TECTERRA’s CEO, Jon Neufeld, met with Marc Prioleau, VP of Business and Corporate Development with Mapbox, to discuss how their open source mapping platform is changing the way developers create scalable mapping experiences for their users.
Mapbox is a large provider of custom online maps for websites such as Foursquare, Lonely Planet, The Weather Network and Snapchat. In this Q&A, Marc and Jon take a look at the future of mapping and the ever-changing sophistication of how people are thinking about location.
Jon Neufeld: Thank you for joining us, Mark. Tell me a little bit about Mapbox.
Marc Prioleau: Mapbox is a venture-funded company. We started in about 2010, and received our first funding in 2013, and essentially what we've built is a developer-focused mapping platform. So, everything we do is built in the cloud, and we offer it out to developers as a series of building blocks that they can use to build their own mapping experience, and so it's very much focused around high volume scalability mapping, but also allowing developers to really build what they need to build, and I think that's really our differentiation is people have taken our tools and built really unique map sets, that ranges from large consumer brands like Facebook Local, Snapchat, The Weather Channel or Lonely Planet. We're doing a lot in business intelligence where companies are using our maps to understand their business operations, and we've done deals with Cognos, MicroStrategy, RBI, Microsoft, and we also work in a number of other fields like mobility, which is a really growing field including things like ride sharing, but also delivery and automotive. So it's really been a dynamic field across a number of markets.
Jon Neufeld: Yeah, it sounds like you guys cover a broad range of market segments and potential use cases. I'm really interested in how the Mapbox platform can enable innovation in the geospatial industry?
Marc Prioleau: You know, I think one of the things that we see in the industry is there's just a tremendous range of innovation on. If you think back to it historically, there was a lot of innovation but ten years ago a lot of things sort of streamlined into one rather large map provider. But now what we're seeing is that companies are really starting to think about how location data and maps can be used to make something really differentiated in their product. That ranges in anything from applications, which we call map-first applications, where when you open the application the first thing you're looking at is a map because that's the way it arranges the world. When companies are in that model, they really need that map to not be generic but do specifically what they need it to do. That can be anything from custom cartography, it could be bringing in huge data sets and deploying those out to millions of users like you might have in a weather application, or it could be customizing things around key parameters for the company, like really targeting very closely what estimated time of arrival is for a delivery service. Most of our conversations with people start with, "What are you trying to do in your business? What are the things that give you competitive differentiation?" and then go to, "What does the map have to do to support those things?". That's really been one of the most productive conversations we've been having with many customers.
Jon Neufeld: Yeah, I really love the map-first orientation for the delivery of the service you guys operate. For people like you and I, who come from a mapping-centric world, I think that makes sense. Have you had any resistance or any concern raised by people who come from, say, a business-first orientation who don't see the world as a map initially?
Marc Prioleau: I think that you're right and I probably look at everything as it should be on a map, but I think what we're really thinking about there is if you think about the classic, and you know this might be five, six years ago, the map was the last thing in the consumer flow. So when you would look for something, you would search for something, you'd think about what it was, you'd filter it, you'd find what it was and then say, "Now show me to it on a map." So the map was kind of the last piece of the flow. Now what we're seeing for companies, and companies like Uber were one of the first, but we're seeing it in bike sharing, we're seeing it in delivery, we're seeing in all these mobility services - when you look at it, the first thing you look at is the map and I think what that's indicative of what those companies have said, "Mapping used to be something we did on our laptop, now it's something we do on our phone" and that means that more often than not, we're out in the real world looking for things around us. It's only natural then to say, "Okay, if I'm looking for things around me, I'm going to put myself at the center of this thing and this thing is going to be a map that I look at to understand the world". I think that's really what's driven technology forward, is that push to mobility, the push to where all those mobile devices know where they are, and the map just really falls out as a logical way of showing that.
Jon Neufeld: I would totally agree with that and I often am guilty of opening up the maps application to find something I'm looking for, and then discarding the ones that are too far away from me. Do you think this a result of people thinking spatially and thinking about the world as a map, or simply looking for the closest service provider? Or is there a difference, really?
Marc Prioleau: I think people are looking for things around them. In Banff, if you want to find something, you want to know what's around you, or in Calgary, if you're driving, what's on the road ahead of you, not what you just passed. We're seeing more and more sophistication in how people are thinking about location. To give you an example, if you're in the ridesharing business, and a large part of your business is predicated on shared rides where - I book a ride and then you book a ride and we pick you up and then we drop me off - then there's a huge amount of optimization around how that works. An example is, this won't be helpful to you without a map, but I work in San Francisco and if I were taking a rideshare to my home, there is one way you would go straight down one street, take a left onto the freeway and go home, and that would be the most efficient route. That's the way everyone knows you should route. But if you want to pick someone else up to make that ride economic work, a couple of blocks to the side of me is a baseball field and if you know a baseball game is going on you might actually skew my route toward the baseball field to pick people up before you get on the freeway. That's what I mean, a route is beginning to be optimized around what the business objectives are. And I think those are an example of some of the nuances we're starting to see from a lot of our customers.
Jon Neufeld: You talked earlier about nuance and data analytics and I think this route-skewing example is a really good one. When you think about data analytics and understanding the world spatially, where do you see the future of location-based technology taking us?
Marc Prioleau: Great question. I think that one of the things we've seen, as we're doing a lot right now working with business intelligence software companies and companies that want to do large-scale business intelligence to understand their business. One of the things that we see really changing in that market is in the past, that sort of spatial thinking or spatial analysis was the purview of a spatial analyst, a person who probably had the word, "geospatial" in his or her title and it was done on that basis and in a very specialized way. One of the things that we're seeing now is companies want to distribute spatial data broadly across their entire network and they want people who are not spatial analysts or geospatial experts to be able to look at things spatially and make decisions. What we're seeing a lot of when working with companies that you would consider to be a software company, but now they want to add a spatial component so that the data, that decision-making framework can be available to a lot of people very easily. That's one of the big changes that we're seeing is just a much broader level of interest in having access to that data, being able to manipulate it, and ultimately being able to make decisions based on it.
Jon Neufeld: It's incredible if we think back to when the first iPhone was introduced and how knowing our position with high accuracy at any moment in time has really reshaped the way we see the world as a group of people.
Marc Prioleau: Yeah, in fact I have a somewhat tongue-in-cheek presentation on the history of mapping, which has about four data points on it, which is many thousands too few, but one of them is the introduction of the iPhone. That moved geospatial, it did two things really - one was, as consumers of maps, we tended to be in situ, we tended to be in place of where we were consuming, we wanted to know what was around us. The other part, and I think it's maybe even potentially more important, is as we started consuming information while we were out in the world, we sifted back more and more data about where we were. That doesn't have to be personalized data by name and all that, but we at least knew traces of where people were and where people were moving. I think what we've seen is that shifted the whole way maps are built. It used to be that maps were built by you and I in the car, and you drive and I take notes, and we're driving and we're surveying and understanding and building a map. Now what we're seeing is by collecting telemetry, location, back from hundreds of millions of users we actually are building the maps in real time. We're actually getting that feedback and we can see not only where roads are but how fast traffic is moving on roads, where people are forming, where people are moving away from and that kind of thing. There's really much more of an emphasis on maps as the world exists right now, not as it existed eighteen months ago, and that's really kind of fueled this cycle that's either virtuous or a complete pain in the neck, depending on where you stand, but consumers expect their map to be what the world is today. That's really put a new bar out there for folks in this business.
Jon Neufeld: That brings us to the concept, then, of dynamic data and as you and I talked earlier, that concept is feeding the user the data that suits them and their journey best. Do you see any benefits or perhaps even dark sides to serving users with customized geographic data?
Marc Prioleau: I think the benefit in the end, and one of the interesting things we're seeing as we look, especially on the consumer side, maybe this isn't applicable on the business side, is really being able to serve tailored information to people about what they're looking for. The example I use is that if you think about consumer applications for a map of, say, Banff Avenue, if you walk down Banff Avenue, if I walk down there, there are certain places that are of interest to me. If I walk down there with my wife, there are places that are of interest to her. So we're walking down past the same places, but those places have different weights depending on our interests. I think one of things we're starting to see is, to the extent that companies can really understand the user, they present information that's relevant to that user and, equally important, don't present things that aren't relevant to that user. That becomes a very personalized experience. Now, the risk there is if you get it wrong you either show them things that they aren't interested in or don't show them things they could be interested in. I suppose that is the risk of it but I think it really starts to get just some very rich experiences. We're seeing this not just in search but we're seeing that in social media, in terms of where my friends are, what are the places my friends like, you know those kinds of things as we work with a number of different companies.
Jon Neufeld: And I think the more we tailor and create that rich experience for consumers, the more interest they're going to have in engaging with spatially-oriented data and seeing the world as a map.
Marc Prioleau: Yeah, and I think it's consumers, it's also the economics behind the consumers. I was talking to a young woman who works at a large social media company and she looks at large spreadsheets of data collected from advertising campaigns and just recently she called and said for the first time she'd had latitude and longitude attached to those records of where people were engaging with data. I said, "What'd you do with it?" And she said, "I didn't do anything. I didn't know what to do." That's really the big market as it goes forward. She's not a GIS expert, she doesn't know about geospatial, but she's very good at data analysis and all of a sudden she has these huge data sets that have spatial information. The challenge for us in the industry is to make people at that level of geospatial knowledge, which is not much, able to use that in really smart ways and to the extent that she and her peers can use that in smart ways, they will do a better job, in that case, of making advertising relevant. I think that's kind of a different market and a different challenge as that develops.
Jon Neufeld: The better we get at creating tools and techniques and systems for individuals like her to use the data in smart ways, the more and more they're going to engage with it and create value from it. We see that quite a bit in applications from companies who want to democratize their systems and make it more open and accessible to companies to have access to the tools to create that understanding. It's wonderful to see that happening at scale like that.
Marc Prioleau: Yeah, and that's really one of the keys for us. There's more and more data and it becomes more and more important to the operations of these companies that are really essentially mobile companies. Then it becomes more and more important that becomes, to use your word, democratized, that more people get access to that data and be able to use it in a really purposeful way. That's been one of the big things. There's a lot of side chains to that and are interesting discussions. The growth of open data, I think, has done a lot to move along, the growth of crowd-sourced data. We're spending and investing a lot in machine learning on the data we get back to understand what that tells us about the how the world's changing in real time. It really has sort of expanded in a number of different directions.