This week on The Data Stack Show, Eric and Kostas chat with Jay Henderson, SVP of Product Management at Alteryx. During the episode, Jay shares his career journey from accounting to data analytics, and his role at Alteryx. They discuss Alteryx’s analytics automation platform, designed for end users without coding skills. Jay explains how Alteryx democratizes analytics, providing a visual representation of data transformations and handling inputs from various systems. They also discuss the challenges of building low-code/no-code systems, the importance of collaboration in analytics, the potential impact of generative AI on analytics, and more.
Highlights from this week’s conversation include:
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Eric Dodds 00:05
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. We’re here with Jay Henderson from Alteryx. Jay, welcome to The Data Stack Show. We are thrilled to have you on air. Great to be here. All right, well, give us a quick background. And then tell us what you do today. Ultrix. Yeah,
Jay Henderson 00:40
Hey, everybody, really great to be on the show. I’m the SVP of products here at Alteryx. I lead product design and strategy across all of the different Alteryx products. I’ve got 20 years of product experience at mostly analytics companies. Before this, I was at IBM and I ran the Watson marketing division. I worked at SPSS, a predictive analytics company for a text mining company. So I really spent my whole career in the analytics space, being a product guy. And so pretty excited to talk you guys through some of the things we’ve got going on here at Alteryx. And to talk about analytics, and you know, what’s happening today, and some of the things that are coming in the future.
Kostas Pardalis 01:22
That’s awesome. By the way, J like, honestly, ways that I think about, like Alteryx is that it’s kind of like one of the most successful examples of like, tool like or product that’s like, no code or low code, right, as we call it, like today, but others has been doing with these, like for a while. And I think it’s like a great opportunity for us to get deeper into that, like, why we need that, how it plays well with or doesn’t play? Well, maybe we’ll see with other tools out there, right. And those are what’s what the future looks like, in the AI world where it’s probably like, you know, like kinda like the definition of like, no code at the end, you just, you don’t even have to type with saying you can speak to your assistant or in like, do something magically. And I think we have the right person here today to talk about these things. So I’m really excited about it. But how about you, what’s in your mind? And what are a few topics that you’d love to go deeper into today?
Jay Henderson 02:31
Yeah, I mean, I think you said it, I think no code analytics will be really fun to talk about, particularly as a way to democratize analytics to hundreds or 1000s or 10s of 1000s of people in your organization, the scale can get really interesting and really impressive. I’m also really interested in talking a little bit about how analytics can be a team sport, and how the different people doing different types of analytics, maybe can work together. And then I’d love to dig in a little bit around generative AI and some of the exciting things that are coming down the pike, based on, you know, all of the crazy new algorithms that seem to be, you know, getting updated on a pretty regular basis here.
Kostas Pardalis 03:13
That’s awesome. What do you think? I think we should go and start talking. What do you think?
Eric Dodds 03:17
Let’s do it. Let’s dig in. Let’s talk so much about today, you know, especially, you know, Alteryx, sort of being maybe one of the biggest companies that you know, many people haven’t heard of. So I definitely want to dig into that. But give us your background, how did you get into data initially? And what was your journey to Alteryx?
Jay Henderson 03:42
Yeah, I mean, I started off my career in one of the most exciting fields possible, which is accounting. If anybody on the show has ever been an accountant, you know, maybe you’ll agree, maybe you won’t. But for me, that was really the doorway into doing things with data, getting involved with technology and technology systems. And pretty quickly, you know, realized that I didn’t really want to be an accountant. But all this data stuff was pretty cool and pretty fascinating. And then made, by my way to a bunch of different data companies over the years, whether it was sort of dealing with data from one particular type of system, I worked at a web analytics company, dealing with particular types of algorithms, I worked in, you know, a place to date made data mining and statistics software, later on a text mining company, then focus on analytics and one function. So marketing analytics and customer analytics, and then found myself here at Alteryx. You know, kind of, were the natural landing spot where it kind of all comes together, being able to do analytics against all kinds of data and different techniques, and it’s pretty fun, pretty exciting stuff.
Eric Dodds 04:49
Very cool. And I have to ask a question. So when we were catching up before the show, I discovered that you liked curling because it is a hobby in the US . Someone who started out their career as an accountant has worked so deeply in the world of data. I just need to know personally, you know, when you’re throwing the stone, how much are you trying to be really calculated like an accountant? Or are you? Is this an area where you’re just doing this? by feel? You know? Yeah,
Jay Henderson 05:18
I mean, like, like any good analyst, I sort of think like, you’re trying to balance the two things, right? Like, you’re trying to go with your gut and your intuition. But also, you’ve got, like, a bunch of facts about how’s the ice tonight? Is it bumpy? Is it fast? Is it slow? Is it pulling in one direction or another? So you know, I think, you know, I try to bring that same perspective that I have in analytics to, when I’m out on the ice trying to curl well said,
Eric Dodds 05:46
is that what kind of separates you think like a good, someone who’s really good at curling from someone who’s, you know,
Jay Henderson 05:52
I mean, look, I’m still relatively new, I’ve only been doing it for a couple years, something tells me that just like, look, practice is the key to actually getting really terrific.
Eric Dodds 06:05
Yeah. Love it. Well, let’s talk about Alteryx. So Alteryx, is a huge company, just sort of ubiquitously used for analytics use cases, especially, you know, sort of across the enterprise. But not sort of commonly mentioned, you know, as you know, you know, sort of modern data stack ish, tooling, analytics tooling, what can you tell us what Alteryx is, and what it does just give us a 101 on the platform?
Jay Henderson 06:35
Yeah. So, you know, we call Alteryx, an analytics automation platform. You know, I think historically, if you talk to him 10, or 15 years ago, we’d be talking about PrEP and blend. And we’ve kind of grown beyond that, and added some more interesting and sophisticated analytics. But look, the basics are, you’re trying to take data from multiple sources. So you gotta have inputs, and you want to bring them together, you want to do various transformations, you want to clean the data, you want to make new calculations, you want to do some advanced analytics, and then you want to like put it somewhere, so you want to output it, whether that’s a database, a flat file, it gets sent in an email, you know, and then you want to be able to schedule it and have it happen repeatedly. Because that’s really, while the ad hoc analytics is important, it adds value, being able to schedule it and automated is where you can really scale up the value. Now, you might sort of say, well, that sounds like an ETL tool, or an ELT tool, like, isn’t that exactly what those are just gonna? Yeah. And I think like, if you just look at the features and functions on paper, yeah, like, they’re kind of similar. I think the biggest difference and where you see kind of Ultrix has had terrific success is really with very casual end users. So these are people who, a lot of times, they certainly can’t write code. So the Ultrix has a no code interface, where you get these analytical building blocks that you drag and drop onto a canvas and connect together. So you know, it’s a no code interface, the people who use it are often sort of doing the job of an analyst, but not always, you know, full time analysts, sometimes they’re accountants or tax professionals, or supply chain people. And so, in that sense, sometimes we’re not always traveling in the same circles is, you know, the other pieces of the modern data stack, we’re not, you know, trying to have it be buyers or users, you know, clearly they’re going to be approvers, and a bigger process for us, but the end users are really very casual. And so, you know, we’re trying to help democratize analytics, and, you know, put it in the hands of the people who have the most context about the business. So you’ll see us with, you know, hundreds of 1000s 10s of 1000s of end users in an account helping power, really interesting and sophisticated analytics for them. But you know, often connecting into those big pieces of the modern data stack, sometimes pushing down, you know, calculations into the databases and things like that. So, we’re right there alongside with the modern data stack, but just sometimes with different end users, and slightly different buyers.
Eric Dodds 09:11
So interesting. I want to go one layer deeper to the foundation, in terms of our definitions. And so we talked about analytics, you mentioned democratizing analytics, okay. Yeah. Analytics is one of those terms where, you know, it’s funny, because you can ask them, like, Do you know what, you know, analytics are and everyone was able, yeah. And it’s like, okay, could you define it for me, right? And then they probably stop for a second. And they’re thinking, well, that’s actually kind of hard to put a sharp definition on because you could argue that the transformations that you’re running on the data in an ETL process qualify as analytics. You could argue that building a chart, and a report is analytics, and sort of everything in between So from Alteryx view, can you define analytics? And you have a sort of working definition?
Jay Henderson 10:08
Yeah, I mean, I think I do think it is very broad, right, I think I do count transformations as analytics, I think a lot of times in those transformations, you’re doing calculations, you’re creating a new metric. By combining the different data sources together. Sometimes you might be doing something really sophisticated from a statistics perspective looking for, you know, one or two standard deviations above or below the mean, it could be generating a report or a graph, that’s going to highlight an insight. And I love sort of this idea that analytics is very broad, right, it’s taking that data, refining it, finding insights into the business. And I think it’s just, you know, it’s incredibly important for an entire organization to be aligned around driving value out of their data. And while you need help from specialists, you need to help with the data engineer, you need to help with the data scientists. You know, at the end of the day, you need to empower more people with these kinds of capabilities in the organization and can’t be trapped inside of a small team, especially the bigger the company, the more you need to train and enable them, and give them the right kinds of tools in order to get value out of that data.
Eric Dodds 11:26
Jay, I wanna, I want you to paint a picture for us. And I think a use case might be an interesting way to tackle this. So could we talk about someone who does not have Alteryx? And they need to perform some sort of analytics task, right? What’s their workflow going to be? Both from a tooling standpoint? And from a team collaboration standpoint? Right? Maybe that’s a little generous, right? Yeah. What do they need to go get from other teams? And then what does that look is, you know,
Jay Henderson 11:57
You know, a lot of what we see is, we run into a lot of people using Excel, you know, again, if you think about us, touching very casually and users, that shouldn’t be too surprising that, you know, organizations are relying on Excel to do some very complicated things. And so, you know, in your organization, if you’re looking at the guy who is awesome with pivot tables, and V lookup, and other things, you know, those are people who are great candidates to graduate into Qualtrics. And you’d be surprised at these, you know, sort of very sophisticated things people are building out in Excel, but in the end, wind up being very fragile, you wind up sort of not being part of the enterprise governance that you have around, you know, when kept and how you keep things up to date. And they’re just very fragile and sort of don’t automate very well. And that’s a lot of what we can do in Ultrix, is, you know, take those formulas out of an Excel file, give you a little representation of how data is coming in all of the different transformations happening to it, not have to, you know, remember some complicated formula for VLOOKUP, and put it in something that, you know, can be automated and repeated, and won’t be so fragile, and will be easier to just sort of understand and visualize exactly what’s happening to the data. And so I think that’s, like a pretty basic example of where, you know, a lot of times you’ll see great adoption of all tricks is, you know, sort of those really advanced Excel users. Yeah, yeah, that
Eric Dodds 13:26
makes total sense. You know, it’s, you know, anyone who’s ever, you know, had to, I put this in air quotes, you know, for the listeners, who aren’t on the video call, but so elaborate, you know, on a 450 megabyte Excel file with some pretty gnarly V lookups, and some macros on it. And, you know, and you’re, you know, emailing or, you know, doing, you know, just using an internal network to iterate on that file version control is, you know, underscore one underscore two, makes total sense. And I agree with you, it’s actually astounding, the level of complexity means, people end up almost building software inside of so one other question on that use case. So that was, you know, sort of graduating from Excel. A lot of times, those Excel files that you’re talking about have inputs from other systems, is that part of it? The challenge that Alteryx solves as well, right? Because someone’s getting a CSV export from where they’re dumping it into a spreadsheet and running all their V lookups.
Jay Henderson 14:32
Yeah, the really kind of interesting thing about the inputs that Ultrix can take in is, you’ll see, we’re great at connecting to all the different pieces of your modern data stack. So we’ve got great support for Snowflake and Databricks. And, you know, Trino, or, you know, dremio, or whatever cool thing you’re using, yeah, we can pull data out of there. In our experience, though, sort of, you know, while data centralization projects are amazing, you know, what comes when nobody’s done with their data centralization project. How long have we been talking about, you know, big, centralized data warehouses and making silos? Yeah, like, the really interesting thing about Ultrix is, it can let you accommodate the realities of your data infrastructure and the fact that there’s always, you know, some file that’s not getting updated in the data warehouse, something that sure it’s getting updated. But, you know, the data updates, you know, hourly, in the load into the warehouse is weekly, or, you know, from your partner community, you’re getting, you know, extracts out of their systems that are getting FTP or emailed us somewhere, and you have a really great system to let you know, the poor analyst who has to, yeah, that data and do something with it, accomplish it, write it, because it puts the power and the tools in their hands directly. So they’re not sort of beholden to it, they’re not, you know, they can actually self serve around bringing this to date the data together, you know, cleansing it and pulling it together into whatever output they’re trying to do, in some cases, that can just be loading it back into the data warehouse or the database. You know, in other cases, it might be producing a report. But it gives you tremendous flexibility to deal with the realities of your very messy data, that probably isn’t all actually where you need it to be.
Eric Dodds 16:20
Yep, it makes total sense. Now, I want to ask the question here, because you mentioned in your chat before the call that even just in the couple of years that you’ve been there, the company’s revenue is almost double. Did I hear that correctly?
Jay Henderson 16:32
Yeah. Yeah. So, you know, ARR, in particular, was a little north of 500 million when I joined about two and a half years ago. So with tremendous growth, it’s been a pretty fun ride.
Eric Dodds 16:48
Yeah, I mean, every company in the modern data stack, you know, that’s a startup wants their valuation to be a billion dollars, you know, which is probably going to be a lot harder in this environment. You kind of describe Alteryx as sort of the biggest company that a lot of people have never heard of, from a data perspective. Why do you think that you’ve been in and around space for decades?
Jay Henderson 17:15
Yeah. And, you know, before I came here, I wasn’t super familiar with Alteryx. Right? Look, I think that’s part of what we’re looking to change, I think we are sort of, you know, talking a lot more across an enterprise. You know, I think a big reason for it is, our end users are sometimes a little bit different than the rest of the modern data stack. Right? We’re, while we have data engineers, that use our product, and you know, we have people in it, who use our product, those aren’t necessarily the people we’re talking to every day. And, you know, when we say we have 500,000 end users, a lot of them really are accountants or tax professionals, or supply chain people. And, frankly, their exposure to the rest of the modern day data stack is relatively small, you know, all they know is, you know, it put all their data in Snowflake, or their data science team, you know, has a bunch of stuff they want to access to and Databricks. And so, you know, it’s a little bit, you know, trying to help the people who have problems that our analytic problems, you know, who are being asked to play the role of analysts, but have some day job, empowering them and sort of bringing them closer into, you know, the rest of the modern data stack that I think a bunch of your listeners are probably way more familiar with, and helping build some of those bridges. I think that’s a huge part of our mission. And in, you know, frankly, it’s what’s been driving our success is you can scale up Analytics, you can put, you know, these capabilities in 10s of 1000s of people in your company, or if it’s a smaller company, dozens of people, right, it doesn’t have to be we don’t need the folks to be gatekeepers, we can empower people, we can do it in a way that they can still govern, and, you know, comply with the policies. But we can actually empower casual end users with sophisticated analytics. Yeah,
Eric Dodds 19:09
That’s incredible. Well, I know Kostas has a bunch of questions. I actually have a question for you, leading products. This is just a personal curiosity of mine. Because when you think about a company, this, you know, clearly an enterprise, having, you know, multiple 1000s of users within a single account, you know, so, you know, a lot of companies are going to consider that a massive enterprise account. Yet you describe solving problems for a casual end user. How do you approach that from a product standpoint, because it’s clear that you need to have a hardened product that can withstand, you know, the needs of the Fortune 500 yet, there’s a very human I just get a really strong sense of a human element to how You’re describing your end user. And a lot of you know, a lot of times you’ll think, well, a big enterprise company, like they don’t care about the end user, that you have a very human tone. So how do you reconcile those things?
Jay Henderson 20:09
I mean, it’s fascinating, I would say, we’re very fortunate here at Alteryx, in that we really grew up selling, you know, selling a seated designer to one person in an organization, having them fall in love with it, that person telling, you know, five people in their organization and them telling five friends and and then tell them telling five friends, and it really is what fueled what I think of as the first wave of growth for Alteryx. And it’s creating raving fans for us. I’ve never met a more passionate and fervent user base. And like, gosh, there’s 500,000 of them. So like, there’s a lot of them, like, you know, people have Alteryx tattoos, like it’s, I mean, it’s not. Oh, yeah. Oh, yeah. Oh, what I would say is, we definitely, you know, hit a wall, where sort of the growth started to plateau a little bit, and in part is because you look, once you get to a certain number of seats and organization, you know, it kind of sticks their head up and goes, like, hey, what do you guys do over there? Like, how come you got all these seats. And, you know, really, that inflection points was what around when I started and sort of, you know, having been at a larger company, like IBM, like, part of why I came here was to help bring more enterprise readiness features to the Ultrix platform, and, you know, help put in the, you know, the governance, the SDLC, the, you know, all of the things that large organizations were looking to have in a platform that they were in a scale to hundreds or 1000s, or 10s of 1000s of people in their work. And so, you know, I think a lot of that has been the work we’ve been doing in the roadmap over the last three years. And it helped that, you know, we had that passionate fan base in the companies, because, you know, there was no way, you know, anybody’s ripping that out of their hands, because it was so critical to getting their job done. I think that gave us the room to mature, you know, the backend infrastructure pieces that would meet those needs. cost
Eric Dodds 22:12
us thank you very,
Kostas Pardalis 22:13
Thank you for giving them a microphone. I have some very pressing questions I have to ask. So, Jay, let’s talk a little bit about the product and about the need for like, low code, no code, kind of like interface, like these systems, right. And I have, like, my equation is the following. Like, the problem that I always had with these systems are not so specifically like in the data space, because we can see platforms trying to provide this kind of experience across like the whole industry, right? From building websites to like, doing AI in the mail. And I felt and that’s like, I really want like your, like thoughts here, as a product person, right? It always felt like there is like a very delicate balance between making like a product that’s, it’s easy to use, or turn it into something that looks easy to use, but actually is like annoying to use, because it’s very, I mean, there is a reason that the machines are complex stuff, right. And there is a reason that we use something like languages to program them like the semantics are very rich there. And that’s why we need programming languages. But transforming that into something like a user interface, for example, it’s a very hard task, actually, it’s not easy, you can very easily end up liking a system that is, you know, like, at the end harder to use in a way or like, a very bad experience. Can you help me understand how, as a product person, like you navigate that, considering also like a user who is not technical, right? Like we’re talking about an accountant here. So how do we then these people are into, like a deal engineer in a way, without even them knowing that they are turning themselves into their engineer?
Jay Henderson 24:18
Yeah, it’s fascinating. I mean, I think there’s, you know, a few different building blocks to having a great no code interface to enabling somebody who’s such a casual and user, you know, obviously, we have a particular sort of, you know, framework and user interface paradigm, it is a, you know, a flowchart interface. There are, you know, nodes or building blocks. What I would tell you is, yeah, that, you know, at that core metaphor is important, but I’ve seen other, you know, low code products that have, you know, flowcharts that that don’t work for a really casual end user So some of that is also just sort of, you know, spending a lot of time with the users, watching them use it doing usability studies, having a strong design team, and understanding deeply the use cases that the customers are trying to solve. Because, you know, frankly, a lot of the ease comes from, you know, kind of the interface itself, and it facilitates getting to the outcomes, the user is trying to drive very quickly. And so, you know, we’re fortunate, like, we’re, we’ve been around for a long time, we’ve got, you know, years and years of experience and honing this ease of use that we’ve got in, you know, helping drive the thrill of solving and being able to get to an answer really quickly. You know, the other thing I like to think is that, we also have some really nice, like, escape hatches within the note code interface that will let you do more sophisticated things. So yeah, you can, you know, cut and paste some SQL that you found on Google or, you know, a little bit of Python code, that if you hit a wall, and there’s some, you know, more sophisticated thing, you’re trying to do that, that, that there is a way to accomplish what you need. And that flexibility often can be really powerful. You know, because you’re sort of giving people the opportunity to get past whatever roadblock they have. And so I think there’s a number of different things that you can, that you can bring together into one product to help, you know, keep the ease of use where it needs to be to make sure customers are getting the value out of the thing that you’re using.
Kostas Pardalis 26:35
Okay, that’s great. And one more product related question. So it’s one thing to try and it’s built, you know, like a pretty complex product. But for one persona, right? Like one very specific type of user, which might be like a data engineer might be an ML engineer, like, but pretty much like all these people are like the same. Like they, they speak the same language, first of all, right? Like, their vocabulary is not that different from one to the other. So even when you’re just talking with them, you’re going to hear the same things. But when we’re talking about democratizing access data through, like, no code of local cheese, then we’re literally talking about professionals inside an organization that may be coming from completely different light contexts, right? Like, someone who is coming from logistics, and someone who is an accountant are like completely different people, like they understand the word in very different terms, right. And they are exposed to different tools. They use different language words. But both of them might be using Alteryx. So how do you do that? Because that’s, I think, like, even harder, like to do, and I’m saying that like, as a person who has tried to build the products, and I’ve seen how complex it gets by just introducing one more persona in there. I can’t imagine how it is when the space of personas is actually open. Right? Like, it can’t be literally like anyone? Yeah,
Jay Henderson 28:03
I mean, look, I think the thing that ties all those people together is, you know, sort of their lack of ability to code, right? Like, they don’t know, see, well, they don’t know, Python, but you know, what they do know, they know, Excel, and, you know, sort of, if you can tell them like, well, you know, hey, imagine you take those 10 Excel files and combine it into one, and then you can sort it and you can write formulas to make new calculations. There are, I think, some very, you know, familiar metaphors that can span those roles, you know, from things like spreadsheets, and frankly, you know, that is very much where a lot of those end users are coming from. And so, you know, hey, our formula function, you know, it draws on that experience that people have across Excel. So I do think there’s some familiar metaphors. But, you know, look, I think it also helps to have great training and great onboarding experiences for the customer. And, you know, we provide a lot of example, workflows for how to accomplish different things, you know, that we have a community where, you know, people can ask for help, and advice, we have a program called the Ultrix aces, which are super users and ambassadors of our product that will, you know, answer questions in that community board. And so there’s a lot of things that kind of surround the products themselves, I think, that help facilitate getting people familiar with it. Maybe the other thing I think that’s really interesting is, people are starving for insights. And you know, they’re kind of drowning in data. And so when you can give them a tool that feels intuitive, like, it’s amazing to just watch their enthusiasm, and sort of their willingness to learn how to do things inside the application. And sort of, you know, I think that there’s this you know, this idea we call the thrill of solving that, I think, is fun to watch, unfold, but also very mode Waiting for people and makes them willing to engage and learn how to use some software and learn how to do things like, you know, join a data set together, when they don’t know anything about what a join means. And then look at inner and outer joins and things like that, that are sort of very, you know, data engineering concepts, but exposed in a familiar and easy to use way.
Kostas Pardalis 30:23
Yeah, 100% not super interesting. All right. Okay, we talked a lot about like the non technical people, but usually, like in labs or special, like organizations that are also like technical people in there, and like, part of their job is like to govern the data and like, monitor the data and expose the data, like to the rest of the organization, right. And I think, a big part of like, what was happening like this past couple of years, especially with, let’s say, the rise of like, the clouds, warehouses was like, Okay, let’s get all the data, put them in one place and have like, the data engineers or like the ML engineers, or like data platform, or people or whatever we want to call them, to governing the way the, the access to these data. So okay, the bread and butter of someone, like a data engineer, is like a pipeline, right? Like, that’s what they’re doing for a living in the end. How, like, an environment in a real environment, or like in a company that has like real, I would say, use case around their data, how systems like Alteryx, play together with let’s say, something like Spark or like Databricks, like, the more let’s say, Dell, engineering tooling out there, right? And then let’s look up at the technology a little bit, and then we will talk about the people because that’s even more interesting to be honest. Yeah.
Jay Henderson 31:47
So I mean, you know, one of the things to notice is, you know, I think we’re in the middle of a really interesting inflection point for analytics, if you think about, the old way to do analytics was, you would get, you know, an extract out of the data warehouse, and then, you know, put it in some analytics tool to twist and turn and slice and dice it, that model is really getting flipped on its head, in my opinion, where now instead of sort of bringing the data to the analytics, we’re switching that to be taking the analytics to the data. And so if you look at the investments we’re making around, push down around in database processing around Cloud Native Compute, you know, we’re able to take the analytics to where the data is, and actually leave it inside of the warehouse. As we’re creating new metrics, and doing different selections and sorting and filtering, and all the different analytic things that we need, that we want to do, you’re able to sort of not have to egress the data, you can leave it where it is. And I think that’s sort of one of the more interesting and exciting trends that we’ve seen, you know, in analytics, and so, yeah, we’ll connect into Databricks. Yeah, we’ll push down into Spark. And, you know, we’ll leverage your Databricks unity catalog, if you’ve got that all connected up. So I think there is really interesting opportunity to sort of leverage the existing investments that your data teams are making, and sort of, you know, gosh, you’ve spent all this money on Snowflake, you’ve spent all this money on Databricks, you need all these people to access it, you want to derive the value from it, you want to get the data in the hands of the people who have the context for the data, and how it applies to the business and let them use it and activate it. So, you know, in some ways, I think it’s, you know, Alteryx has been pretty great partners, particularly to the database vendors, where, you know, we’re helping them realize the value of the big investments they’ve made in some of those systems.
Kostas Pardalis 33:44
Yeah, 100%? And how about, like, the people involved? Like how, okay, like data engineers, for example, like Phil about, okay, giving the power to anyone to go and build pipelines at the end, right? That is going to like to run on, like, their systems. So what are the boundaries there between what the data engineer cares about or should care about and what let’s say, the last mile pipeline that a user needs? Yeah.
Jay Henderson 34:12
It’s, uh, you know, look, first of all, what I would tell you is, you know, analytics is a team sport, right? These people need to collaborate. And frankly, I think, as a vendor, the vendors need to do a better job helping these people collaborate, because it’s not, it’s, you know, it’s harder than it needs to be. And, you know, there’s all the people and the organizational dynamics too. And so, you know, I don’t want to make it sound easier or convince people that all you gotta do is buy some software from Alteryx. And it’ll all just work. But I think you actually kind of touched on it, like what sort of best practices around how a lot of companies are set up, which is you need those data engineers building those pipelines. You’re sort of creating the, you know, the data sets that are the source for all those things people are doing in Alteryx, right. And they’re the base things that people are selecting from that they’re combining with their spreadsheets and doing all the downstream calculations. And I think it is tough to know sometimes, which things do you want to push upstream into the warehouse versus downstream, let the end users take care of, and some organizations are afraid to give, you know, hundreds or 1000s of people access to that data or access to perform the analytics themselves. But I guess what I would say is, I think all of us have gotten into data because we believe it’s important to run, you know, running an effective business. And so, you know, what I would say is, I can’t really convince myself that a successful business in the future won’t have every single employee, being a data worker, you know, just like the way we used to talk about knowledge workers, every single person, that company is going to need access to data to get their job done. And the exciting thing about it now is you can still derive competitive advantage from it. And so you can be at the forefront of this trend, and create real competitive advantage for your organization, at scale, if you can provide the right kinds of data and the right kinds of analytics to every single person in the company.
Kostas Pardalis 36:24
Yeah, and why are companies afraid of that? Because like, what you’re describing, and by the way, I totally agree with you. Like, it’s very important, right? Like, how people can create, like an organization without having access to the data. So why are these customers there?
Jay Henderson 36:41
I think sometimes they’re scared for good reasons. You know, there’s regulatory concerns, there’s, you know, privacy concerns, you know, data’s can be sensitive stuff. And, you know, sort of having the right governance structure, I think, is important to being successful with these sorts of efforts. And I think, you know, sort of empowering and users, you know, can feel like a scary thing. I think that the governance angle is one, I also think there’s big skills gaps, you know, from all of these end users, I think there’s concern that sort of, will the users know what to do with the data, once they have it? Will they be able to, you know, sort of create productive insights out of it. And I guess my argument, there would be, first of all, I think, you know, you can create competitive advantage, if you give them access to the data, I think oftentimes, the people closest to the business also understand the implications of the data much better than centralized teams. And I think, you know, the software has come a long way, just in being able to automatically surface insights that are, you know, interesting or important or have changed. And so, you know, I think there’s, we’ve come a long way to sort of lowering the barriers of entry into surfacing insights. And I think those people who have that context are gonna be great at doing it. So I think those are probably the two biggest things I hear from our customers, you know, worried about governance and worried about the skills gaps, but I think there are things you can do programmatically, to put people in a position to be successful with it.
Kostas Pardalis 38:20
Yeah, and talking about like, the gapping skills, I think it’s, like a very good opportunity, like to get into the Gen AI. Yeah. Like trends show. Let’s talk a little bit about that. Because like, in a way, we’re talking about, like, generative AI, like, it sounds like the, you know, the Ultimates, no code, low code, whatever solution, right? You don’t even have to tie, like, practically, technically, if you don’t want to. So what’s Alteryx, like looking into that, like, and let’s try, like, remove the hype from it and keep the substance because there’s also like, a lot of hype out there. And I think it’s good. That’s
Jay Henderson 39:03
One of the things I’d suggest for every single person listening, this podcast is, you know, if you haven’t signed up for chat, GPT Pro, go do it, and play around with the advanced analytics. And it is, you know, pretty mind blowing to use that thing, you can upload a file of data. And you can just say things like, tell me something interesting about my data, or perform some advanced analytics on my data. And the things that it can do, I think, are a really clear indicator for how generative AI is going to impact the analytic space. Now, there’s a whole bunch of problems with sort of that as a model for analytics. First of all, I think, you know, personally no one wants to load up their data to open AI and have the data leave their four walls. The things that ChaCha PD does with the data It aren’t repeatable. So I can’t schedule it, I can’t make it go on an ongoing basis. You know, I think also, it doesn’t have the context of your business, and the things that are happening in your company or your industry or your function. And so there, there will be fine tuning of the models to give it better context. And so, you know, I also think that the asset that it produces, what ChaCha PD does is they wind up writing Python code to analyze the data. Well, that’s great. But if I don’t know anything about Python, cool, I can get an answer. But I can’t look at that Python code and know whether it’s actually doing the thing I needed to do. And so, you know, as I think about the lessons, you can learn from that. I think there’s really exciting things that we’re doing here at Alteryx. First of all, we’re launching a product called AI studio that will let you build your own LLM models, fine tune on top of a base model. And sort of give your company’s data. So the model can be deployed in your four walls. And so companies should be comfortable, you know, feeding their own data into fine tune it. And then we can do chat to SQL chat to Python, but also chat to workflows. And now as a really casual user, I get an asset that it creates where I can look and see oh, look, it really is pulling the right data sources, it really is filtering and sorting. And that formula for calculating the tax implication looks right. And so it’s sort of creating an asset that a very casual end user could look at, and feel good about. And so, you know, to me, I think about generative AI being able to do, you know, chat to Alteryx workflow, or chat to sequel and chat to Python. Now, all of a sudden, like, it’s the thing that’s going to bring all the analysts together, right, it’s going to help make it a team sport, because now we’ve all got this, you know, copilot with us that’s going to help, you know, help us navigate across all these different lenses of, of light or languages of analytics that we want to talk. So, to me, I just, I think we’re at a really, you know, transformative moment. I think this is the most disruptive technology in generations. And I think we’re gonna see some really exciting things in the analytic space.
Kostas Pardalis 42:18
Yeah. Okay. That’s amazing. Eric, I have to give the mic back to you. Because I know when we are talking about AI, like, you’ve gotten like, you have so many questions. So.
Eric Dodds 42:30
Yeah, no, I mean, we’re pretty close to the buzzer year. You know, I’m interested to know, I agree with you, Jay. I really think the transformative potential is immense. But I’m also interested to know where you think the initial failures are going to be. You know, especially as it relates to sort of how MLMs impact analytics, we’re, you know, there’s a lot of promise out there, I think one of the things we can say about AI is that it’s created this ocean of promises, that it’s really hard to distinguish between what’s overblown and what’s real, because it’s very clear that there’s immense potential. So help us understand which promises are maybe not going to come through when it comes to the world of data and analytics.
Jay Henderson 43:23
Yeah, I mean, it’s a really great question, you know, the analogy for me, at least, you know, this feels like the moment when the internet first got big. It does not feel like the moment when blockchain was at its peak hype. Yeah, yeah. Look, it’s not that sort of the Internet didn’t have, you know, we had diapers.com still. So you know, it’s not like there weren’t failures during that phase. But, you know, there were enough successes, where, you know, we’ve now created enduring business value. And so, you know, as I kind of look at generative AI, you know, I think your list of generative experience experiments right now, probably needs to be pretty long, right? And I think you’re right, not all of them are going to be successful. But I think for me, the thing that feels very obvious to me is that there will be lots of successes, and that, that sort of, maybe not everything is going to stick, but you’re going to find some things that are going to just, you know, be incredibly valuable. And so I guess my advice to companies is like you better be running some of those experiments and you better be figuring out how it applies to your industry, your function, your use it as an individual. Because, you know, I think it will be the most transformative thing that happens to all of us. And I realized I’m feeding the hype guys, there’s a reason there’s hype, right, like, there’s there is there’s real promise there of delivering business value. And frankly, just, you know, taking analytics to an entirely different level. Getting not just data into the hands of people and not just insights, but giving them the ability to interact, to create the insights that will impact their day to day jobs and decisions. So, you know, try it out, see what works, you know, and, you know, you have to balance moving quickly with the need for enterprise governance. And, you know, proceed in ways that are, you know, that are going to fit within your company’s policies and things like that. But, man, there better be, you know, a couple dozen experiments, you guys are all running. That’s my advice. Yep.
Eric Dodds 45:37
Wise words, J, still great to learn about Alteryx. I just love hearing. Again, I’m just going to reiterate the human nature of the way that you think about the product and your users, you know, even within large enterprises. I think there’s just a lot for all of us to learn from the way that you’re reading that. And thanks for giving us some of your time. 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.