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Alter Everything

A podcast about data science and analytics culture.
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Alteryx Alumni (Retired)

Take your geospatial analytics to the next level with Alteryx Location Intelligence! Alteryx Sr. Technical Product Marketing Manager, Dave Bryson, shares why geospatial analytics is crucial to any organization, and how you can get early access to Alteryx Location Intelligence to level up your insights. 








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Episode Transcription

MADDIE: 00:02

[music] Welcome to Alter Everything, a podcast about data science and analytics culture. I'm Maddie Johannsen, and today, we're talking about location intelligence. Spatial analysis is crucial for many organizations, and Alteryx has really powerful tools ready to help you optimize your data. So I sat down with my Alteryx colleague, Dave Bryson.

DAVE: 00:24

My name is Dave Bryson and I am a product marketing manager here at Alteryx for our location intelligence and spatial products. I've been here at Alteryx for about four years now, since 2018. So yeah, it's been great.

MADDIE: 00:38

We chat about spatial IQ, lidar, mapping planets, the proliferation of spatial data, and the future of location intelligence at Alteryx. Let's get started. You mentioned that you've been at Alteryx for about four years. Something cool that we do for our new hires is to have in-person training and orientation, and I remember from my new-hire training, we heard a lot about Alteryx history and what our focus was way back in the day. I'm sure you remember this as well and I'd love for you to tell our audience about that history.

DAVE: 01:14

Yeah. It's funny. There's a lot of people don't know this about us, but Alteryx started as a spatial consultancy. The name of the company back in the early '90s was SRC, which was Spatial Re-engineering Consultants.

MADDIE: 01:29

It's a mouthful.

DAVE: 01:30

Yeah, [laughter] it definitely is, and very specific. But back then, Alteryx worked a lot with the Census Bureau, and the products that were created back then-- I think it was 1998. There was a product release called Allocate which allowed users to work with spatial data, and then from that, there was another product called Solocast which allowed customers to do customer segmentation analysis against demographics. And it wasn't actually until about 2010, I think, that actually, the company was renamed Alteryx. And if you look at the name Alteryx and split it apart, Alter and then Y-X, the Y-X is another term for latitude and longitude coordinates. So even in the name of the company, you still see the Alter Y-X, altering spatial data. So I always love that about Alteryx that they kept that little-- it's almost like a little Easter egg there in the name of our company.

MADDIE: 02:30

I think it's so fun to see some of the old-school vintage tool icons as well. We'll have to put some of them in our show notes, just some of the pictures. They're, I guess, '90s, right? It looks so '90s. It's so fun. But I think that there's also a hidden Easter egg in there for listeners to change the tool icons in Alteryx to the old ones if you click a certain area. But maybe we'll include some instructions in there to do that in our show notes for people who are interested. But yeah, no, I think that history is super important to the company, and it also speaks to just that appetite for spatial tools that we've always had. It's a crucial tool to not only have for convenience but also for survival if we think back to the dawn of time. So thinking about the more recent history of maps, I can remember a time where an atlas was essential to a road trip and stopping for directions was a thing. We've really had the opportunity to watch those huge advancements in this arena. Can you walk us through that more recent history?

DAVE: 03:43

Yeah. It's funny you bring up the road atlas because that's actually, when I was a kid, where my interest in mapping came from. My dad had an atlas in his car, and to pass the time, I would look at the atlas. And it was a Rand McNally map and the maps were beautiful. He looked at them and, from a style perspective, they were very well done, very clear. They would show you the boundaries of the cities. And in the back of the map, they would have the population of all the cities. So I would look up, "Oh, New York has seven million people, and Chicago has this many people." And I'd go to the map of that city and look at it, and they were so different from where I grew up in North Carolina that I'd always would imagine in my mind's eye these places that I hadn't visited yet. But you're right, that was how we used to interact with maps. And when I started my career in Geospatial, at the time, most of the spatial work was done in what's called GIS, a geographic information systems, and the focus was paper maps, creating paper maps from computers. The output of that work on that computer was a paper map. And there was programs and software at that time was really optimized to create these very large wall maps or maps that would be created and put into books. And right around the early 2000s, you started to have more mapping applications come in through the internet, like MapQuest, if you remember that. You would print out MapQuest directions and they would just be text directions. You didn't even have the nice maps like you have now.

DAVE: 05:19

And one of the things I noticed early on when I was starting my career, we were getting into the cloud and web mapping and things like that, and I think that it was really hard to explain to people. It was really hard to explain, especially to businesses, what is GIS, what is mapping, what is location intelligence. That word didn't even exist yet, and there was no reference yet for people who had never heard about this before to really understand it. And what I've noticed over the last few years is that, just in the culture in general, because of things like Google Maps where today, you get in your car and Apple CarPlay comes on or Android Auto comes on, and boom. You got your map on your dashboard. Off you go. You put in your destination. It tells you exactly where you need to go, how long it's going to take you to get there, what the traffic is like. These are all things that, even 10 years ago, we did not have. And so what's great is now when I talk to customers or I talk to analysts and they don't know much about spatial analytics or location intelligence, you can use things like Google Maps and these consumer applications to orient people. "Yeah, imagine Google Maps but with data on it or your data on it." And then immediately, you can start to have a more in-depth conversation about what location intelligence actually is.

MADDIE: 06:38

I agree. I feel like that is a lesser-known term and concept. So I love that perspective of thinking about it as Google Maps with your data on it. That's really important. And I think that craving for that spatial understanding, not only is that super important for businesses, but I feel like it's something that's hardwired in us, in our brains, and everything like that. Can you share more about that aspect of it? As you were talking about also being a kid and looking at different cities and things like that, what is it about spatial that gets you really excited?

DAVE: 07:17

It's interesting because spatial intelligence, it's actually something that we all have, and it manifests itself in a lot of different ways. And there's different types of intelligence. There's emotional intelligence. There's linguistic intelligence. There's seven of nine or so defined different types of intelligence, and spatial being one of them. And one of the ways it manifests itself is just our ability as humans to put together objects in our mind or think about shapes and objects, so you imagine an apple or imagine a group of something and you put them together in your mind. If it's data, maybe it's locations of something or the distribution of numbers. You can sort of think it and see. If you're looking at a spreadsheet and you say, "Okay, I've got a column of data and I see a particular category repeated over and over again," one of the things that we do a lot in data is pivoting, pivoting data. That's actually kind of a spatial intelligence because you're taking data as an object and you're saying, "Okay, I want to take all of these categories, clump them together, and then summarize a particular variable, let's say, sales, by category." That's actually thinking spatially within the data.

DAVE: 08:28

And then of course, you have what a lot of us do if I were to say, "Hey, Madeline, go buy a lamp at a store somewhere." Immediately, you're probably going to think, "Okay, which store am I going to go to? How far away is it? How do I get there?" when you're mapping that out in your mind to think about how do I transverse the street network or the sidewalk network to get to where I need to go. And it may not always be the-- may not always be the closest one, right? It might be where it's the cheapest or the one that you want, right? And it's interesting how, again, with things like Google Maps and whatnot, this intelligence, I think, it's become much more relatable. And when you really dig in to what is spatial intelligence, every analyst who uses Alteryx, has it. I think that's actually why one of the reasons that Designer is so popular is because it actually visualizes through the canvas and through the application and configuration of tools the process of doing what I just described like pivoting data, for example. You can see it play out in front of you and put in the different tools that you want in order to get to the result that you want at the end. We pick on Google Maps a lot because it's so common, but, I mean, think for people who are working in the data analytics space, mapping is proliferated there as well. So even within our space, in our analytics space, just putting data on the map and thinking spatially about the data that you have and that dimension of spatial has really exploded. The proliferation of mobile devices over the years and stuff like that and how it contributes to not just the cultural understanding of spatial but just the data as well, I think that's actually a pretty interesting statistic where if you look at when Alteryx was renamed to Alteryx in 2010, there were about 300 million phones sold every year. And then fast forward to a couple years ago, it's 1.4 billion phones.

MADDIE: 10:28

Wow. Worldwide, I assume?

DAVE: 10:30

Worldwide. Yeah. And what's interesting about that is every phone is essentially a GPS receiver. And not only is it receiving location data, but it's transmitting location data. And so you've had a three or four X or more increase in these devices that go out every single year. And every single year, they get better. They get more accurate. There are more apps that send more data. So my guess is that any listener right now, whatever company you work for, has got some kind of location data. Maybe it's not mobile device data, but cut any kind of customer data, store data, anything, insurance policies. Anything has some type of location tied to it. And so yeah, I think that's just an interesting example of just how not just the software is evolving, but the devices that are out there that can transmit this type of data is just exploding. It's not just phones. Think about IoT stuff, so your refrigerator, cars, your cars now. You're poking your phone to your car. So that's actually how Google Maps gets its traffic data is it's tracking you as you drive and you're using the Google Maps app, and it's tracking you and every other car, and that's how it makes the traffic maps. And that's why they're actually really good. And again, that's just one example of an application of this. But I just think that aspect of the availability of geospatial data and how it's been collected and stored, and there's just so much of it. And I think tools like location intelligence and Alteryx give you the ability to tap into it.

MADDIE: 12:18

I love this conversation. I feel it's really bringing back memories of when things really started to change in the 2000s. I remember when Google Earth came out and it's just sitting there, and so it's like, "Okay, let's look up Paris. Let's look up Istanbul. Let's just go walk around the streets on Google Earth." That was such a big deal. That was never a thing before then.

DAVE: 12:47

Yeah. It's funny you say that because I remember when Google Earth actually started out as something called Keyhole. And it was a product-- it was actually made out here in Boulder. And I remember downloading Keyhole for the first time and playing around with it. And it was funny. Everyone I always showed it to, the first thing, "You got the world at your fingertips, right? You can look at anywhere you are in the world." Everyone was like, "Where's my house? Show me my house." And even today, I was on Google Maps and I introduced my kids to it. So my daughter, she's eight, and this was a couple of years ago. I showed it to her, and she was like, "Daddy, show me the house. Where's our house? Show me our house." It was this intuitive thing where the first time you see a map on a computer, it's, "Take me to my house. Let me see my house." And I think maybe there's something there that it just grounds you or something. You know your house, you know your home or where you grew up better than any other place, and seeing it on that map orients you maybe a little bit. So maybe that's why people always do that. But I find it's interesting that whether it's kids or adults, it's always, "Where's my house? Where's my house? Show me my house."

MADDIE: 14:06

That's so true. I have a theory that it's tapping into the craving that we have for a nostalgia, in a way. If you look up my parent's house, for example, on Google Maps, you can see my old car from college. You know what I mean? So it's just funny to look it up and see that and just see how-- and even my house now, just seeing how the house has changed over the years since that image was taken. Yeah. There's that nostalgia piece there.

DAVE: 14:34

It's so cool that now, on Google Earth, for example, they have a time slider bar where some places, you can go even back to the '60s or '70s or something and see what the place looked like back then. And you can take the slider bar and go all the way up till today. It's fun sometimes to look at places where growth has happened dramatically in the last, let's say, 20 years. A good example is Dubai. People like to look at Dubai in 2000 and then '95 and then look at it today and just the explosion of that place and how fast it grew. But even going back to your-- and you can look at it in black and white pictures from way back and see how your town has changed, your home has changed, your neighborhood has changed. It's pretty neat that they added that feature, but it shows you just how much of that data-- we talked about cell phones and the proliferation of cell phones. A lot of that early data was taken from weather satellites, government satellites. And today, other companies like Planet Labs, for example, where they send these constellations of what are essentially cell phones into orbit and will take pictures of the earth every 12 hours or more, and that data just gets beamed right back down to Earth and is available through these various data providers. So we're collecting that whole area of satellite imagery and things like that because that's a whole nother area of the location intelligence space that, again, has exploded over the last 10 years because now it isn't just you have to get some kind of contract with NASA or NOAA or something to get data. There are just companies sending up rockets all the time with sensors that can collect all kinds of data, not just imagery but infrared imagery and lidar and all kinds of other interesting parts of the spectrum that they can collect. That's really been fun.

MADDIE: 16:32

That's wild. Yeah. I haven't even heard of some of those terms that you mentioned, like with lidar. What is lidar?

DAVE: 16:40

It's pretty cool. So it's sort of like radar for the ground. So what it produces is 3D models, so like really accurate models of buildings, houses. I know we always take it back to Google Maps, but Google Maps has that, right? It has 3D buildings of downtown whatever, just about every city. A lot of that is collected from lidar. So not just buildings but elevation, hills, mountains, things like that can all be measured with lidar very accurately. And so if you're doing a slope analysis, for example, for avalanche prevention, you might target certain areas with certain slopes and certain amount of snowfall. Lidar is ace for a lot of that type of analysis.

MADDIE: 17:26

That's fascinating.

DAVE: 17:28

One of my favorite things is how we are now mapping other planets.

MADDIE: 17:35

Oh wow.

DAVE: 17:36

Like for example, we've sent a lot of satellites to Mars. And those satellites are similar to the ones that we have orbiting Earth that can collect imagery, can collect lidar, can collect a lot of different types of data to map Mars. And some of that data is actually becoming available. There are people who have taken Mars and put it on a globe, like Google Earth, and you can cruise around Mars and look at-- there's frankly not a lot to look at, but there is, though. I mean, Mars is fascinating. And it's one of my-- I love doing that. It's so fun to-- and the Moon. People have done it with the Moon, but Mars especially. So hey, as various companies out there try to get us to Mars, maybe one day, we'll be sitting around and looking at our great-great grandchildren's homes on Mars or something. That part of it's pretty fun.

MADDIE: 18:37

Yeah. Yeah. Anything is possible at this point. There's people not too many generations ago that they didn't know if the Moon was going to be quicksand when we landed on it, so you never know what's going to happen. Pivoting back to you, Bryson, as you've been in the spacial analytics game for quite a while, can you share some examples and some of maybe your favorite projects that you've worked on?

DAVE: 19:01

Yeah. I've been very fortunate to have been in roles where I work directly with customers. So when I left graduate school, I got a job at a company called ESRI or ESRI. They're kind of like the IBM of GIS. And so I started there in 2008 as a sales engineer and was there for about eight years and got to work with a lot of different customers in local government and retail, real estate, manufacturing. And what's really cool about spacial is, this is actually one of the reasons why it's hard to explain to people, but it is applicable in all of those different industries. So those industries could not be any different from each other, right? Like local government and retail. They're two completely different industries with different objectives, and yet spacial is applicable in both. So I've had the luxury of being able to work in those different spaces, and I think one of my favorite use cases over that time was we worked with a retailer on an analytics project, and one of the things we were doing is we're applying something called-- this is going to sound-- it's a very fancy word, but spatial autocorrelation. And put very simply-- and hopefully, data science or spatial data science community doesn't scream their head off at this while I say this. But explained simply, spatial autocorrelation is essentially looking at locations and comparing them against their local neighbors, right? The output of the analysis will usually be their areas where sales are really high, areas where sales are fairly low compared to the neighbors, right, the nearest locations. But the ones that are really interesting are the output of the analysis where you've got locations that have high sales surrounded by low sales. So that means they're overperforming against their neighbors. And then the opposite also is true where you have low sales surrounded by high sales. Underperforming.

DAVE: 21:02

And so we ran this analysis. It was just in Chicago. And so I looked at all the locations and there was one that sort of stood out. And I was like, "That's interesting, this high surrounded by low." And I looked at it on a map and it was near a hospital. And I was like, "Okay. That's interesting." There was a university nearby. And so I was like, "Okay. Maybe run this in another city." And so we looked at in Kansas City, in Houston, in Dallas, in Los Angeles. And the same patterns emerged where there were these really high-performing locations that were near hospitals. And I would not have seen that if I'd just taken the spreadsheet and sorted based on sales and looked at Chicago. I would've seen the location but I wouldn't have seen the relationship with the neighbors and I wouldn't have seen the fact that it was near a hospital. I wouldn't have been able to investigate more about the why. And years later, here where I live in Colorado, you'll see a lot of new hospitals get built. It's one of those things that when they build it, it's a big deal. And so they built a new hospital near my house. It's only about two or three miles away. And sure enough, they built a shopping center right next to it, and this retailer that I was working with, there was that location right next to the hospital.

MADDIE: 22:12


DAVE: 22:13

And so it was really cool to see how, in real life, there it is. And the funny thing is that location is always packed. [laughter]

MADDIE: 22:21


DAVE: 22:21

It's always busy. So even all these years later, they learned that lesson about hospitals and putting their locations near hospitals. And like I said, I don't think you would-- I would not have seen that or the customer would not have seen that level of detail or dimension in their data, had they not had spatial analytics and mapping as a part of that analysis.

MADDIE: 22:45

And it seems to be the validation of comparing it to other cities. It takes the guesswork out of it, and then you can start to think about why that's happening. You can really prove, "This is happening. It's happening in more places than just this one example." It kind of removes the human tendency to just theorize based on, "Maybe this is happening," or, "Maybe this is what my experience would be in this situation, so that must be what it is." It kind of removes all of that guesswork and really just drills down into the actual facts, which is what the analysts and data scientists really want, so.

DAVE: 23:25

Yeah. No, it's interesting because if you think about that use case, you rank all of the sales for the store network in Chicago, actually, that one wasn't the best-performing. So it would've gotten lost in the shuffle. The very interesting part about looking at data is when you look at data like sales or things like that, a lot of times, it'll be the same-- the same patterns will emerge, right, where wherever you have the most people, you will have the most sales. So you'll look at customer data or something like that and you'll be like, "Oh. New York, Los Angeles, Chicago, Houston. Why? They're always the most. Why?" Because there's the most people living there, right? So if you can't get deeper and do more of that hyper-local analysis with your data, you're missing that element. Everything is just going to turn into a population map, right, where, well, most sales are where the most people live. But within that, within those cities, within these locations, there are nuggets to pull out, and you can't really get to that if you're just sorting and pivoting data. You need that spatial element to get those deeper insights.

MADDIE: 24:33

I mean, that phrase, location, location, location, and that's a phrase for a reason. So yeah, that rings true.

DAVE: 24:40

Yeah. Absolutely.

MADDIE: 24:42

And I want to talk about location intelligence in Alteryx. I think you mentioned that earlier. Can you explain what that is?

DAVE: 24:51

Yeah. So really, location intelligence is it's sort of a broad term for spatial tools within the Alteryx product set. We do have a lot of spatial tools that are already in Designer today. It's the green tools. If you're on your toolbar, you can see them up there and they are there for you to use. They come with your license. You don't have to pay anything extra to use it. Where a lot of people I think get stuck is, "How do I feed data into it to use these tools?" And one of the things that I've really gotten into lately is about three or four or five years ago or so, a lot of government agencies, public sector agencies, have engaged in something called open data, where they have a mandate to share their data with the public for free, and the mechanism that they use to share this data is open data portals. So actually, if you just go and Google-- for example, if you Google "Open Data Denver" or "Open Data Los Angeles," you will find, right at the top of the search results, these data portals that have hundreds, sometimes thousands of different data sets available. A lot of them are geospatial data sets, and you can download those for free and you can put them into Designer and you can start to blend them in with your existing data and do all that right in your Designer desktop, which is really awesome because it helps you to get started with these tools without having to buy specialized data sets or anything like that. But through what's called our Location Insights product, we actually do provide data as well. So if you're interested, this is an extra. You can talk to your Alteryx sales rep about it. But it actually has data inside of Designer that's baked in, so you can pull things like demographic data and run drivetime polygons, lots of different types of analysis just integrated right into the product. So, hey, if you're looking to dip your toe in the water, use those green tools. Go to these open data portals, pull in some data, start to see what you can do, and then the sky's the limit after that.

MADDIE: 27:00

Yeah. No, that's cool. That sounds very convenient and very helpful. Can you share what's in store for the future of location intelligence at Alteryx?

DAVE: 27:12

Yeah. I can give a little bit of a-- maybe a little bit of a sneak peek. So obviously, Alteryx, over the last year, two years, has really been focusing on moving our platform into the cloud. And that's given us the opportunity to reimagine a lot of the ways in which our users work with data and even spatial data within Alteryx. And so one of the initiatives we have is to create a more map-driven interface that builds upon - we talked about Google Maps, right? - shifting the focus of doing spatial work and working with spatial data to a map. And what that's going to do is it's going to allow more users to take advantage of spatial data within their organization. Spatial data today is exploding and it's more and more moving to the cloud, and our customers are moving their data to the cloud and to platforms like Snowflake or Databricks or other cloud repositories. And the Alteryx Location Intelligence initiative is something that is going to help give our customers the ability to interact with and analyze data stored in our platform.

MADDIE: 28:21

Very exciting as well. And is there an early-access option for Alteryx users right now?

DAVE: 28:30

Yeah, there is. There's an early access that has been released. You can go and sign up for that. We'll also have some sessions at Inspire focused on location intelligence and spatial in general. I've got a few sessions myself that I'll be doing. One is I did a YouTube series called Spatial How-To where I kind of walk through the process that I talked about, going to open data portals, pulling in data, using our spatial tools. Actually, I think I'm calling it Spatial How-To In Real Life. So going through those YouTube videos, showing it in a session, and there'll be lots of new location intelligence stuff to talk about at Inspire. Definitely sign up for the early access. Attend our sessions at Inspire. We're really excited to be sharing spatial and having spatial at the forefront of Inspire this year.

MADDIE: 29:16

Very cool. Yeah. Any session that is how-to or has an application in real life, those are very helpful and always really popular at Inspire. So for folks coming to Inspire in May, it's in Vegas, so make sure you hit up those sessions that Bryson will be giving. And I think that you also looked up some demographic information for tourists in Vegas.

DAVE: 29:45

Yeah. It's sort of like an interesting data point about tourism in Las Vegas and where tourists come from. And when you look at where people come from to Las Vegas every year, it's kind of the usual suspects. The top two are California and Texas, and California are neighbors with Nevada, which makes a lot of sense. Texas is, I think, the second-largest state in the union, so of course, there's probably a lot of people coming. But the third one is Washington State, which I found really interesting. I would not have guessed. I would have thought, similar to what we talked about earlier, that it would just be a population map. The most people come from the most populous states, which for the first two are true, but that third one is kind of an interesting outlier. Why is it Washington State is the third most common state that people come to Las Vegas from? So I thought it would be cool. What if some of our Inspire attendees look a little deeper into that? Figure out what could be explaining the sort of over-indexing of people from Washington State coming to Las Vegas. There's probably some hypothesis. Maybe it has to do with the weather or something else. But yeah, it might be kind of a fun interesting spatial challenge to understand migration of people coming to Las Vegas.

MADDIE: 30:56

Totally. I would not have guessed Washington State either.

DAVE: 30:59

Yeah, no. I think, actually, that's number three. I think number six is actually Oregon, so yeah. Washington and Oregon both are in the top 10. And Michigan, I think, is right after Oregon. So maybe there's a weather angle there, but it would be really interesting to dig into that a little bit more.

MADDIE: 31:18

Oh, totally. If any of our listeners are planning to come to Vegas in May for Inspire and you're from Washington State, drop us a line on the show notes. Add a little comment. Just say howdy. That's really interesting, and what a good anecdote. [music] Thank you so much for joining this episode. It's been a blast hearing about your past, Alteryx's past, and the future of spatial intelligence and location intelligence, et al.

DAVE: 31:44

Thank you very much for having me. It's been really amazing, so thank you.

MADDIE: 31:49

Thanks for listening. To check out Alteryx location intelligence, head over to 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.