The Data Stack Show - Data Council Week: How To Do Self-Service Data Analytics and Business Intelligence Right with Ryan Dolley of GoodData

Episode Date: April 15, 2024

Highlights from this week’s conversation include:Ryan’s background in data (0:58)Transition from Performing Arts to Data (2:23)Understanding End Users in Data Projects (6:08)Learning from Failures in Data Projects (8:07)The self-service era (19:50)Struggles of self-service (21:23)The disillusion with dashboards (26:23)GoodData's approach (30:06)Merging wisdom with modern approach (31:50)User experience with GoodData (34:05)Defining metrics and AI (36:35)Connecting with Ryan and GoodData (39:26)Final thoughts and takeaways (41:06)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Welcome to the Data Stack Show. Each week, we explore the world of data by talking to the people shaping its future. You'll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by Rudderstack, the CDP for developers. You can learn more at rudderstack.com. All right, what's up, Data Stack show listeners? Welcome to episode one of Data Council Week 2024. We're in the field at Data Council Austin for the third year in a row.

Eric and Kostas both had conflicts this year, so I'm filling in. I'm Brooks, the producer of the show, coming out from behind the scenes to bring you a few special episodes this week. Matt KG, my colleague at Rudderstack, who brings over a decade of experience working in data science, is joining me to dig into the details. But today, the show is all about Ryan Dolly. He is the VP of Product and Strategy at Good Data, and we are really excited to talk to Ryan today. Got a great show prepared for you all. So, Ryan, welcome to the show. Yeah, thanks for having me, guys. Glad to be here. Absolutely. I'm excited to connect. So first, let's start out how we always do. Will you just give us a bit about your background, kind of how you got into data and got to where you are today?

Yeah, so I've been working in data since roughly 2011 on business intelligence, data warehouse teams predominantly. And in that time, I've done most of the things you would want to do. So I started out at a utility company in Wisconsin on a BI team, building reports and metadata models and that sort of thing, and then moved into consulting for a while. Then I got into the vendor side. I've worked at large vendors and now startups. So I was at Oracle for a little while, and now I'm at GoodData as VP of product strategy. So I was at Oracle for a little while and now I'm at Good Data as a VP of product strategy. So lots of stops along the way, pretty much enjoyed all of them and have really loved having a career in data so far. That's awesome. You left out your career before data, so

went to college for creative writing and had a career before Data in the performing arts. You've written plays. If face value, that doesn't make any sense to me, but I know you're going to tell us and kind of tie a thread together where this makes a ton of sense. Yeah. Could you tell us just about kind of going through that, how you went from performing arts to kind of working in Data? Yeah.

In undergrad, I majored in creative writing and theater. So I've written plays, I've acted in many plays, directed them both as a student and then professionally. I was the assistant literary manager at a Tony Award-winning theater, wrote play reviews for Time Out Chicago magazine, was a script advisor at the Steppenwolf, and started my own nonprofit theater company. That's amazing.

So I did all this stuff, right? And over the course of five years, like everything I just described, my lifetime earnings were about $4,000. So I hit a point, you know, you have to, when you're in the arts, you have to have a day job, right? And my day job, I was at Northwestern University. And I can vividly remember working in this department doing data entry for donations, right? So someone writes Northwestern a check. Back in those days, you had to manually type into a computer system the value of the check and who donated and that sort of thing. So I was doing that. And I remember looking around and realizing I was the newest hire. Yeah. And everyone else in that room had

been there at least 10 years and they all hated one another. And if I didn't do something, I was going to become one of them. Right. And so I, at the time, Northwestern had an incredible employee discount to take, to take classes. So I started taking night classes in IT. My dad was actually in IT and worked in business intelligence. He worked at Cognos. And so I kind of had seen that this was the type of career that could really take care of my $5,000 earnings in five years problem. And so I did night school for that. And honestly, the connection, a lot of people listening probably will say, yeah, there's no connection here. This was a total left-hand turn out of nowhere. But especially

when you get into business intelligence, the business intelligence side of our industry, you're dealing with people and what motivates people and what helps them make decisions. And that is also what I studied in my undergrad, right? Was like, what is it about human beings that leads them to take action? And it's just, you know, instead of doing it with words on a page, I'm not doing it with lines on a chart. That's good. Are there any kind of specific skills that you feel like you've taken from, you know, certainly for your kind of, it's probably easier in the data world, right? We think a lot about content. So like creative writing is maybe easier line to draw but i also i mean i just imagine kind of in data roles to your point of

like how do we get people to take action i imagine there's a lot you take from the performing art yeah that fits in there yeah absolutely i mean just learning how like theater more than i would say any other art is about distilling down that exact question, right? Like why do people do what they do into an hour and a half, two hour presentation? And so one of the biggest skills that I took from it is actually every play, when you're an actor, you read the script and there's what the characters say, right? But what the characters say is actually just a loose roadmap to what they mean. And in the data world, one thing I've found in working with end users is that there is a similar gap

between what they say and what they mean, right? And learning how to read into that to uncover quickly to map the gap between what your end users are saying and what they mean and maybe what they actually need is the type of skill that i have found was very easy for me to do because of this training in the arts and i have found over the years that for many dated people it's actually very difficult. Because they come from a very kind of deterministic, right, give me some requirements and I'll build what you said. Then they build it and the end users aren't happy.

And then the question is, well I did what you told me to, why weren't you happy? And it's because the data team failed in this process of understanding the gap between what they say and what they mean or need. A lot of that where they don't even necessarily know what they want. They have no idea. And if they don't know what they want,

you have to come up with a way to try to figure out what it is they're going to want. Exactly. I often say end users or customers don't know what they need until you give them what they asked for. You know? Yeah. Do you have any just kind of specific stories or memories of like instances where that was just like so apparent? You know, I can tell the most, the one that stands out the most in my mind is actually was, was a lesson for me is actually a time when I failed in this regard.

Okay. Right? Yeah. When I, this was back in like the OLAP cube days. So for those of you who've been in the industry for a while, you'll know what those are. You youngins may not, but I had spent a long time working with our project management office to develop these cubes. And I was, this was, I was very fresh, like straight out of, you know, night school at

Northwestern, applying my technical skills now and i was just building what they were asking for so it was for the project management office at this utility company and when i delivered it it was exactly to spec right beautifully designed multi-dimensional analysis environment and of the 29 project managers like 27 didn't use it yeah they you know they was just like they were like oh that's very cool yeah that's neat i'm gonna keep using my excel spreadsheets and it was because i did not engage my creative mind i did not you know and that was the last time i did that yeah from that point forward and i think everybody has the time they got this is like six months of work right yeah everybody think everybody has the time they got this is like six months of work

right yeah everybody in data has the time they got burned when they when they built exactly what they were asked for and it was a total flop yeah and you don't forget that yeah are there any so moving forward from that experience were there any kind of concrete things you began to start doing of like hey every time i work on a project i'm gonna do you know xyz these couple things to help me make sure you know that i'm not just doing what they say i'm kind of figuring out what they mean and what they need yeah there's a couple things i would do right so there's always the like it's very basic like using the you know the five w's yeah right is it seems obvious but it's not done enough yeah you know and then the other thing i would often ask them i would really try to drill in on like what is the change that you're

like you're coming to us asking for some new data some new analytics whatever it is like what is the change that you're hoping? Like you're coming to us asking for some new data, some new analytics, whatever it is. Like what is the change you're hoping to drive with this, right? What's the end outcome, you know? And it can't just be I want to know blank, right? It's got to be so that I can whatever, whatever that thing is. The other thing I would often do is I would ask them if I could go and they could just show me what they were doing today. Like, let me sit there and literally watch them do it. Yeah. And oftentimes I'd be able to derive insights about,

especially because I'm primarily focused in BI, right? It's really that contact point with the data and the business people. And so better understanding their process, seeing what it is they're doing would often give me insight into how I could design a system that would fit into that, enhance that, help achieve their goals. Right. But doing that over the shoulder was always super valuable. Yeah. Instead of just having to kind of describe here's what. Right. I mean, yeah, you got to get out of like you can't be in a room and have them you know go over requirements list for you and then walk away and feel like okay i'm good to go right you're not no you never are yeah it's um i can't remember who kind of described this but they talk about you know you can get to know a city by looking at a map but until you walk the

streets like you don't actually understand how this place works. Right, right. That is exactly applicable to this situation. And it's something that for you, like you said, it kind of came more naturally to you. How has it been when you try to take someone who's newer at this who comes from more of a data background and you're like, all right, don't just do what they say. Yeah. But they asked for this like how do you teach that to him because i've had that exact same conversation like that moment and i've had that conversation trying to teach people and i was i've had a conversation that went like i'm gonna tell you to do this and you're not gonna do it

until you get burned at least three times yeah yeah i mean there's unfortunately some element of like, you got to touch the hot coal, right? Yeah. To learn what it means. But, you know, it's very hard because our industry, it attracts, it certainly attracts a lot of a certain type of personality. I think people who enjoy the process of certainty. Yes. And finding certainty, right? And the fact that you can, you know, so much of what we do is, is ultimately deterministic, it's about setting up the right flow. And that's very, I like that too, right? That's super satisfying. You know, when it comes to actually, I mean, I have given this advice similar advice to this many times over my career

and sometimes people will take to it often it's hard it's hard especially when you're very fresh and you don't you know you haven't gone through the stuff that you and i have gone through you know one of the things i encourage people to really do is like, if it's very foreign to you, is that actually the arts and literature is the place where you can learn this. Much better than you can learn. If I were to write a book, you know, an instruction manual for techies on how to ask these questions that might be useful, but I actually think that would be less useful than engaging with the arts and literature and learning more about humans and what motivates them, moves them, drives them. Yeah.

And I don't think you can get, you can't get that building out a dag in DBT, right? It's not going to happen. Well, I think there's the temptation from a lot of people to be like, oh, I'm going to read a book on like the psychology of how people work. And it's like, no, go read The Great Gasp. Exactly.

Yes. Like you want to understand what motivates people? Go read great novels. Yes. You will much more not just understand it, but like internalize that for yourself. That is exactly the thing, right? Because that internalization is what happens when you engage with the arts. You can read a book about the psychology of decision making and understand it cognitively, right?

But you need to, both in your own life, you need to personally experience it. That's a great thing about the arts is like this is how we imbibe the knowledge of things we haven't personally done. Yeah. how we imbibe the knowledge of things we haven't personally done yeah you know and so like i tell you know i say all the time to people like every as a data person my recommendation is at a minimum every other book you read needs to not be about data yeah it needs to be a piece of literature or you know a biography of an interesting historical character or something like that. Right. Not a technical manual, not a business book, not a self-help book. Because even that's going to get you where, like, I remember like running a team.

So I had to come up with something because we were centralized. The people wanted dedicated things. And like I came up with the idea of like we're going to organize it like an octopus we're going to have intelligence in the head and also like out in the tentacles yeah you don't read about this stuff you're not gonna like that's where the creative stuff comes from right yeah yeah and that's also like it's a your mind is a muscle right now and so you need to exercise it and it will be if you're listening to this and, you know, you're a young data engineer, you just got out of college, and you haven't been asked to do this in a while or maybe even really ever,

it's going to be hard at first when you pick up challenging novels, especially. But just like going to the gym is hard at first it gets easier you also so on your linkedin profile i think you've got dungeon master oh yes is one of the things there and um i mean i i would say you can find outlets like that kind of do the same thing right yes like whatever you want to nerd out on just like indulge that part of your brain, not just like, hey, at my job, I do data stuff. Right. I need to go how to learn to do data stuff.

It's like, go play Dungeons & Dragons. Yes. Like that's going to help you do data stuff. It absolutely will, right? Engaging that creativity. Yes. Play Dungeons & Dragons. I think also like I picked up woodworking

early on in my career because it was like, it was a creative kind of thing that was also tangible, like having those hobbies there. I mean, like, you know, whether it's Dungeons and Dragons or whatever, but stuff that's just creative and different, right? You know? Yeah, absolutely. And, you know, and to the greatest extent possible i think finding things that like you can also engage with others in right so even if you're doing something like woodworking there are other

people who are into woodworking right you can go meet those people exactly and talk to them and so even the most solitary endeavor has a community around it yeah i mean i know people who they picked up knitting while they're still knitting circles that you think exactly. Exactly. It's good. Ryan, kind of going back to your just varied experience. So on one hand, you have like the very clear, okay, creative arts to data. This is different. But also within data, you've had a very kind of diverse set of experience.

So you've worked at a huge utility company. You've worked at massive tech companies. You've also worked at a consultancy. I imagine there's like very different experiences, but there are also, I'm sure, some common threads of like, wherever I was, like these same problems were there. What are a few maybe less common threads between those different experiences? Yeah. I mean, so the most problem that that we had everywhere was that we've got all this data and we don't we feel like there's something we should be doing with it or it could tell us something but we don't know what that is yeah that's the most like it's almost the

existential question of the industry right and so no matter where I went, I was often solving some flavor of that problem. And like at Oracle, I was on the Oracle sales team. So really my job was to convince other people that we could solve their problem. For about a year, I was doing that as a sales engineer. But otherwise, it was directly addressing it and and uh so that i mean that and then all the implications of that it's been very interesting over the course of all these stops to me to observe i feel like i i can now read the lines of the matrix a little bit because i've been so many

places with the industry how the industry works the hype cycles the you of the matrix a little bit because i've been so many places with the industry how the industry works the hype cycles the you know the technologies that come and go the way they get popular the way they get popular is often has nothing to do with the technology it has to do with the people around it and the social connections you know can make or break one startup versus another even though one may have better tech, right? But they didn't know the right people or they didn't meet the right people. To see all of that has been really fascinating

through all these different stops. Yeah, that's a great perspective. Another thing, so kind of going from, that's such a wonderful, you described as what the existential problem we have on this data what do we do with it yeah your career has been in the kind of bi analytic space so that's getting closer to what we actually do with it and i know you've worked on a lot of kind of self-serve projects oh yeah lots to talk about talk about there. I know Matt has some, you know,

let's call it scar tissue from self-serve projects. But my, you know, my kind of perception is there's a way to do self-serve, but you can't just say, Hey, let's make self-serve like for the whole, like, or can y'all just talk about that a bit like what's what is your philosophy around yeah on self-serve today yeah it's funny you bring that up i i recently was talking with aaron wilkerson who maybe you should have on your show at some point he's a he's a the head of data governance at carhartt and fellow detritus so he and i get along But he shares this exact opinion even more strongly than I do, which is that fundamentally, he would say self-service doesn't work. I would say it often doesn't work or maybe usually doesn't work.

So this idea, especially in the BI world, if we walk down memory lane, what has happened in BI over the course of my career? When I started, BI was about a centralized data team, building out very robust metadata models, and then building out dashboards, or even, I started even in the pre-dashboard, dashboards were new and cool at one point, believe it or not. We built out standard reports with a header and a footer and maybe dump it to PDF, that sort of thing. But self-service was kind of a sideshow at that point, right? And the tooling didn't really exist for it.

It was all about professionally authored reports that might take months or sometimes years in order to really produce. But you knew the numbers were right and everybody could trust it and agree on what they meant. Then we kind of entered the self-service era when Tableau really took off in the early 2010s. And what you saw there was this idea, the theory of value for Tableau, I think, is that you can take a desktop tool, you can put it on an analyst's machine, you can let them go crazy with spreadsheets, and they're going to produce valuable outputs fast enough that it kind of replaces what we did before because it took us so long to build stuff in that era.

So this is kind of the basis of the self-service era. The Tableau came out, Power BI came along, a lot of others came along. It did deliver very pretty visuals very quickly. Yes. It was great at that. Were the numbers correct is the question, right? And oftentimes they weren't.

I was in a situation once where I was working for a firm and we were a heavily regulated industry. We had two different, did business in multiple states. We told the regulators of state A, we had 1.1 million customers. We told the regulators of state B, we had 900,000 customers. It turns out that regulators talk. And we got in big trouble right and that was a result of the theory that we're going to push all analytics content creation to the edge onto someone's desktop and they're just going to get

it right right and this is where self-service really struggles and so and i've seen it struggle very badly it's one of those things i find it's almost like a sugar high in some ways. Yeah. Like the first year when you really embrace self-service, you're amazed at what great things people are building and how quickly they're building them. But then maybe year five, you've got some wreckage in the past and now you're trying to reign things in a little bit and get more control over the data and overall data quality well i think some of that comes from that idea of like hey we don't need to

model data anymore we just throw tableau on it and it's like okay but now everyone's creating their own definition of this metric exactly and now you get to sit in that hour-long debate where marketing and sales and product all tell you what they think churn is. Right. Because they've all got different numbers. Right. Yes. Yeah.

And I've been in those meetings. Yeah. And they're not fun. And so I think, and this is not to say, I don't think we should return to where things were when I started my career, where there was just one data team and they were the only people who could build anything. Right. But what we need to do is kind of rationalize these two ways of working. Where like self-service, and this is something, you know, a little plug here that we do a good data, is like self-service works best, I think, when it has some guardrails around it.

So like if you have a metadata model that has been validated and you've gotten sales and marketing and product together, and you've either agreed on a value of churn or you've agreed to have three different definitions, right? Either way is fine. But that's reflected in the metadata model and that self-service is built on top of the metadata model. Yes, you can bring a spreadsheet in and you can merge the metadata model with spreadsheet data, fine, that's great. But having that foundational piece allows self-service to be more effective, ultimately, even if it's not as fun as the sugar high of everybody going crazy with their own desktop dashboards. So how do you feel about there's some people who want to take that

to the next step sometimes when they're like, okay, it's not just about everyone can build on top of your data model or whatever, but they're like, VPs should be creating everything and using dashboards and finding it all themselves. Because I know that. I've known several data BI people who are kind of like the we should never have to be asked a query they should always just look for it themselves

and yeah i can kind of if there were a camera on right now you would all see me rolling my eyes no absolutely not in fact like the last thing i want want a VP doing is building their own dashboard. Unless that's really their hobby. Yeah. Because they have more important things to do. Right. I really do believe that. And so what I always tell data people, I have met many data people who have an attitude like this. And they get frustrated that people can't do what they view as the very simple things, right? Like I

build out this insane pipeline. You can't go in Sigma and build a visualization. That's easy. What I always tell data people is you need to remember that just like you're an expert in data, these people have an equal level of astounding expertise in something else. And if you were to walk into their realm, you would appear just as clueless as they do when you ask them to build their own dashboards. And it's also like that VP, he doesn't want a dashboard where he has to click through three filters. No. He wants the number. He wants the answer.

He wants the insight. He's like, and I think there's that confusion where like you think you're building a dashboard and that's what they want. What they want are insights when they want them because they already have a day job. They don't want to go do 14 other things. Exactly. Exactly. Exactly. And the dashboard, I feel like there's a lot of disillusion with the dashboard as an interface lately. It's this in BI every two and a half years that we debate our dashboards dead and then two and a half years later, we're still all building them.

It's like SQL. Yeah, it is like SQL. It's dead. It's really not dead. Right. Right, right. You know, and for BI people, like the Tableau, I don't want to knock Tableau. Actually, it's an amazing tool, right? So I'm not knocking Tableau.

Let me get that out of the way. But like the way Tableau kind of brought dashboards, you know, was like a sledgehammer for bringing dashboards as the interface that end users interact with data. Dashboards are very good, but they're really only good for, in my mind, when you have broad agreement on what the things on the dashboard mean and you need to operationally monitor them over time. Yes. Then the dashboard is perfect.

But if you're just trying to answer someone's question, a dashboard is total, it's overkill, and it's actually confusing, right? Right. You just need to answer the question. Yeah, and I think that's a tough one for a lot of, like especially new data people to understand because there's that tendency to want to show not just like what you want, but here's the context, and here's how I got there.

And it's like, calm down. Right. They want a number. Yes. Just send an email with the number. They don't care about anything else. They really don't. And there's definitely a movement I've seen in the data world, I feel, the last couple of years around we should be showing our work.

We should be, I mean, we should be able to show our work. But we should, I don't think we should make that a center point of how we interact with our users. No, I think that I would agree with that one completely. That it's like, I think sometimes it comes out as trying to like prove a value or like, look how hard work we're doing. And it's like, really they just like, they're in the middle of something and they need to know what was this number from last quarter. Yeah.

And when you put all that in there, the number one feedback that I've gotten, I'm sure you got is like, I couldn't really find where it was. Yeah. I spent five minutes going through the email or the spreadsheet or whatever to find this thing. This is what i really needed and like i can't you said if they question it yes we can show them our work yeah but you don't need to volunteer it up front right

right like i would not yeah i would not if you're going to walk into a meeting with important business stakeholders and your idea is, I'm going to start by like, you know, showing them my pipeline and explaining how I did this. I think you're in for disappointment. You know, it's just not. General rule. Tell them what, tell them the answer. Yeah. And then we'll go into some details. Exactly. If they want to know right and oftentimes

they will they'll say yeah well that's especially if the answer is not what they expected right right then they'll say okay how did you come to this conclusion but again the answer to that question is not is i would not get too technical i would explain the logic of how you came to it but i would not you know show them a dag They don't need to see code. Right. They never need to see code. Exactly. Yeah.

Ryan, we've hinted at good data. Yeah. And I think we're, yeah, I think we're teed up to really talk about it now. But yeah, could you just tell us a little bit about what, you know, what is good data? What's your approach? Good data is an analytics and business intelligence platform, right? I'm an analytics and BI guy. And what sets good data apart is really that we're trying to give you almost the best of, when I did that history lesson on BI earlier, we're trying to take the best parts of that earlier era of BI, which was more focused on metadata modeling, data quality, guided self-service, and update that for 2024.

Right. So to kind of bring those ideas into a post-Tableau world is a big part of what we're doing, of our mission. And then the other thing we really focus on is, that's kind of the end user experience we deliver. And then the way we do it also is, BI really has not kept up with advancements in software engineering, certainly, but even in the rest of the data industry, in terms of software engineering principles, CICD, APIs, and automation and integration at the code level. These are, of course, not end-user facing things. But everything in our platform, we have a very friendly, easy-to-use, metadata-driven BI platform.

But under the hood, we have SDKs, APIs, Python libraries. You can access our data through pandas. You can do all sorts of stuff in good data as a developer to make your life easier, integrate it, automate it, scale it using software engineering principles. We're taking the wisdom of the BI ages and merging it with a more modern approach to data engineering,

software engineering. Yeah. Like you said, I think that's one for data that I even know in my career, it's like when I started, there was a lot of like, data is different than software engineering. Right. It's like 80, 90% the same. Yeah. And it's a big difference on that last 10, 20%.

Right. But it's still not something we should be throwing out everything that we've learned from software. Well, I mean, let's just give you a simple example. Like BI tools almost universally have no versioning. Yes. Right. I'm actually thinking of that. And every BI person I've worked with doesn't use like Git or anything.

It's crazy. Yeah. And so everything in good data is YAML and you can integrate it all into your CICD and version it and do whatever, you know, just like you would, just like data engineers do with their work. We need to do it in BI, right?

Yeah, and that's a crazy one when you stop to think about it and you're like, wait a minute, how many changes have you made to the underlying data in this dashboard? Oh, like five in the last week. Yeah. Well, what happens if we need to go back to one of them? It's impossible.

We can't do that. There are whole companies that exist because they make plug-ins that do, like, big companies that make plug-ins that provide versioning to BI tools. Right. And I can tell you, I was working with one of these BI tools. I got hit with a SOX audit. Okay. And then you have to prove whether or not the code has changed and when it changed. And it was not possible. It was not possible.

And then shortly after that, we finally got budgetary approval to buy one of these plugins that allowed us to do versioning. That's also an argument for having some type of documentation of when you make changes. Yeah, right, because we could have been manually doing that. Some people use ticketing systems to do that. I worked in places that have done that. But having something where you can say, we made this change. It was approved by this person or whatever. Right, and so there's the way we were solving that problem,

which was not at all. And then there's a cultural business practice way to solve it. That's good. Best is the software should solve it for you. Most definitely. Maybe you can let the software solve it for you. It's always better. Right.

Tell us a little more about the user experience with good data. I mean, what really sets it apart from other outfits out there? Yeah, well, we talked about, so there's a couple things. The first one is when we think about this discussion we had about self-service, right? Good data has, everything in good data is built from a foundational metadata model. And then, so when you go in as an end user, because of that, because we, you've already mapped out the measures, the dimensions, you've already defined metrics, the joins are all pre pre defined as an end user. I don't ever have to know any of that. Everything is a very clean and easy drag and drop. And because someone like me has built the model,

you can combine any element in the model, any measure, any fact, in any way you want with any attribute, and you're always going to get the right answer. So the user interface has been designed around that. It's very clean. It's easy to use. It is not, you know,

BI tools inevitably add so many features like they just become harder and harder to use over time i think that we've struck a nice balance between having the features people need but keeping a clean airface and then the reality is a lot of those features are really only used by very high level authors. Right. Well, for that, the whole code, the whole platform is API based and the whole, and we have a React library. So like when you hit that point of complexity, you're trying to design an output that is that requires too much, would require a more complex tool than we've built. Yeah.

You can, what we tell people to do and what they do is it's like, this is the point where you should be building a custom data app. Yeah. Right. Not hacking a BI tool. Right. To do what you need. So the BI tool has been designed, like the BI front end has been designed to be easy to use, friendly for business people to do this kind of guided self-service within the walls of the metadata model. And then we give you all the tooling if you're a professional author to go and build exactly what you need rather than you know hacking the bi interface to to get you know try to bend it to your will so so within that do you find any questions around like you know you're still gonna have to go through and define this stuff right you're gonna have to define your metrics yeah and sometimes when you talk with beta like data not data people about this they'll kind of be like well that's

hard yeah can't like can't you do it for us can we get ai to do that for us or something like that yeah yeah yeah yes it is hard the cruel truth about getting ai to do anything is that it really helps to have the metadata defined really well up front before the AI can do anything for you. But yeah, so there is pushback to that. I mean, this is the reality is that taking the time to build the model, a really well structured model up front is going to take longer than just, you know, throw it on, let's rock and roll. Exactly.

It's going to take longer. But what you're going to get at the end is better, more accurate, better performance, easier to use. And so, you know, that's a sales job that we have to do, good data sometimes, or that our customers have to do internally. When one of their business partners says, look, I just don't want to do any of this.

I can't be bothered to tell you what my metrics are. You know, you kind of have to say to them, like, well, if you want to get value out of this data, then we do need to work together. And you do need to help us define your metrics. Do you guys help those internally when you've got it, like the data team, and they're trying to go to that business user, do you guys ever offer any help to them? Hey, let's go talk to them about, this is how you should talk to them. We absolutely do that sort of thing. I personally will get involved.

Because I've been that person. So I have a lot of sympathy for them when they're fighting that fight. Yeah, so that is something we help with. And we get used, there's really two big buckets. We get used to a data platform, general purpose analytics data platform. The other thing that has emerged, because we have all these APIs and SDKs and the user interface is highly configurable, is that we get used a lot for kind of data product embedding. Yeah. So that, you know, you're trying to monetize data or you're adding analytics to maybe you're a SaaS vendor or any industry really where you're now building customer facing data products.

We found that we slide really nicely into that as well because you can, our platform is composable, embeddable, code driven. You can really integrate it into whatever application you're building. That's cool. I do just have to point out, we made it 36 minutes before we even mentioned AI. That is a pretty good deal for a podcast that's being recorded in March of 2020. Man, we are at the buzzer here, Ryan, but this has been just such a fantastic conversation.

A couple of things. You're on our podcast, but you also do a lot of content stuff yourself. So can you tell us, one, where can we find you and then tell us about good data as well? Yeah, so I'm very active on LinkedIn. Just connect with me, Ryan Dolly. And then I do every, not every, most Thursdays at 12 o'clock Eastern, I do a live show with my brother, Eric, called the Super Data Brothers. So Eric is the head of BI and analytics at Michigan State University. So it's like the family business.

And yeah, so we do interviews or we'll do like opinion pieces or audience feedback shows. It's a live show. It's like a Twitch stream for data nerds, basically. That's on LinkedIn and YouTube. So check that out. And then GoodData, of course, GoodData.com

if you're interested in hearing more about that. Awesome. Okay, last question to wrap us up. We talked a lot at the beginning of the show just about kind of exercising the creative side of your brain and the benefit that has in data work. What's on your kind of current reading, current listening list? Yeah, so right now I'm reading Crime and Punishment. What's on your kind of current reading, current listening list? Yeah. Yeah.

So right now I am, I'm reading Crime and Punishment. So I'm kind of on a serious literature kick. I ping pong between sci-fi and like literature, right? Yeah. Sci-fi can be literature. Yes, absolutely. Absolutely it can be. It's just, yeah.

So I read only literature. It's just the question is, does it take place in space? You know, I've been listening to this course on medieval history. So it's like one of those, it's like a 60 hour lecture series. That's been very interesting as well. And I am not reading any data books right now, like zero and I'm loving it. So I encourage you all to join me. Awesome. I would also say that. Go read something that's not data, not technical.

You will be so much better for it. You really will. All right. Well, there you have it. Go read a book, not a data book. Ryan, thanks for joining us. We'll see you again soon. Yeah.

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

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