Consumers today increasingly see the world through their phones. Parents bemoan the fact that their children live their whole lives through their phones.
Phones are by their owners very nature mobile- walking down Oxford Street in London look out for and dodge pedestrians engrossed in their phones. All the searching, buying, selling, interacting moves and dropping location data onto maps is no longer enough.
Retail, travel, hospitality and transportation know that location based services are the key to competitive differentiation.
Insurance carriers are increasingly focussed on the gig economy and micro-insurance services e.g. for uber style travel and coming autonomous vehicles.
Banks and Credit Card Service providers need to map and predict spend by sector, time and location.
Location Intelligence describes the process of combining this location data with enterprise data for a complete view of consumers, retail behaviour and life on the move .
And by its very nature, it will be big data.
The missing or under used component has been leveraging location analytics, or the where factor to further improve decision outcomes. While typical BI systems handle the who, what, when and how factors, the combination of BI and geographical information systems allow for new types of analyses by adding the where factor to analyses, and doing so in the context of all the others. Adding the location context when analyzing business data allows for revealing spatial relationships, trends, dependencies, and patterns that may have been undetectable in a traditional enterprise applications or BI.