Already incredibly useful for helping us get directions, find the nearest grocery store and find out our state capital, Google Maps is now becoming the hot way to display enterprise or organizational data that’s associated with particular places. As a data visualization method, the timing of this trend isn’t surprising. The concept of big data has opened organizations’ eyes to the value of their myriad data sources — many of which are tagged with geo-location information — and now is opening up new ways to process and display that data.
IBM’s Jeff Jonas described the importance of geospatial data at our Structure: Data conference in March, calling it “prediction super-food.” You can watch the video below to get the full (and rather entertaining) explanation, but here’s a summation: geospatial, or space-time, data adds context to the information we already have, allowing us to make better decisions. Using a puzzle analogy, lots of data without context is like a pile of puzzle pieces, but lots of data with context is like those same puzzle pieces coming together to complete the picture.
Geospatial adds an incredible amount of context. It allows for complex tasks such as tracking of people as they go about their business to help determine who’s connected to whom, or predicting where someone might go next and what’s the best route to get there. If we’re talking about a spreading disease, Jonas explained, geospatial data helps us determine its vector and velocity.
This is where Google Maps comes in, because it presents an intuitive way to visualize and consume that data. You’re not just looking at times, places and other information in text form, but you’re seeing it in relation to time and space.
Last week, for example, I covered the aptly named Space-Time Insight, whose product overlays real-time data atop Google Maps (among other interfaces). It lets customers visualize what’s happening and then act accordingly based on whatever their needs happen to be. California ISO, for example, uses Space-Time to see where wildfires are burning and determine where they’ll travel next, as well as to monitor energy prices and conditions in numerous locations and adjust the grid supply accordingly.
Wednesday, SAP announced a similar partnership with Google that lets SAP applications overlay their data on Google Maps. To demonstrate the breadth of possibilities for data-plus-maps mashups, SAP suggested a handful of possible scenarios:
- A telecom operator could use Google Earth and SAP BusinessObjects Explorer software to perform dropped-call analysis and pinpoint the geo-coordinates of faulty towers.
- A state department of revenue could overlay household tax information on a map of the state and group it at the county level to track the highest and lowest tax bases.
- A mortgage bank could perform risk assessment of its mortgage portfolio by overlaying foreclosure and default data with the location of loans on Google Maps.
- With SAP StreamWork, a team of customer support representatives in a consumer packaged goods company could collaborate and pinpoint the location of consumer complaints within specific geographies and make a decision regarding how to address and prioritize resolutions.
- A theme park operator could use the Google Maps API Premier and get real-time traffic information on attractions with SAP BusinessObjects solutions to send rerouting messages to customers in order to improve satisfaction rates.
- U.S. census data could be overlaid on a Google map of the country, grouped by state and drilled down on at the county level.
Something tells me we’re only getting started when it comes to fusing big data, advanced analytics and next-generation displays. The truth is that we’re still a long way from mastering the capture and analysis of big data streams, which arguably are necessary steps before tackling the visualization issue. It’s difficult to even imagine how we — or our machines — will be consuming data 10 years from now considering how far we’ve come in the past few years. But we’re off to a very good start.