'll tell you here's an interesting one my I don't know how my son did this but there's this YouTube channel it's the BBC archive and he found this he's seven this kid he found this documentary about the last lighthouse in Britain that was run by like dudes who lived in the lighthouse it's

and it was in the 70s so this lighthouse is now fully automated right like everything else But there was just something so appealing about this, about watching these guys, like, clean the lens by hand and light the gas lamp, you know, to keep the ships coming in. And so I don't know what it would do, what it would be, but like something where I'm doing that, right? The modern day equivalent of wiping down that lens, if I can tell you, like, that's the next career. Yeah, I love it. I love it. Well, Ryan, it's always a pleasure to have you on the show. love the conversation, and we'll have you back again sometime soon. Yeah, thanks, guys. It was a real pleasure to be here.

The Datastack show is brought to you by Rudderstock. Learn more at rudderstack.com.