BSM 99 Enhancing the BI Systems with Location Analytics Paper

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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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 analyse 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. Organisations 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 visualisations 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, children’ 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 visualisations 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 visualisations. 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 fore-casted results as visualisations 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 visualisations.

Location analytics provides yet another way for business users to dig into data and get a visual understanding of information to realise the ultimate goal of making better business decisions.

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