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Fern Halper

By Fern Halper

Blog archive

Three ways to use geospatial data in analytics

Geospatial data can be extremely powerful for a wide variety of use cases. Geospatial analysis – i.e. the practice of incorporating spatial characteristics in various kinds of analysis- has been incorporated in BI and visualization solutions for at least several years.  Recently I’ve been hearing a lot from vendors about geospatial applications and using geospatial data in a range of more advanced analytics.   In a recent TDWI technology survey, you can see that the geospatial analytics is growing in importance.  We asked the respondents, “What kind of analytics are you currently using in your organization today to analyze data? In three years?” and “What kinds of techniques and tools is your organization using for big data analysis both today and in three years?”  In the figure below, the 39% of respondents were currently using geospatial analysis and this number jumped to 79% in three years.  The number of respondents answering affirmatively that geospatial analysis would be used in their big data solutions in three  years was 81%. 

Companies are becoming excited by the prospect of incorporating geospatial data into analysis.  In terms of more advanced analytics, here are few examples of the kinds of analysis I’ve been hearing about:

  1. Predictive analytics. Geo-location data is being incorporated into predictive analytics.  The most recent example of this is KXEN (www.kxen.com) announcing support for geo-location in its predictive analytics solution.  KXEN will support data sources such as GPS, phone calls, machine sensors, geo-referenced signals, and social media.  This includes capabilities for location-awareness, co-location, and path identification.  For instance, marketers could use this kind of data to determine how likely someone is to purchase something at a store based on the path they took to get there (based on what others who took similar paths with a similar profile did) and offer a coupon based on this.
  2. Operational Intelligence.  Operational intelligence incorporates analytics as part of a business process.  Companies like Vitria (www.vitria.com) that provide operational intelligence solutions, support geospatial data.  For instance, a wireless telephone company that monitors its network could use location data to determine which cell tower to fix based on where their high value customers are located.
  3. Situational Intelligence.  Situational intelligence is a technique that integrates and correlates large volumes of multidimensional real time and historical data to identify and act on a problem. Part of that data is often geospatial For example, companies like Space Time Insight (www.spacetimeinsight.com) provide this kind of solution to help companies that deal with physical assets.  Visualizing and analyzing this data can help answer questions like what happened, where did it happen, and why did it happen. Companies such as utility companies would use this information to a pinpoint problem and find the closest person to fix it.

I expect to hear a lot more about geospatial data and geospatial analysis moving forward.  I’ll be writing more on the topic in the second half of 2013 and in 2014.  Stay tuned.


Posted by Fern Halper on July 1, 2013


Comments

Tue, Nov 26, 2013 Mo

Fern, thanks for this important post. Looking forward to your follow up at the end of this year, if you are going to do one.

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