“Without being Orwellian about it, your credit card and the information people have about you is like a little GPS tracker,” says Simon Thompson of Esri, a California company that provides geographic information system technology. “When you start mining geospatial data, these patterns become highly relevant.”
Remember geography lessons in school, painstakingly memorizing the longest rivers, cultures of various regions, the state capitals? For most of us, those lessons are in the past. But in the world of big data analytics, geography is making a comeback.
The relatively new market of location analytics is expanding the uses of more traditional geographic information system (GIS) technology to include social, geographic, physical and emotional indicators that help organizations better predict trends, according to ABI Research, which forecasts that the location analytics market will grow to $9 billion by 2016.
This isn’t news to some of the world’s largest technology companies — Apple, Google, Nokia, Facebook, Microsoft and, most recently, Cisco — each of which has snapped up location related analytics vendors in the past year. In a conversation with MIT Sloan Management Review contributing editor Renee Boucher Ferguson, Simon Thompson, Director of Global Business Solutions at Esri, a Redlands, Calif. based GIS provider, discusses the future of location analytics and why it’s relevant to organizations today.
Can you talk a little bit about geospatial technology, where location analytics fits in, and how it is being used?
Geospatial data and geospatial technology have been around for 30-plus years. And really the basis of it is about exploiting the idea of connections based on the location of things: Why they got there, how they got there and what does this mean to society and to business.
In the early days, a lot of geospatial technology was about collecting information. So local governments realized that if they understood the roads, and then if they understood how the houses connected to those roads and the property lines and the parcel information, and then the values of the houses and how they were connected to the sewers, and then where the rivers were that might flood, or the population age of the people living in those houses — if they had all that information, they could provide better services.