Location is a key attribute for data analytics.

For many years, location has been revered as a key attribute for data analysis – which is as true as ever – and new data sources such as social media have made this even more relevant.


Every user of a smartphone generates a continuous trail of data which is being analysed on a daily basis by the more sophisticated organisations involved with data analytics.

On a more mundane level, banks and retail organisations with bricks-and-mortar outlets keep track of where their customers live for door-to-door deliveries, for target advertising and, when combined with external data (such as post-code demographics), for retail network optimisation and similar corporate decision-making.

GIS technology started on the desktop and then migrated into enterprise software using central – or shared – data resources. However, there is an increasing move to put GIS functionality back into the hands of individual users. This means that, progressively, coverage (penetration) maps, delivery &/or routing maps, etc. are being produced by individual users and true geo-spatial analytics are moving into the realm of Big Data and predictive analytics for delivery to the C-suite.