Hyperpublic: Structuring Place Data, Redefining Hyperlocal’s ScopeStreetFight Dec 08, 2011 Back to press
Are you up for a quick thought experiment? Let’s say you’ve met some friends for drinks at Finnegan’s Pub after work, and, while you’re waiting to order a beer you check in to Finnegan’s on Foursquare. What data did you just create about yourself?
Well, at a very basic level you’ve created an association between yourself and Finnegan’s at a certain time — i.e. “You are at Finnegan’s.” But if you traverse the graph of that association a level further, and if you start to examine the metadata surrounding that check-in, it becomes clear that that data reaches far beyond place and time. For example, there’s the fact that Finnegan’s is an establishment of type “Bar.” Or the fact that it’s after work, and that it’s happy hour. And then, of course, there are the drinks and the food that are at that bar — one of which you maybe tweeted about after your ordered. And there’s the neighborhood that the bar is a part of. And there’s all of the other people who are at the bar with you right now. And so on, and so on… until we’re no longer just talking about “You at Finnegan’s,” but instead we’re talking about ‘You after work at a bar called Finnegan’s in the East Village that serves Guinness and wings during happy hour, et cetera…” .
All of this is a long way of saying that there’s a ton of mostly unstructured hyperlocal data floating around out there that is immensely valuable to businesses, advertisers, developers, and pretty much anyone who’s building a business in the hyperlocal space. Or, I should say, it would be immensely valuable if only that data was structured so that one could ask, for example, for “The Twitter handle of everybody who, after work, goes to bars in the East Village that serve Guinness during happy hours.”
Enter Hyperpublic, a New York City based startup founded by Jordan Cooper and Doug Petkanics that wants to be the open platform for the web’s local data. Hyperpublic is a platform that “collects, organizes and structures geo-local information from the open web and makes it available to the world for free.” If you imagine the web as a giant database of unstructured data, then Hyperpublic is an index that is structuring relevant information by location (although there is presumably some hard core data gymnastics being performed in the background that makes all this possible).
Put another way, Hyperpublic is to a lat/long as Google is to a link (or as Facebook is to a friend). They are a platform in the sense that they do not provide a true consumer-facing product (yet), but rather provide data via their APIs to developers or companies that are building hyperlocal apps and websites. A company that wants to build an app that aggregates all daily deals in Brooklyn, for example, might use Hyperpublic’s platform to query for Brooklyn deals rather than scraping Groupon, LivingSocial, Amazon, and the zillions of other daily deal sites out there separately. In fact, GeoDealsandEvents (where developers actually get paid to display daily deals) is currently one of Hyperpublic’s three core products — the other two being Places+, which is essentially a POI database, and DataonDemand, which is kind of like a catch-all for ‘everything else’. Thus, at its essence, Hyperpublic is hyperlocal data.
The trick for Hyperpublic is going to be to figure out just how exactly to monetize all of this data at scale and beyond the crowded marketplace of daily deals distribution.
So why is this exciting? Besides enabling a slew of new daily deal aggregators, what might we see spring forth from the ether when local data is properly structured? It’s easy to imagine various applications of search, i.e. “Show me all wine bars that serve croquettes.” (N.B. this seems to be more or less where the thinking is headed at HyperpublicLabs, at least for the moment). Similarly, one can imagine that this type of data would be immensely valuable to advertisers who want to target, for example, everybody who drinks beer and is in walking distance of an establishment.
The trick for Hyperpublic, however, is going to be to figure out just how exactly to monetize all of this data at scale and beyond the crowded marketplace of daily deals distribution. We’ve already seen other companies, such as SimpleGeo (which was recently acquired by UrbanAirship), struggle to build a robust business model by productizing location data (to be fair, the sale was basically a partnership via acquisition, and a good move for SimpleGeo … but the acquirer vs. acquiree sides of the equation are telling). That particular wicket is made all the more sticky by companies like Foursquare that are essentially giving away venue data and customer insight to build an ecosystem around their core business.
So where do the opportunities lie? Part of Hyperpublic’s key value proposition as an aggregator is that they are data-source agnostic, and so can focus on building an infrastructure that allows them to correlate data at a massive scale. Similarly, by focusing on correlating all of the rich data available around a location, and not just on the venue, they broaden the scope of potential use cases. While a company like Foursquare might be interested in aggregating some data from other sources, they are principally interested in working within the scope of their own data. Aggregation and correlation, however, are Hyperpublic’s bread and butter, and it’s interesting to think about what’s possible when you start to look at that mass of data in the aggregate.
By leveraging data from across multiple platforms, Hyperpublic could think about building a decision engine that would be by definition more useful than one that focused on a single source of data (and, as eBay’s recent acquisition of Hunch has demonstrated, there’s always going to be a robust market for acquisitions of data platforms that can drive decision making).
Alternatively, forget about driving decisions about what’s happening right now, and imagine what’s possible if you can start to infer patterns of movements between people, places, and things. Conceivably we can start to envision services that would enable predictive analytics and future-optimization. For example, imagine a company that’s planning to open a new shoe store. Wouldn’t this company be interested in licensing data that could help them to predict traffic patterns and user tastes in various neighborhoods and that might influence their choice of locations? Or wouldn’t they be interested to be able to use this data for inventory planning, based on planned rather than perceived market demands?
The possibilities are, as they say, endless. It’s a great, big, exciting problem for a company that has the talent to take it on and the patience to solve it, and it’s not much of a leap to suggest that if a company like Hyperpublic can be the ones to solve it, they’ll redefine the scope of the possible in hyperlocal.