This week on The Data Stack Show, Eric and Kostas chat with Aron Clymer, the Founder & CEO of Data Clymer. During the episode, Aron discusses data from his role as a consultant around issues of warehouse visualization and implementation, helping businesses make data-driven decisions, current trends in the industry, and more.
Highlights from this week’s conversation include:
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Eric Dodds 00:03
Welcome to The Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You’ll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by RudderStack, the CDP for developers. You can learn more at RudderStack.com.
Welcome to The Data Stack Show, Kostas. We love talking with consultants on the show, because they get to see so many things on the front lines. And they cross so many technologies, especially the ones that are vendor agnostic, implementing all sorts of technology. And today, we’re going to talk with Aaron climber. He founded Data Clymer, a really interesting consultancy focused on, you know, warehouse visualization stuff. But he was really instrumental in turning Salesforce into a data driven company, which is absolutely what I want to ask him about. You think about Salesforce, I mean, they’re so successful. And I just want to know, what is it in for them to go from being not data driven to data driven? I mean, that sounds weird, you would think that they would be out of the box. So that’s one and then if Aaron is so kind, like I just want to ask him if he has any good Marc Benioff stories, because, you know, there are lots of those. And he was there for a while. So that’s what I’m going to ask him about. How about you?
Kostas Pardalis 01:30
Yeah. What I love about consultancies that they have seen enough out there, to summarize, use cases, but also edge cases. So I think like, it shouldn’t be like to talk about what patterns exist out there, building their infrastructure, and also share some, you know, weird stories, like some weights, cases, some things that you really can’t like to hear about. So that’s what I would love to talk about with him. And of course, like also to listen to the Salesforce stories.
Eric Dodds 02:15
All right, well, let’s dig in. Good. Welcome to The Data Stack Show here. And thanks for giving us some of your time. Oh, yeah, absolutely.
Aron Clymer 02:23
Thank you for inviting me here.
Eric Dodds 02:26
Okay, we’ll start where we always do, give us your background and tell us what led you to starting data climber?
Aron Clymer 02:34
Oh, yeah, I’ve just been so passionate about the data space for so long. I had been in corporate America for 2025 years, before starting this and I just realized kind of dawned on me finally, that I’m never going to be a true expert. Don’t get into consulting, I was at Salesforce and Pop Sugar for 10 years, two companies, two stacks to sort of business challenges and datasets in 10 years, and I thought, I gotta do 50 of those in a year, you know. So that’s how I’m going to be able to walk into the next company or the next client and be able to say, I know exactly what you need to do in this situation for this business problem you have in data. So I’ve just started Data Clymer, I was in a systems integration, data warehousing implementation firm, and started that six years ago and just have loved the journey ever since.
Eric Dodds 03:28
Very cool. And could you describe just like a couple of recent projects so that we get a sense of, you know, what’s the I know, having done consulting, there is no typical project, necessarily, but maybe just a couple of examples of like, clients and projects that you’ve done recently?
Aron Clymer 03:44
Yeah, sure. We’re in a lot of different verticals and industries, simply because data warehousing overall is very applicable across the board, right. And sure, we can do this almost everywhere. We happen to get into the major league sports, and actually, all of sports. Early on, I think that our Cisco giants were our fourth client. Oh, and the there, it’s a very interesting story that I think does expand across industries, but most major league sports teams are on a third party, you know, MSP managed service, something, you know, they’re not doing their own stacks, and they’re all feeling a lot of pain or not being able to customize the way they want or bring in the data they want. So it’s no there’s just this big need out there to own their own destiny, you know, own a stack, do whatever you need to, you know, never get stuck kind of thing. And so that’s resonated across the industry. So we’ve been to a lot of different sports teams we implemented for the Las Vegas Raiders, the Vikings, six or seven other teams and so we’ve done a lot including working out directly. So that’s been just kind of a fun, it’s almost an edge with it’s not, I don’t want to label us as the sports at aside because, of course, they’re fun logos. And they’re they’re fun projects to talk
Kostas Pardalis 05:04
about. Outside of that. A lot of
Aron Clymer 05:09
Gosh, payment providers, banks, financial services and trying to think of a really interesting one to talk about. Yeah, across the board, a lot of high tech to you. I mean, I come from the Bay Area and high tech, and quite a few COVID based as well, sort of, you know, health tech as well, colors. And that’s been really interesting just to see they have a whole platform that sets up testing sites for medical testing, and COVID tests would be a good one, of course, they cover and just being able to really drive all their again, operate more their operations through data, which isn’t a topic I’m just excited about in general. So yeah, a lot. Just a different Great. Pete’s coffee is a great one, too. That’s all inventory operations. More of a West Coast Coffee Company, if you’re from the west coast, but nationwide, technically.
Eric Dodds 06:00
Yeah. Okay. So I have to ask on the sports side of it. I mean, I don’t know a ton about sports. But are you dealing with, you know, if you think about a franchise, you sort of have like player data, but then you also have, you know, let’s say like ticket sales, maybe more of the business side of the house? Are you dealing with both types of data? Or is there a specific flavor?
Aron Clymer 06:20
Yeah, the first thing I always have to say is this is not the sexy ball sexy Moneyball, but that aspect. Yeah. And so it’s not the plaintiff side, it’s all the business side of these teams, its sales and marketing, that it’s all about fan engagement at the end of the day, which is why that translates across the board, right? It’s essentially customer engagement. Yep. No, that approach is very applicable across industries. For sure, it makes total sense. Okay, well, let’s,
Eric Dodds 06:49
so a lot of questions about current stuff and what you’re seeing on the ground. You know, because one thing I love about consulting is that you get to see such a wide variety of problems at a wide variety of different business models and all the tools out there as well. But I’d like to rewind, so you spent a really long time at Salesforce. I think you said it was seven or eight years at Salesforce. And, you know, as we were talking before the show, the main thing was really helping the company become more data driven in a number of different ways. And we’re talking about a huge scale here, even though you know, it’s an even larger organization now that you said you worked with, I think it was 450 product managers, and give us just an overview of kind of what was the state when you entered? And then what was the state when you left? And then I’d love to pick your brain on you know, how you actually drove that journey?
Aron Clymer 07:49
Yeah, absolutely. Indeed, yours does seem like a lifetime for a lot of people these days. One organization. Yeah, I came here in 2008. And there were about 3000 people at the time, and the company in the company, who I had a feeling they weren’t using data as much as they could be. And sure enough, they were using data at all actually. The first thing I did there was actually build a predictive model against their 30 day free trial dataset. thing I’ve done, I can predict, with some, you know, some kind of percentage accuracy, who’s going to convert, you know, which customers are going to convert on a 30 day free trial. But in the first five days, there’s some interesting usage data, right? Sure enough, yes. There was a signal there in the first five days, you could get a much better idea of who’s going to convert, I brought that to the head of sales and presented my findings. And the answer I got was, Well, why would we need data? We’ve got a huge sales force, we call every prospect on the phone, and we talked to them. So I don’t need data to help me. That was it. It was a great example of what sort of the headwinds I was facing. I’m like, okay, yeah, you know, there’s no, no real need for data because we think we’re doing everything as well as we can do. So, yeah, it was a lot of over eight years of having discussions with mostly prior, mostly dealing with the product usage, data sets, I was dealing with product managers and product more and talking to them. But that bled into sales, marketing, customer success as well. There’s just having a lot of conversations around, let’s get out of the vanity metric, sort of mentality of always having up into the right number of users using my product, because Salesforce always is up into the right as Salesforce grew 30% year over year, and I think it still is, you know, since day one since 1999. So growth has not been the problem for Salesforce. But finding metrics that actually make a difference and using data that actually is going to be actionable, was the challenge. So it’s more about having the right conversation about what is actionable data, you know, and getting people to really think through. If I had the answer right now in front of me, what would I do? And if I could figure out what I would do then it’s worth measuring and more Finding out what is the answer to that business problem? And what data? Do I need to answer that question?
Eric Dodds 10:05
A lot of that, those kinds of conversations. Yeah, for sure. And it’s so interesting to hear, you know, you think about a company like Salesforce, and you’re like, Well, surely it’s data driven, right? It’s so great to hear that, like, we call every customer on the phone, we all need data. So what did it how to put you? I’m sure there’s an infrastructure side to this. Right. So was there any existing infrastructure for BI? Or, you know, what were the what? Maybe this is a good way to ask this question. How are what were the product managers using? Right, because, you know, 450, product managers across that many products, you know, what were they doing to get any intelligence on, you know, what they were trying to accomplish? Yeah, right.
Aron Clymer 10:52
I mean, when I got there, and unfortunately, one of the handcuffs of being in Salesforce was, it took a long time to get cloud technology internally in there. So I mean, I started in 2008. And we were on an Oracle data warehouse with business objects, all this legacy stack that was very slow and very, took a big team to maintain and operate. And using
Eric Dodds 11:14
Salesforce, this whole thing, though, like, like them, you
know, slightly hypocritical, is super hypocritical. It came down to trust, customer trust being our number one value, and they really wanted to make sure they had tried and true technology internally, it was super trusted. That was the reason. So that means that I could see that, you know, from a business strategy standpoint, but unfortunately, it just meant that we had to use technologies that have been around for quite a while, right? Well, no cutting edge. So for me, it was about the patterns. And, you know, luckily, that is actually the key, I think, to doing it right and doing it well in data warehousing specifically patterns, design patterns, best practices, you know, that hasn’t changed all that much, has changed a little bit. But, you know, a lot of those patterns that we developed on an old legacy stack still apply today in terms of best practices. So it’s great to really go home, those you know, get those really down, especially when you have poor performance, right, that’s when you have a lot of efficiency, you have to, you know, build into the system. So I enjoyed, like coming up with, how are we going to use this legacy stack? How are we going to do it? Well, and when it came down to it, when I started it was this data warehouse with very, you know, no real standards on how you’re gonna ingest data or get data, or build metrics or even model your data very well. So we just had to come up with a lot of standardization around data ingestion, specifically. So we had to deal with 400 product managers. And my team was only 2025. People. I mean, you know, to scale that we had to go to a product manager and say, let’s talk about your actual metrics. Once we understand what those are. Now, here’s how your team can instrument all this and the products, we capture all this data, and just build frameworks that make that really easy to do.
Eric Dodds 13:04
super interesting. Okay, and what was that, like, when you left? Like how, you know, what, where’s give us a couple examples of, you know, the ways that product managers were operating with data that they, you know, the they weren’t when you started?
Aron Clymer 13:20
Yeah, well, first of all, I guess I shouldn’t say from a technology standpoint, probably my biggest win from the stack itself was to be able to get Tableau in there and replace business objects as much as possible, because business objects, I must have trained 400 people on that tool. I don’t think any of them ever use it very much. I mean, the usability was pretty bad. The largest AP project, but product, but Tableau, as most of us in the data, professional world know is much easier to use. So that was great to be able to train a lot of people in self service, their data using Tableau, at least at the time. And so but what was more important, I think, was really just the fact that everybody, including again, a lot of people outside of products knew how to ad hoc query their data and get answers, you know, how to explore the slice and dice on all the dimensions they cared about. And then build data products actually out of that. So we built things like an early warning system to detect customer health, essentially, the warning for unhealthy customers, let’s get them back healthy, healthy, that was really more for customer success, right. But it was all based on product usage data from the product team.
Aron Clymer 14:30
And then, of course, the product roadmaps being data driven was a big thing, and making sure that a lot of product managers were making those data driven decisions. And then the third thing we did that actually really had a pretty big impact was just internal advanced analytics. So for instance, we would have a couple folks on the team who could do predictive modeling and we would build a predictive model not necessarily to predict the future, but just to analyze the system and understand the drivers of success of something regression will tell you, you’ve identified your target. What are you? You know what success means? It’ll give you the top five reasons why that success is being achieved at certain customers, for instance. And so we actually did a bunch of analysis that led to whole new products being built with the results. So pure internal analytics, not operational production models or anything, but it is super useful.
Eric Dodds 15:24
Yeah. Did you do any sort of cross product stuff, right? Because you’re collecting it from all these different products. And I mean, the interesting thing about the Salesforce ecosystem is lots of different products you can use or you have the CRM at the center of the big ecosystem of products, like very inquisitive. Did you do anything on that front?
Aron Clymer 15:45
You know, that I think maybe the closest thing was to develop whitespace analysis where you really were looking at each customer and understanding where the whitespace was, meaning what are all the products, they’re not using? You know, what are they using? Are they not? What are opportunities to actually upsell, cross sell, or at least fill out the picture for customers? But not necessarily like well, you know, how one product usage affects the other or? Or any of that young? Anything?
Eric Dodds 16:11
Yeah, super interesting. Okay, last question for me, because I’ve been monopolizing. Do you have any good stories about Marc Benioff?
Aron Clymer 16:20
Oh man, I wish I did you know when when they were smaller I thought were his he’s kind of be walking around here somewhere ah, you know, I have nothing new good things to say about Mark DOT I mean, if of all of the successful mega successful CEOs like that, first of all, he founded it what kind of a founder can take it to 100 plus billion right? Really stand up guy found in creating the foundation he created the 111 model get back 1% of profit and time and, and created this huge foundation. I just, you know, just really Yeah, just nothing but respect for the guy and what he’s done. And how he was he stayed out of the headlines either, you know, he was very stand up, respectable person that just drove the company to discuss that success. But nothing, no amazing stories about him. I liked the eatery through great parties. That was
Eric Dodds 17:15
Well, thank you for thank you for doing a Salesforce deep dive Kostas, take it away.
Kostas Pardalis 17:21
Thank you. So Aron, you mentioned that you certainly do. consultancy, because you are hungry like to learn Warren, like to see more use cases out there and like, become, let’s say, like an expert much faster. Alright, legs, in the space. So I’d love to hear what you’ve learned and sort of try to turn these into some kind of like patterns, right? And to do that, like, my first question is above, like the people who are coming, like missiles that are coming in, they are asking for your help. Right? So what are they asking for? Like, what’s the most common, like, let’s say a product that you see out there. It’s like people coming and saying, oh, like, we don’t have a strategy, right. And we will like to implement data strategy, we will like to start, like identifying the data that we can use lives in the right infrastructure, all that stuff, or you see more of like a modernization. need out there where like, you have businesses that they already do something, but they feel like, okay, we probably need to update a few things, if you will like to stay relevant to, okay, these are like, just like two examples. Hopefully, there are more and better ones. So I’d love to hear what you see out there.
Aron Clymer 18:52
Yeah, it’s really interesting. I know, as I think about even what you’re saying there. And I think about how I’m always surprised at how, and maybe that instant sales horror story all over again, how, you know, companies are using data a lot less than you think. And so we are maybe surprisingly more working on the table stakes sort of basic stuff, you know, it’s just get a data warehouse, running and self service, get data into people’s hands. Not even advanced analytics, not, you know, predictive modeling necessarily, let’s just get a really great data model that really is a 360 view of the business for the customer. to curate that really well with the, again, data modeling and best practices there. But let’s just self service this data like that it still remains the number one use case and it hasn’t changed in a long time. So what we do more normally is to work with mid-sized companies. So a lot of times they don’t have something and we’re bringing the entire stack to them for the first time. Another good quick just sports example, there’s big time In. So a collegiate sports conference with 28 sports, they didn’t have a data stack at all. So we’re building a full stack for them. But the innovation there is that all the schools are going to use it too. So it’s going to be this shared environment that actually no other sports organization has anything like that even the Major Leagues don’t have a really like shared environment like that. There’s still sharing files with SSH SFTP. But so, you know, it. Unfortunately, I don’t have these amazing sexy stories of all of this advanced stuff, because it really is just getting data in front of people. You know, thinking about what you said about from a pattern and from the business side to even that is pretty broad, you know, it depends some of our clients really want or, you know, they’re focused on the financial data, and we just need to get financial data in front of people, they just want to actually create a p&l in a BI tool, even, you know, get out of their financial tool. This, the pattern really is, let’s get out of all of our analytics capabilities of our SaaS applications. And let’s get into a BI tool where we can do a lot more and do whatever we want, and a lot more powerful. Now we have some clients where we’re getting their inventory data is a mess, they just need to get on top of their inventory data. For others. It’s absolutely marketing bait. Case study in general, a lot of marketers need a better marketing system. And some digital transformation, some let’s get it in the cloud and go full cloud and migrate. Well, we have. But you know, it’s just across the board. There’s just a lot of almost every department in a company we work with, right? sales, marketing, finance, and audit
Kostas Pardalis 21:34
backed. Yeah. And did you see this transformation happen, like, stays, let’s say in the bargains, or is it more of like, something global that happens like in the company, let’s say, let me give you an example. Like make it similar, like, they usually like finance coming and being like, are like we need behind instead of doing everything inside the SAP, it’s for whatever reason, or it stays there, right? Maybe it’s like an opportunity for you to expand the accounts, obviously, but or you see more, let’s say, broad projects where companies are like, we want to democratize our data. So we don’t just want to take the data out ASAP. But we also want to make sure that everyone inside the company has access to this data. And they can figure out one way or the other, like how to get funding from that.
Aron Clymer 22:30
Yeah, we usually do start with a department or two, however, because it’s mid sized companies, and not necessarily large enterprises. The good news is that we are able to convince them to make sure this is an enterprise data warehouse solution. It’s a central data warehouse for the whole company, we might start with one department, one data set, but even then my stories about you know, Salesforce or the product usage data set is you, the entire company can use that, you know, and find value from that. So even if we start with one department, the data is still consumed by a lot of different departments. So that’s the story we try. We are huge fans of, again, keeping a single source of truth in one data warehouse. And actually, maybe that gets back to your question about patterns, again, is that that I think, is the important thing, right is to really centralize all your code as much as possible, govern it, keep it governed and controlled. So it doesn’t become a mess. Because trust if you lose trust in your data, that’s the number one killer of data projects. You do have to put a lot of thought and governance into what you’re doing. Yeah,
Kostas Pardalis 23:31
Allen percent. And like you mentioned, that were like the the democratization like a couple of times, like, what does this mean, in the context of combining it to
Eric Dodds 23:41
me, that means
Aron Clymer 23:42
as many people as possible who should have access to the data should have it in a self service way, they don’t have to be technical. They have a tool cloud tool that they can use to then query the data, hopefully ad hoc, and ad nauseam if they want to. So give me a good example, actually back from just before I started this when I was at RAND data, Pop Sugar in San Francisco, b2c company, and we democratized the data across every single department. And my favorite story from there is the PR department. The PR department was two people who were trying to put out as many stories as possible.
And they had a fashion search dataset, so they could actually detect fashion trends anywhere in the world with this dataset is pretty fun. But the only way they could get the data and get a story was to submit a request to the data team, and wait about two or three weeks, get an answer and write a story. It was very easy to get that data set into a modern cloud stack with a cloud BI tool. In that case, it was a looker. And I was able to train them in about an hour and a half how to query that data to find any fashion trend in any place in the world at any time period. Right. So with just that one training, they were then able to produce a story every day. And so it was like 15x productivity because of self service with an accorded pretty simple data set, the idea is to get every employee to be able to do that too, then, and you sort of incorporate data into their daily job, their business process, and make it more data driven. That’s how you change culture.
Kostas Pardalis 25:18
That’s super interesting. Like, let’s say, like, longer, like in culture, just like super interesting. So, okay, making the data accessible is like, obviously, a very important step like you have, you need to have like the data out there available for anyone to go. And I work with a team, we want them to McGraw dice that, but that’s not seen enough, right? Like, you also need people to, like, know, first of all, that the data is there available. And also lets it build the kind of thought process of like, when they come with a new problem, like to go and reach out for the data and see how the data is split, like to help them. So there’s also some kind of, I would say, like educational parts, in this whole process? How do you see this thing working, because you know, like many times more like with both of the DynamoDB, all the DAG, like split on the show like was what was like a look like on the technology side of things. But it’s very interesting what you said about the inside that you tried, like to communicate to the sales manager, right? Like, at the end, no matter what kind of data system you have there. Like it’s people’s problem, like to choke them, like use that, right? So how do we make people work with the data, like, learn how to use the data, find the data. And maybe we don’t have to, I don’t know, but like, I’d love to hear from you, like how big of a problem is, and like how we can solve those.
Aron Clymer 26:51
Yeah, I think that’s still actually a huge problem. And it’s not like I’ve cracked that nut completely at all. But that’s why I always come back to data is hard. Even if you’re an end user, it is difficult, right? It just having the data data dictionary that makes sense is a huge challenge, you know, so that an end user can truly understand what they’re what you’re using, I think there’s always a partnership between a data team or more of a data, more of the data, technical folks, and the end users a constant education. And there’s always a communication there. Because when you want new data, you can’t just use, you know, maybe wave a magic wand and have it available. So you do need some help there. But I think it comes down to, you know, a lot of iterative training, essentially, and getting and building people what they need. And once they get the hang of it, and they do it a few times, they can have some small wins quickly, then they’re definitely going to keep using it. And there’s also just that some people are curious, naturally, and some people aren’t. And if you’re not curious, then you’re probably not gonna use data very much. But, you know, I think small wins quickly. And at the same time, this is kind of what I most love about data is making it as easy as possible. So taking a lot of effort to curate a dataset that is easy to use, right that almost anybody can use. So it takes a lot of work to make sure you got exactly the dimensions and measures you know, whatever you need. And not too much, not too little right to make it consumable. And not boil the ocean. But but also low grain data, I always go back to I’m not I’m not talking about aggregations of data, I mean, have access to the low grain detail data, but make it just as clean and easy as it is to understand how to use and I think a lot of people then finally do start to actually use it. And of course, you need a tool that makes it easy,
Kostas Pardalis 28:40
as well. Yeah, it makes sense. Makes sense. And from your experience, both back and Salesforce today, but like or working in a consultancy. If you could go back to yourself when you went to the sales manager and presented the data, right. It’s what you would say, to make that person back. They’re more successful in like these attempts to use data for some reason because as you said, they’re not people that are more curious, like you have people that like they only succeed champions of using data inside like the company, but it’s not always easy to do that. Right. And it’s very easy, like getting scared on SOAP. While you will translate what’s like your advice, let’s ask these people how they can make a copy and not the ends?
Aron Clymer 29:32
Yeah, that’s a great question. I think if I had been a little more on top of a game plan there, I think I would have said, well, it doesn’t hurt to do a little pilot, right, let’s do a little POC. Let’s get this in the hands of a subgroup of the teams. Get it you know, 2030 sales reps. And let’s see if we can come up with, you know, the top five value and benefits to them of this data in this dataset and we’ll see if it makes sense, let’s try it out. So perseverance is one of our values. I think that’s true in consulting for sure. But it’s true everywhere, right? If you persevere enough, yes, again, get what you’re looking for. And with a group that is a little bit of perseverance to, again, iterate through something, to get an end use, get this in hand in the hands of end users, get their feedback, and see if you can really understand
Kostas Pardalis 30:24
how it’s going to be best implemented. Which function do you think is the easiest one in the company to sell data related? Like?
Aron Clymer 30:34
I think marketers are very data driven by nature, right? And there’s so much usage of that data. And then again, I think product managers as well, I mean, if you’re building any kind of tech products, understanding usage of that product is pretty key. Those two, I think, are most data driven, kind of Yeah.
Kostas Pardalis 30:54
I would actually say the same thing, probably like that and say, sales are like, the less open to that stuff. Like, unless you can convince them that like you can, you know, take the pipeline and make it 10x bigger or something. That’s the way to work with.
Aron Clymer 31:14
Yeah, that’s what I thought. Maybe if you understood who to talk to, who to focus on in your lead funnel, it makes sense. Right? But
Kostas Pardalis 31:22
yeah, it probably has, like, also some suggestions there, because he hasn’t worked like a lot of that stuff. But yeah, I think like sales is, it’s a really interesting silencing function like to go and, like, sell something like that. But what did you think, Eric?
Eric Dodds 31:41
Well, you know, one thing that’s really interesting is the incentive structures really different for sales, you know, then those other functions, you know, if you think about marketing, or product, you know, product is motivated by, you know, planning the roadmap, and then executing against the roadmap, and, you know, perhaps even driving like feature adoption, although maybe that’s, you know, sort of like a growth function within product. And then marketing, you know, certainly a little bit closer to the sales side, and like you have numbers to hit. And so you’re sort of pursuing as aggressively. But really, even in both of those situations with product and marketing, you’re measured on execution, and throughput. And of course, there are key metrics there, right, and feature adoption, or, you know, mitigating churn on the product side, or whatever it is right activation. And in the marketing side, you have traffic and leads, and, you know, all those sorts of things. And, of course, they’re like performance bonuses and stuff. But when you get into sales, the foundational compensation structure is fundamentally different, right? Like your motivation is tied directly to, you know, primarily one vector. And so I think that’s what makes it difficult is like, to focus on marketing, and products, you know, you’re rewarded for measuring, because you’re rewarded on output, right? And so, measuring is in your best self interest. And interestingly enough for sales. I’m not saying like, I’m not, this isn’t like a dig on sales, but there’s just less inherent self interest and being heavily data driven, because that’s not the way that you make money. You never make a lot of money. So I don’t know, that’s my take. But I would also say, I think that’s changing. You know, I talk to more and more salespeople who are coming from highly technical environments, they need to understand a technical buyer. And the more that once you see the power of using data to help you do your job, even in a sales context, like that, you realize how helpful it can be. So I think that’s also changing a lot. And I think that, you know, I see salespeople more and more asking for even marketing data, right, like these opportunities, what channels did they come from? Right? That’s a great question for a salesperson to ask. Not all of them do. But those sorts of things are really interesting, because it can help them prioritize or, you know, sort of rank and do other stuff. So I do think it’s changing. Yeah, I don’t know. Does that answer your question cost us?
Kostas Pardalis 34:37
Yes. So our, let’s go back to live good, like, they can call like, and let’s look a little bit more about like the technologies that you see how they’re being used, like we have, like we’ve talked many times about, like the modern data stack, like the clouds that you mentioned before. What are like some trends that you see out there that are really, really, let’s say, transformative? And I would, my guess is that you will probably be mentioned, like the Cloud Data Warehouse. But together with that what else is out there? Like really enables data democratization and the self service around data?
Aron Clymer 35:24
Yeah, absolutely. Well, yeah, my world is around data warehousing. So I always start there. But you know, in my experience, getting the data, yeah, getting the right tool that is going to expose that data, self service makes it easy, all the stuff we talked about is so critical. And I’ve been actually only used to BI tools so from a BI standpoint that makes that really clean, easy, and you just use the right approach. The first one was Looker, they actually started this company doing a lot of Looker work because Looker is just executing live sequels against your data warehouse. So you get instant query, instant results, again, for most recent data you have, and you can query as much data as you want and add as much details but I love that sigma computing is another one that has taken that same approach. We work with a lot of them lately, because they’ve just become so successful in the market. Again, because of that full cloud approach where you don’t have to be technical, but behind the scenes, the tool is executing direct queries against your warehouse live. But on top of that, it has a spreadsheet interface. Pretty much everybody knows how to navigate a spreadsheet interface. So the training and the UX is a I’m finding this, you know that to be pretty incredible, in terms of how easy it is to get people using the tool. So I like sigma computing, specifically for BI and then they’re doing a lot more kind of blending traditional analytics with some really cool stuff like they can right back to the Data Warehouse. And, you know, eventually people will be able to use it even more like a spreadsheet, meaning they can even, you know, enter in some of their own data and mesh it with data that’s in the Data Warehouse upon really interesting stuff like that. And then this bleeds into the pattern. And the trend that I see that I love is just more and more applications like that, that are running directly on top of the data warehouse. So you have the most recent data you can get. And if there’s new data that comes in and instantly available to you, and you know all of that. So in the marketing space, we work with another company, flywheel software that allows marketers to create audiences, run campaigns, do AV testing, do some AI on top of that all directly on the Data Warehouse. And so you know, I love seeing these full cloud approaches that are right on top of your stack. And, you know, the future of all of this is running more and more of your business directly off the data warehouse.
Kostas Pardalis 38:01
But that’s the most exciting trend I’m seeing out there
Eric Dodds 38:06
I have a question for you, by the way, I agree. Super exciting trend. Something that costs us and I have talked a ton about what that means for let’s call them maybe real time packaged SAS analytics tools. So Google Analytics, you have a whole class of product analytics tools, you know, a lot of sort of Amplitude, Mixpanel, etc. which are really useful because it’s plug and play, right? I mean, everyone complains about Google Analytics, but the reality is, it’s ubiquitous and ubiquitous and marketing, because it just, it automatically produces all the reports you need. And if you think about, like, if you if I said, Okay, could you go rebuild all of these views that are in Google Analytics and segment like, that is an unbelievable undertaking, right? And arguably, like not necessarily worth it. Obviously, there are severe limitations to Google Analytics, which is why there’s a huge limit to the warehouse. So there’s this interesting gap. What do you think the future looks like for sort of the package SAS side of things?
So like, you know, Google Analytics is much more limited in terms of data, flexibility, querying data points, then being able to do whatever you want in your warehouse. But at the same time, it has all these reports out of the box, right? Like, well, it doesn’t. There’s a point at which it doesn’t make sense to pay an analyst to rebuild something that’s already a prepackaged interface that people can just use out of the box. Got and so they’re just like, I agree that things are moving towards running your business off the warehouse, but, you know, there still is a really big gap like Starting out of the box with Looker, where sigma or Tableau and building an entire suite of Web Analytics is like, yeah, Google Analytics. Yeah, that problem is actually being solved with templates. And both sigma and Looker have a way for a third party to create all those templates, and then just have them plug and play at any client. So I think that’s where this is gonna go, like even Google. I mean, I haven’t looked lately, but they may have already created this, right, a Google Analytics Suite, that if you’re using sigma or using Looker, press a button, then it’s instantly all those dashboards and reports are actually there. Because it only took one person to develop those, and then they can deploy that anywhere. So it’s basically a migration of a lot of that logic, but it’s a one time migration that everybody can take advantage of. So that’ll be where it goes in the long term is that, you know, that’s going to make a lot of sense. Because the reason again, you made the good point is why would you even want to do it in your warehouse in the first place? Well, there’s a lot more flexibility in the way you do it. You can bring in data from any other data source right into that report to your Google Analytics report, essentially, and tack it on. And you can do that in 10 minutes, rather than the five days that would take you to do in Excel. So yeah, so I think that’s the you know, all of the forces are driving people to the Data Warehouse because of efficiency. And, you know, that’s the logical place and all that work that’s done. And so, all these problems will be solved through templates such as Dacian migration of certain things and so forth.
Eric Dodds 41:28
Yeah, I agree with that. I think it’s, I think the challenge that I’ve seen a lot of companies face and that process is that once you get full configurability you start changing like one of the interesting things about package SAS is there are guardrails. Right? And so they sort of forced you to build meaningful reports, because you are limited in what you can do. We move those limitations, right, you go down these customization pads that are like, okay, you know, it’s getting way too crazy. But yeah, I agree. That’s, that’s super interesting. Sorry, Costas. I jumped in, interrupted. Oh, no, no, you
Kostas Pardalis 41:59
should more often. Like, Mike, my blade. Please go on. Yeah, so we weren’t discussing the technologies. Any worries. I know, that’s like a hard thing to ask from someone like consulting. But let’s take it like data warehouses, right. Like, there’s a lot of evolution and innovation happening there for like starting with Redshift. And having today like, products like Snowflake. So what do you love to work with? And what would you have, let’s say, suggestions on how to improve? Yeah,
Aron Clymer 42:42
and it’s still a really amazing day. One of the exciting things about this whole space is it’s kind of fractured, there are a lot of vendors. And there’s a lot of ways you can do this. And data CLI, we’re here we are technology neutral at the end of the day. So we’re trying to use the best in class solution. So for now, the new best in class data warehouse comes along, we’re definitely going to take advantage of that. Snowflake has just become the almost the de facto for us at least. And it’s not necessarily because we made that choice early on and said we’re only you know, we’re doing Snowflake, they just took over the market. A lot of our clients by the time we even talked to them, which is pretty early in their data strategy, had already, you know, kicked the tires on Snowflake, because it was so ubiquitous. So when I started this, yes, it was a lot of redshifts, Amazon Redshift, and a little bit of Big Query, and you know, it really shifted to Snowflake. So now like 90% of our projects, plus or Snowflake. As soon as I saw Snowflake, I started playing with it. I mean, yeah, it’s just made a ton of sense, you know, to separate compute from storage, never get stuck with scalability, essentially, just to rid of all the headaches, the way I described it all the time was you get rid of pretty much every technical headache of data warehousing, you know, you don’t need a DBA anymore, you don’t need to worry about compression, and performance and scaling and you know, compute event, you can just keep adding compute really easily. You can do it programmatically, even so you can scale it up and scale it down as you want. So all these features and then data sharing across multiple data, Database, Data Warehouse implementations, so that it looks as if the data is in your data warehouse, but it’s actually in somebody else’s data warehouse, like stuff like that, that only the cloud can do. kind of blew my mind. So when I saw this, I thought, y’all Yeah, this is the place to be. And sure enough, I mean, we’ve never had any issues with, you know, Snowflake invitation or any regrets. So Snowflake is BI in, for us, at least is the de facto standard. We and then for data. We mean, there’s really three pieces to data warehousing in my mind. There’s the data warehouse itself, there’s data ingestion, and you could talk about reverse ETL getting the data back out of the data warehouse into systems. And then there’s your visualization or your end user applications, right. So, for the data movement, essentially II Fivetran has always been a very solid product for us. So Rula Fivetran. And we again, I mentioned sigma competing for the BI layer in a lot of those kinds of tools, those DBT as well. I mean, DBT is a really wonderful product to use, I love the fact that they came on the scene and also solve problems that you thought might have been solved by now, but are not necessarily are not in an elegant way, like DBT sort of solves a lot of data
Kostas Pardalis 45:30
modeling challenges in an elegant way. Makes sense.
Aron Clymer 45:36
Sorry, I didn’t mean to interrupt, I was gonna mention back to your hierarchy, our discussion about templates and packages, even DBT is packaged up all these modeling, prepackaged modeling solutions, right? So there’s another good example of oh, you can plug and play a Shopify model or whatever, you know, whatever it is, because these vendors are out there, and they’re standard. Sure.
Kostas Pardalis 45:54
One last question from me about the data warehouses. So you mentioned red seats, big green against Snowflake and you weren’t going through like the features of snow Carson. It makes a lot of sense, like when you compare it with something like the Red Sea, right? But like BigQuery always was and still is, like, quickly, like self service, kind of like the lower house, it scales like, you don’t even kind of like define warehouses, like someone would say that it’s like even more, let’s say, seven lists are easy to use that like Snowflake. Why do you think that it’s like Snowflake? Sorry, BigQuery, hasn’t molars? Like to get more traction broadcasts?
Aron Clymer 46:40
Yeah, that’s a really good question. I think, for me, at least early on, I would always get nervous when I realized it’s still a shared resource. And it’s still a little bit hard to calculate exactly what your computer will be. Whereas the Snowflake, it was just guaranteed. And so there was that there was also just the pricing model of, you know, based on scanning, you know, scan records, and how do you predict what that’s going to look like for your cost model. Going forward, that was a little hard to get my head around, then at least I understand compute minutes in Snowflake, and I can probably get a good idea of how long something should run. And therefore I can try to predict my costs a little better. So I’m guessing I mean, that is literally the non less technical side of things, but it’s really the marketing. They’re the go to market approach to victory. That made it a little more difficult for people to get their head around. It’s my guess,
Kostas Pardalis 47:34
super interesting. showrunners. Yeah, totally agree. Actually.
Eric Dodds 47:39
Eric, all yours. All right. Well, we’re really close to the buzzer here, as I like to say, but one question, I’d love to know. So you said, Okay, two big projects over a decade, I want to do 50 of these a year, you know, 100 of these a year? What’s your favorite part? You know, and I would say, like, of the process, not necessarily the outcome, because, of course, it’s great when, you know, the company says, Man, this is amazing, like, everyone’s logging into their dashboards every day, and we make good decisions. But, you know, that’s, of course, the outcome. And that’s great. And we all love that. But in terms of the process that leads there, what’s your favorite part? Like, you know, if you had to get your hands dirty, going through that journey, which part would you choose to focus on? Oh,
Aron Clymer 48:24
I hands down love, love, the delight on an end user’s face, you know, when they get it, and you’ve taught them just enough to be dangerous with their data, right. And so, to me, it’s that last mile of like, here, I kind of talked about this before, right? Curating that dataset to be exactly what that what a group of users needs, let’s say and it’s just simple enough to use, but also complex that it’s very meaningful. So I love the curation part really, and that you step back farther, that’s really data modeling. You not only enjoy the modeling, but much a little less low of the low level modeling, but the high level modeling that semantic layer, sort of final modeling, where you’re pre pre defining all your joins, and you’re making sure your fan outs are not going to cause problems and all the things that you worry about in terms of making a dataset bulletproof. So a user can’t shoot themselves in the foot, they’re always going to get the right answer. Easy to use, I just love that final step of curating the set, and then training them up and showing them how to use it and then watching their eyes light up when they say oh my god, I can do this and this, and I can slice and dice however I want. And I you know, I answered questions ad hoc, that’s probably why I focused on data democratization, too, but I just, I do like my last mile of the journey the most. Yeah, that’s super cool. I kind of view that
Eric Dodds 49:42
as a great example of sort of art and science, right? You can almost think about it as architecting or it’s like, okay, well, you’re building a house. But if you’re trying to bring someone’s vision to life, you know, you have the ability to, you know, shape it in a way that sort of brings them delight as an outcome. which is, you know, really specific businesses and users, depending on their use cases. So very cool. Well, Aaron, this has been such a great show. Appreciate the time I learned a ton. I’ll look for an invite from Marc Benioff for one of his parties, because I hear that they’re really good. You got it. All right. Well, thanks so much for joining us.
Aron Clymer 50:21
Yeah, thank you very much. It’s been a pleasure to talk to both of you. It was a great discussion. Thanks for talking shop. I always love it.
Eric Dodds 50:28
You know, one thing I think that is interesting about Aron’s story that we’ve heard so much on the show, and this is gonna sound so cliche, but you know, the people side of things is always the hardest. And he referred back to that over and over again. You know, just in terms of like, how do you make progress in an organization in terms of becoming data driven? And it’s, I guess, maybe my big takeaway, this is how I would frame it, Costas. I love talking with people like you’re in because they just don’t talk about the tools when you ask them about becoming data driven, right. Like, it’s so simple for them, right? It’s like, I mean, I need to ingest data, like, how are you going to do that? Well, it doesn’t really matter. There’s lots of good tools, right? You need to warehouse data. It’s like, well, how are you gonna do that? Like, well, I mean, like snowflakes, great, but like, everything’s pretty good, right? And then it’s like, well, you need visualizations, like, Well, how do you do that? And they’re like, We have a couple tools we prefer but like, everything’s pretty good. I just love it. Like, for Aaron, things are kind of simple. You know, you have ingestion you have, you know, storage, you have modeling, and then you visualize it so that people can make decisions and like they’ve changed preferred vendors over time, but like, he wants to help people make better decisions. And it’s really refreshing to just hear the perspective of him, all the tools are awesome. And maybe we prefer some of them. But really, it doesn’t matter what you use, it’s actually about getting a good data model and delivering, like, really clear visualization and dashboards so that people can make decisions. So I love that. I think it’s so great. Yeah, 100% like, and I love to focus on, like the human factor, to be honest, like, because that’s what it is. Right? Like, you can have the best technology out there. But
Kostas Pardalis 52:23
you need the culture and like to vindicate the people on how to use this data and like how to think in terms of data, and they make decisions or like, make it part of their work, right. I think like, especially like the example that he gave about his own experience backup, Salesforce, and it went like to get it Wilson’s sales were like, okay, yeah, that’s cool. But why do we need it in case like,
Eric Dodds 52:51
we call every customer? Yeah, like, so.
Kostas Pardalis 52:55
I think that was like a really good part of the conversations that we’ve had, and yeah, it’s one of these things that you can need a consultant. You need someone with like, you know, implementing, but it’s not part of the system itself. Right. So that observer and like, they can see what’s going on, and like they can see what the bottlenecks are. And yeah, I’m looking for a coffee mug on the show when I like, discuss more about those.
Eric Dodds 53:22
Yeah, if you’re a vendor in the modern data stack, every problem, you know, is a nail to the modern data stack. Nice. It’s really refreshing to hear from like, like Aron, who brings it back to basics. So thanks for joining us. We’ll have many more great guests on the show, and we will catch you on the next one.
We hope you enjoyed this episode of The Data Stack Show. Be sure to subscribe to your favorite podcast app to get notified about new episodes every week. We’d also love your feedback. You can email me, Eric Dodds, at firstname.lastname@example.org. That’s E-R-I-C at datastackshow.com. The show is brought to you by RudderStack, the CDP for developers. Learn how to build a CDP on your data warehouse at RudderStack.com.