Let’s say that your company is a major insurance company (or really any company). The powers that be want to increase sales by 20% in various regions. You have life insurance data for tens of thousands of customers in the Midwestern US. You have their addresses and other personal information plus data on their existing policies.
When you view this information in a table or multiple tables, there is really no effective way of correlating all that into a data image that can be used to improve sales. So you dress it up in a set of graphs. That’s better. Now you know what you are looking at. But does it help you figure out how to increase sales? It might give you an idea. But you’d still need to play around with the relationships to get an understanding of what might be most effective.
But then, you have a great idea. You can map all the data to a map. That way, you can see the concentrations of customers with various policies and conditions and use layers to model different parameters for different concentrations. You can run a historical trend on the map to see where changes in sales behavior is happening and learn how one region or neighborhood has reacted to marketing over time. Try that in a table or a graph. With these insights in hand, you can more effectively plan a new marketing campaign that targets the areas that are most likely to respond positively, thus increasing sales.
Of course, you can do this only if your BI solution provides live Geo-Analytics. Layered, map-based Geo-Analytics are not always relevant for all BI analysis scenarios. But when you have stacks and stacks of geographically oriented data, there is no faster way to get to the insights that help you make good business decisions.
Check out this short video from Panorama to learn more about how easy Geo-Analytics can be to use and how they speed up decision making.