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Alteryx Alumni (Retired)

The Alteryx Analytics Cloud Platform is here! Automate intelligent decisions, democratize data, and unlock valuable insights all with one unified platform. Corey Spencer, Alteryx VP of Platform Product Management, shares the evolution of data in organizations, and the power of the Alteryx Analytics Cloud Platform which brings together Designer Cloud, Machine Learning, and Auto Insights.








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Episode Transcription

MADDIE: 00:00

How early is too early for spring cleaning? I absolutely love organizing my space. I can't stand it when I have a messy closet, random items are out of place, certain storage bins are labeled, but some aren't. And if you work with data, that's sometimes how it can feel if you have data and data tools scattered about. Alteryx Analytics Cloud can help you make sense of the chaos. I'm Maddie Johansen, and in today's episode, we'll break down the new Alteryx Analytics Cloud, which brings together designer cloud, machine learning, and auto insights, and how the unified platform will help streamline your data to insights faster and easier than ever before. Joining me for this episode is VP of platform product management at Alteryx, Corey Spencer.

COREY: 00:48

So my name is Corey Spencer, vice president of platform product management here at Alteryx. I've worked in data for 20 years, so about as long as SaaS systems and online data have been around. I've worked in the industry at a company called Omniture, at a bunch of startups, at Adobe, at New Relic, and now I'm here having the time of my life at Alteryx. Love it here.

MADDIE: 01:15

Corey's witnessed the evolution of data analytics over the past 20 years. And now that we've arrived in the space where finding actionable insight in your data is key, we'll talk about how Alteryx Analytics Cloud can help you get there. Let's get started. So to kick it off, you mentioned that you've been in the space for over 20 years. I'd love to just start off with hearing about your-- with your years of experience, can you tell me how you've seen the space evolve throughout your career?

COREY: 01:49

Yeah, great question. I think for the first 10, 15 years, there was this mad dash to getting data. We have to catch all the data. It was freaking Pokémon, got to catch it all. We got to get everything. If anyone does anything, we're going to capture it. If there's any return or we need every single keystroke of whatever is happening in this digital world, we have to figure out how to capture it. And we have to figure out how to store it cheaply. It was one side of the spectrum was like, "Can you get all the data, and can you store all the data?" And there was this idea like because if we have it all, there'll be this inherent value to having the data. And on the far other side of the spectrum, there was, "And now we need tools to visualize all that data because now we have all this data, and we're monkeys. We need shapes and colors and look at this graph and look at that bar chart and whatever." And so these were kind of the two holes of analysis or being data-driven is like, "Do you have all the data, and can you analyze and understand that data?"

COREY: 02:58

And I think one of the reasons I'm here or one of the biggest reasons I'm here is during that time, I spent most of my career helping customers collect that data as fast as easily and as cheaply as possible. In fact, if you've used the internet today, you've used technology that my team built to do that. It's on most fortune 100 websites, and it's in most apps that you use, and it's capturing data and storing it more easily than ever before. And then we've got data warehouses like Snowflake and Databricks that are storing that data more cheaply and effectively than ever before. And you've got visualization engines out your ears with all sorts of ways to see that data. But customers aren't getting value from that data because the more holistic they think of as an organization, the more they see that those data were never meant to work together. Your online data for your sales, your campaign data, your accounting data, your logistics data, your shipping data, your employee data, those things are nuanced just enough that none of them are supposed to work together. And in fact, even if you buy four or five of those products from the same company, they probably barely work together.

COREY: 04:11

So there is this huge gap between collecting the data and understanding the data, which is in this preparation area of how do you prepare and get the data to look right? In fact, I think it's so difficult you see people doubling down in the silos. They're like, "I'm a marketing data expert. I'm an accounting data expert," instead of like, "I'm a data expert." So one of the things I love about what we're doing at Alteryx is changing that whole narrative because like, oh, it's so disheartening to watch somebody who's a data analyst have to write a Jira ticket to a data engineer and say, "Hey, can you join these datasets together, and can you clean the data and change this so that I can run my reports and understand the data," and have to iterate five or six or seven times because the data engineer doesn't understand the data, and the business user doesn't understand data engineering? And so kind of creating this level playing field for everyone to work together, I think it's a really cool problem to solve. And it justifies the data collection and makes the visualization that much more powerful. Does that make sense?

MADDIE: 05:19

Totally. I think that the evolution kind of taking a step back and looking at where we were before, I think is super important for the conversation. And also, before we move on to the insights and where we are now, why do you think there was that data gluttony back in the day? It just seems kind of strange now since we've changed since organizations have pivoted. So yeah, why do you think that was the case?

COREY: 05:47

Great question. By the way, I have construction going on at home. So if you hear yelling or banging, it's nothing to be alarmed about.

MADDIE: 05:57

A day in the life.

COREY: 05:58

I think just generally, our instinct is more is better. And I wouldn't even argue that more is bad. Gluttony is an issue of whether or not you can digest enough. So having the data isn't the problem. Being able to use and have the data be actual, that's the problem. So having lots of data that you don't use, that's bad. Having data that enables you to do more is great. And sometimes you have to get the data to figure out what it is you're going to use and what you're not going to use. But when you have these silos of data that are sitting stagnant or are used-- no data should live in an island, but people have to do that because they don't know what else to do with it. So when you're like, "Yeah, here's our online revenue, and here's a completely different graph of our offline revenue, and here's our customers in a completely separate-- our online customers versus our online customers," and they're probably the same people, you just didn't know how to bring the data together.

MADDIE: 07:02

Yeah, yeah. That's a good point. The cost and the focus on getting as much as you could as cheaply as you could. Obviously, that's changed now, but as far as putting things together and being efficient, that still costs money, and that's still a problem. So shifting forward to where we are now, where organizations are focused on finding that value in their data, I want to talk about Alteryx Analytics Cloud. And from your point of view, given your career history and now as our VP of platform product management, how do you see Alteryx Analytics Cloud filling that gap for organizations focused on finding that insight in their data?

COREY: 07:46

Yeah. I love the question. So a lot of what we're focused on is low code and code-friendly interfaces for people to do things that-- for those who know what they're doing to have a safe place to do it, and for those who are learning to have a safe place to affect change. A lot of our products are around upskilling people to do complicated things with data in a friendly and highly usable way. And I'll give you an example. I'll tell you, this is why I'm here. I was in Singapore, a couple of months ago.

MADDIE: 08:18

That's awesome, by the way, Singapore, how cool.

COREY: 08:20

Yeah. Singapore is pretty good.

MADDIE: 08:21

Yeah, we can't glaze over that.

COREY: 08:23

Yeah. So I was with our CEO in Singapore, and we were doing a conference. And on stage was senior vice president of one of the largest banks in Asia. And he tells this story, and it is about a bank teller for their bank who lived in North Africa in this poverty-stricken village. And he was so thankful just to have a job at the bank as a bank teller. But he's a go-getter, so he emails this senior vice president and says, "What else can I do? What else can I do to increase my career opportunity?" And this guy's like, "You know what? I don't have a lot of time to spend on this." So he says, "You know what? Take an Alteryx certification and learn designer, which is our on-prem product that helps with data prep." And he says okay. And so like a month later-- and honestly, this SVP is like, "My job is done. I was a good person. I told him to go do something." A month later, he gets an email back from this guy who says, "I did it." And he's like, "Oh, wow, well, here's an advanced certification. Go and get that." And he's like okay. And another month and a half goes by, and he gets an email from this guy, and he says, "I did it. I did that too." And this SVP was like, "All right, here's a data problem. See if you can solve it." Now, this guy was a bank teller from North Africa, limited education, limited funds. A couple of hours later, he sends back the answer, "Yes, here's the answer." And the SVP gave him a job as a data analyst and moved him to a much nicer city and much better position that moment. And this individual still goes back to the small village in North Africa as an example to say, "Look, this is the cheat code to bettering your life to bettering your career. If you can learn this, you're doing things that you're not supposed to be able to do."

COREY: 10:14

And to me, that's why I love what I do is semi-regularly, I hear from users that are like, "I love Alteryx products because I'm not supposed to be able to do this. I didn't go to school for, I don't know SQL, I don't know python." A lot of them end up learning it. But they start using these really friendly tools that show them how to analyze data, how to prepare data, how to do really complicated things in data, and even how to use machine learning to predict the future. And that democratization-- I think that's a term that we use so much in tech sometimes it loses its influence. And we're just like, "Oh, democratization is a buzzword." But it's kind of a sacred word, and that democratization of saying, "Yeah, if you're willing, here's tools that can help you do something that you never thought you could do and are going to help you get your kids in college, pay your mortgage, going to increase the way that you want to look at life and the way that you look at your opportunities. It changes everything." I have story after story. A woman in Mexico who messaged me something. "You have no idea how much these tools have changed my life." A marine vet who comes home and starts learning how to use Alteryx and is now working at Amazon and doing data preparation and never thought that he would have that opportunity. It's story after story of meeting people who have the right mindset and finally giving them the right toolset to be successful.

MADDIE: 11:49

Yeah. Gosh, I love that story and all those stories. I agree. That's what keeps me excited to talk about Alteryx and speak with our community. Being on the community team, we have a welcome and introductions board where we encourage all of our new users to say hi. And so many people from around the world are like, "I'm so excited to get started. This seems such a vibrant community. Everybody's been so helpful so far." And going back to what you were saying about having the right mindset for folks who want to dive in, the community is full of users who are just getting started or people who've been using it for a long time, and they can't get enough of it. So it is an exciting place to be. It's a really great product for those reasons. With that mindset in mind, what are some of the characteristics that you see in people who do succeed with Alteryx?

COREY: 12:41

Yeah, great question. So what helps someone succeed? So first, one of the reasons we're moving all of this stuff to the cloud is to increase that mission of democratization, to allow more and more people to use it, and for businesses to be able to expand their footprint with Alteryx without having to expand all of their personnel to manage server and all these other things. As we think about who's successful, there's a couple of things, and I think they're probably pretty universal for successful people. Those that are curious, those that are determined, and those that are just willing to ask questions, are those that I think are successful. So for example, I was not an Alteryx user before I came here. I spend a few hours every day in Alteryx Analytics Cloud, playing with our products, and just having fun. And this morning, I was trying to solve a problem. And so I was bringing in the datasets, and I was playing with it, and I couldn't figure out how to solve this problem.

COREY: 13:44

So I messaged an ACE that I happened to know. And I was like, "What am I doing wrong here?" And he said, "Do this right here." And he just told me which things to-- and within seconds, I had this dopamine hit of like, "I solved the problem. I solved the problem," right? And it was like, felt so cool. And I had to drag over the tool, and I changed the thing, and I saw exactly-- and had this revelatory moment of like, "I know how to do this now." And that actually opens up more problems that I've been pushing off punting that I could go and solve now. And I think that willingness to be like, "I'm going to use this because I'm curious how it works. I'm not going to give up the second that I don't understand something. And I'm going to ask a community of people who have never seen more willing to help." This is an exceptional community. And then you get that moment to be like, "Yeah, I--" and then what's weird is you want to show it to somebody else. You want to be like, "Let me show you this cool problem that I solved." My wife's not as excited about these problems that I solved as other people, but it kind of lifts up your whole identity of like, "Hey, I solved a problem. I can do more work."

MADDIE: 15:02

Yeah. The dopamine hit is a really great example of that. I love lists. I love tasks like that. The community is really great for that for providing the weekly challenge and things like that so you can have the-- you can earn the badge, and you can see right on your profile, "I completed this task, and I solved this problem." And I'm curious from your perspective what it's like for organizations who might not have Alteryx or maybe back in the day before data democratization there was this big push for data democratization. So asking a basic question, what was that like?

COREY: 15:44

Super great question. Okay, so what you're going to find in most organizations right now is you've got your business users that are trying to use data to answer questions. And there's this big push around democratizing data. We want anyone to get access to data. So like, "Cool, here, Jane, here's access to Snowflake." And Jane is like, "What the heck am I supposed to do with this access to--? And a manual, the sequel, and now what? What do I do with this?" And so what often happens is Jane asks Mary, "Hey, as a data engineer, can you help me cleanse some of this data or figure out how to do some queries?" But they're always really limited, and so you have data engineers who should be working on big algorithmic issues doing janitorial data work so that the business users can get stuff done. In fact, I hear from other people in the industry all the time, where they say like, "We've got another issue of there's just not enough data engineers. There's just not enough people who can manipulate the data in the databases the way that we need them to be able to get the data in the formats that we need for our applications to work." They've got some cool analytics program, and it has a proprietary data model and the data has to look a certain way. And if we get the data to look a certain way, like boom, we're going to make you lots of money. But getting the data in the right format from those sources requires an engineering team that most companies don't have or if they do are on other more important things.

COREY: 17:15

So you end up with this weird issue of seeing potential and being frustrated because you have the data. You just can't use it because it's not in the right format, or it's not joined together with the other data sources. So you just end up with a big backlog. You just end up with like, "I've got 100 data requests that I'm going to work through." And any attempt to make a timely decision is just shot because you're in a queue along with everybody else that's trying to decide what's the biggest priority. So again, one of the things I love about what we're doing, whether it's auto insights, which I think is one of the best products on the planet, designer cloud to better prepare this data is taking out so much of that janitorial work and getting to the things that human beings can use to drive value and really make decisions. It's a huge shortcut for organizations to go from having data to using data. It's just saying, "Jane, I'm not giving you Snowflake access because that would be silly. I'm giving you Alteryx access that has access to Snowflake. And that's a workflow layer on top of that that's going to make it easy for you to use our data warehouses, easy for you to analyze that data and take 30% of that horrible work that you were doing and let you focus on that 60% that's going to make a difference."

MADDIE: 18:39

And also, I think it comes with a built-in safety net. I think you mentioned earlier, having it be a safe environment and being able to go into Alteryx and know that you can undo it. If you make changes in your data, you might not actually change the real data unless you publish it.

COREY: 19:00

Yeah, I think even more so what's really interesting is the number of - I almost swore - crappy Excel document that people passed around an organization as gospel. Like, "Here's the Excel document. By the way, don't touch sheet 3, 4, 5, or cell 2A or 2B, because then the whole thing breaks." And in the second you-- "Can you talk me through your logic on how you got this data together and prepared?" Then they're like, "Yeah, here's all of the equations," and you can't follow crap. You're trying to figure it out. One of the things that I love about designer cloud is I can look at something and I can see your thought process in seconds. I can follow it. And if I need to debug it, I can literally find exactly where it was wrong. And especially when you add app builder, which allows you to turn that into an application or auto insights that allows you to create a dashboard out of that data in seconds and start sharing the data, the second somebody says, "I don't know if I trust this data; I need some more confidence in the data," your pop open that workflow, and you're like, "Take a look. This is how we got to the data."

COREY: 20:12

And it's not like 4 paragraphs of code or 17 pages of random cell references. It's very logically laid out, and you can understand exactly how someone got to the data. And that confidence trickles down to everything else. If you have confidence in the data capture and the data preparation, then the decisions that you make out of it, you can make standing on solid ground. If there's a weakness anywhere along that chain and you tell someone something they didn't want to hear, "I'm sorry your campaign is not effective; I'm sorry your logistics is not cutting mustard," the first thing they'll say is, "I think the data is wrong," because you're challenging their perception. And so you need to be able to go back and say, "This is how we got that data. This is how we prepared it. And this is how we analyzed it." And there's no tool that does that better than the Alteryx tool just plain and simple.

MADDIE: 21:05

Yeah. And earlier you mentioned Jane as the business user example. Let's say that I'm Jane, and I need answers; I need to figure out what's going on with whatever is happening with my team, what's happening with the performance of my department and somebody says, "Okay, here's Alteryx, can go in and check these things," what advice do you have for Jane in terms of getting started with a new platform? It's nice that it's all one platform, but it could be daunting for some people getting started.

COREY: 21:39

Yeah, how do you get started? Well, first you start small, right? You're not going to be able to solve an organization's problems or all of your data questions in the first week. You start small, and you start playing with it. Again, that curiosity of like, "What if I push this, and what if I do that, and what does this look like? Across the entire platform, from a connectivity standpoint, can I connect to these data? And what do these data look when I do connect to them?" Now I bring it into designer cloud and like, "Okay, is this how I want the data to look? Are there multiple datasets that should be brought together? How do I bring them together? What's the unique identifier that brings these datasets together and starts helping to make sense?" Just those questions and then celebrate each individual victory along the way. So when you get to the point where you have even part of a dataset that you're eventually going to take into auto insights to analyze or into machine learning, Alteryx machine learning to use as a predictive analytics tool, each one of those steps is its own little high five.

COREY: 22:45

As I said, today's I'm trying to figure out something and I find it out, and I have that dopamine hit, and I'm like, "I did it; I did that." Take a second and appreciate that you just did something that you didn't think you could do. You did something that 30 seconds before that you couldn't, and now you have the opportunity to continue to build on that foundation. And then eventually it becomes second nature, and you're going in, and you're doing all sorts of stuff, and you're having no problem with it. And at that moment, you find the other person in your organization that could also benefit from it. And you say, "Hey, let me show you what I learned." And the best way to really validate what you've done is to teach others. And it's one of the things I love about the community here at Alteryx is that to a person when you talk about them, they're like, "Yeah, once I learned how to use this, I couldn't wait to share it with others." And I think that's a really powerful part of the evolution of anybody who's getting into data.

MADDIE: 23:37

Yeah. I mean, I think I've heard you before call Alteryx kind of a cheat code. And I don't know anybody who wouldn't want a cheat code to making their jobs easier. So yeah, I think that that's really important. And then also you mentioned auto insights and how you think it's one of the best coolest products on the planet. Tell me what you think about it.

COREY: 23:58

Yeah, if you've been asleep the last six months, maybe you've missed a generative AI like ChatGPT is kind of a big deal. And so having AI that is especially trained to do things, I think is definitely critical to being a knowledge worker in the future. And we have an AI, and we acquired a company called Hyper Anna. Anna is the AI that runs auto insights. We renamed it auto insight. Anna's entire existence is to look at data and to analyze that data faster than any human being can and to come up with insights to analyze it and say, "Here's what your data means, and here's reasons that your data's doing that. Here's some things you should definitely look at." My first job in this space was to do that for some of the biggest companies in the world. I would get their web analytics data, and I'd be like, "Yeah, it looks your revenue was down in the UK, or it looks this particular campaign didn't perform as well or performed really well." The lag of getting the data and analyzing the data and then putting together a dashboard and then sending it out is something that our industry has just kind of lived with for 20 years. And if we have AI that can generate art and we have AI that can write books, we have AI at Alteryx that can analyze your data.

COREY: 25:24

So when you think of an end-to-end, what's incredibly powerful is I can get in and get the data to the point where I have to confidence that dataset using designer cloud, and then I can send it right into auto insights, and in 30 seconds, it has analyzed that data. I can continue to push it repeatedly in there so it's always getting the most recent data, and it's creating dashboards to what it calls missions. It's telling me what the data means that I can then send to my team, and I can look at, and as an analyst dig into the really nitty-gritty of it. So having an AI, having an artificial intelligence that specializes in analyzing data, I think is going to be a key differentiator for Alteryx and Alteryx users over the next five years. As these generative AIs are going to be creeping up all over the place-- ChatGPT is cool. It can't analyze data. You feed it a dataset, and it will describe the dataset to you. But Anna can analyze the data. And I think that's something that-- if you want to talk about cheat codes, man, that's got to be like, that's the granddaddy of cheat codes is having an artificial intelligence analyze the data for you.

MADDIE: 26:29

And yeah, if I'm Jane again, when I hear somebody explaining to me, "Okay, this AI is going to analyze your data," to me, I'm such a visual person. I might not really understand like, okay, but what is that experience going to be like? The experience is so easy and user-friendly. The interface is gorgeous. It very clearly will write out in words like, "This is what the problem is and maybe you should focus on this area." So you don't have to look at numbers and just kind of figure it out on your own. It's very clear like, "Hey, this is your problem." And literally telling you that. So it is so much easier.

COREY: 27:08

Absolutely. Again, we're monkeys. We like colors and shapes, right? But the reality is that we're looking at colors and shapes and data visualizations so that we can answer questions. And one of the things that Anna does in auto insights is it just literally-- it shows you colors and shapes because that scratches that part of your brain that needs that. But yeah, it literally says, "Your sales are down by this percentage in this region. And we think it's because this campaign or this issue is causing it," which is what you would have figured out if it had just given you the visualization. But you would have figured it out in 20 minutes or half an hour, and this is telling it to you in milliseconds. So yeah, the experience is very friendly. I think it's great. Honestly, I think there's a whole other podcast in what's the future of data visualization as these types of technologies come out. Yeah, auto insights, man, if I'd had that over the last 20 years, I would have been able to be so much more effective in my jobs and focused on the real things that I wanted to do to drive change.

MADDIE: 28:16

That's great. Super excited for more people to get their hands on auto insights and analytics cloud in general. I think there's a lot of great opportunities for folks on the horizon.

COREY: 28:28

Yeah, me too. Again, I apologize for the construction going on. Hopefully, the listeners aren't like, "Why did Corey do this in the middle of an earthquake?" But we're really excited about this. And over the next couple of months, Alteryx Analytics Cloud is releasing in February. We've got more and more products. We're going from no cloud products to seven cloud products over the next three months, stuff that's just backed up ready to release that would have been in beta and that customers have given us amazing data on. We're bringing our on-prem and our cloud together in the future. This next year is a big year for Alteryx, but all because of our users. And so the feedback that we get from our users, the feedback that we get from our community, that's the heartbeat of everything that we do. So if you're listening to this, please let us know what you love and what you don't love about our stuff because that's what's going to make this better. We need that. It fuels everything.

MADDIE: 29:24

Thanks for listening. To get started with Alteryx Analytics Cloud, including designer cloud, machine learning, and auto insights, check out our show notes at Catch you next time.

This episode was produced by Maddie Johannsen (@MaddieJ), Mike Cusic (@mikecusic), and Matt Rotundo (@AlteryxMatt). Special thanks to @andyuttley for the theme music track, and @mikecusic for our album artwork.