Intelligence is now about “Just not who Neel is but where Neel is”

Geospatial data is being used by both organisations and individuals as a new source of information to aid their decision making. Some examples include identifying routes, monitoring taxi movements, locating service centres etc.

Identifying optimal location for your service centre/outlet

Finding the location for your service centre, retail outlet, ATM, chemist, warehouse etc. has traditionally been based on gut feel of where high footfalls are likely to be. Adding location science to gut-feel can make a great impact on your top line.

Managers want to know their product market share within a locality. They may want to go even deeper – for e.g. They may want to know their product market-share in Morning-Star apartments? To answer this query, the sales person or MIS department will have to go through all the records and extract information of customers who are from that postcode and belong to Morning-Star apartments. They will spend time and effort in identifying the various spelling variations of morning-star in the data. The tabular analysis can be frustrating and time-consuming and ineffective.

Geospatial analytics, coupled with data from other sources, can be extremely helpful in identifying these hotspots. Historic data like time and geographic features can be coupled with geo-coded information and fed into a machine learning algorithm to predict probability of events.