Read the paper titled “Enhancing Your BI Systems with Location Analytics”

And address the following in 500 words (in two pages regular/Standard Margin):

Section 1 – Overview / summary of the reading – this may include: 

• What are the key points?
• What was learned?
• What are the most important issues? 
• Why is it important (or not)?

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Sponsored by

Enhancing Your BI Systems
with Location Analytics: The
SAS® Vision

Location data has long been a big portion of business data, although it is rarely used
to analyze business metrics. Not many business analysts have taken advantage of
incorporating location data into their analytical workflows. Traditionally, location data has
been used solely for mapping and GIS purposes.

Most analytical workflows are focused on business data. Organizations try to glean insights
from business data by using analytical algorithms to predict trends. In many cases, these
analytical workflows and business intelligence (BI) systems are silos and are not integrated.

Typical BI reports and dashboards contribute to one part of business insights. Advanced
analytics, like forecasting and predictive analytics, add a second layer and produce deeper
insights. A third dimension can be added to business insights by using location information
and augmenting location data with demographic and lifestyle data to better understand
everything from consumer purchasing patterns and habits to trends in health care issues
like diabetes.

At SAS, we see location analytics as not just putting points on a map. Rather, it’s part of
a much larger picture of using location context for analysis in graphs, tables and visual
analytics. Presenting the results of predictive analytics along with location data on maps
provides easy-to-understand visualizations and helps everyone better understand their
business because most people find maps very easy to comprehend.

Here is one example of how location data can be used to drive downstream analysis. Imagine
you’re looking at sales data for different products, like shoes, childrens’ clothes and toys.
Analysts can place the data on geographic maps to see where customers are located. They
can produce geographic clusters of customers, find out where the most profitable customers
reside, and create reports in BI systems that contain sales, profits and other metrics. And,
analysts are able to select an area on a geo-map and use the data points to perform further
analysis in their BI systems. The ability to use geo-maps to produce ad-hoc visualizations and
analysis helps business managers identify customer patterns and drill into more details of
the business.

Now let’s look at a different situation—how to use analytics to create location-based
visualizations. Business managers always want to know projections for the next quarter, the
next six months and the next year so they can plan their resources and adjust expectations
accordingly. SAS enables you to do predictive analysis, like forecasting of sales, and then

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Enhancing Your BI Systems with Location Analytics: The SAS® Vision

use the forecasted results as visualizations on maps. This allows business managers to
see and compare current data and predicted projections for the future. By using location
information on maps, business managers can clearly see how customer clusters are
changing over time and better plan distribution and logistics.

At SAS, we see location analytics as an extension of BI. We like the idea of using location
data for mapping and driving downstream analysis, as well as using analytics and location
information to drive mapping visualizations. Location analytics provides yet another way for
business users to dig into data and get a visual understanding of information to realize the
ultimate goal of making better business decisions. 