Understand the need to start considering map representation as one of the mandatory visualization need for an Analytical initiative.

1. Ask for Maps – Location Analytics!
When we look at a metrics performance, we are interested with three W’s ‘What happened’, ‘When it
happened’ and ‘Where it happened’.
The ‘What’ informs about the metric’s health whether it’s doing good or bad against a pre defined
threshold. The ‘When’ informs the time period of measurement whether it’s current day or YTD etc. The
‘Where’ informs the location to which the depicted metric belongs. Unlike the other W’s, the ‘Where’
part need not directly relate to an address location, it can refer to a store or a branch name or a
department which in turn has a direct relationship to a location.
A graphical representation in terms of a geographical Map for the location factor dramatically improves
a user’s ability to relate to the information that is depicted. Had recently come across a practical
scenario on a how a tabular logistics data converted to location maps had a direct impact on the users
interpretation. Check out these links on the Maps and Data representation
• http://earth.google.com/outreach/tutorial_fusion_yourowndata.html
• http://www.microsoft.com/mappoint/en-us/home.aspx
For every industry location plays a very vital role, it’s not just for retail, telecom, internet businesses.
With the global network of data collection points increasing like mobile devices, kiosks, the ability to
garner location information has also increased, there by pushing for a need to understand a metric from
the location perspective. A separate set of analytics called Location Analytics has emerged in last few
years like http://www.locately.com/ which help in the analysis of location information.
Still now support for Maps is an additional option and involves some amount of specific configuration,
an ad hoc Map report cannot be created, but very soon Maps will become like a type of graph a ‘bar’ or
‘pie’ which can be chosen during ad hoc reporting. Such Map graph type can understand which of the
attribute is ‘location’ aligned, understand the dependency and map it.
Next time you start on an Analytical initiative consider map representation as one of the mandatory
visualization need.
Thanks for reading, let me know your thoughts.