SAP Predictive Analytics Goes Deep Down on Data Visualization

Businesses are facing the most diverse work environment that the world has ever seen with five different generations working together, across geographies — each with different skills, experiences and work habits. Many companies use predictive modeling to determine how customers will behave in the future by using a set of data describing the actions that occurred in the past.

Sap says all of this represents a major opportunity for productivity, talent development and employee engagement, but most companies are unprepared to capitalize on it. To tap market of data visualization, SAP updated its SAP Lumira data visualization software, as well as its SAP InfiniteInsight advanced predictive analytics software.

InfiniteInsight 7.0 adds to its predictive analytics solution the ability to use geospatial dimensions latitude and longitude as the predictive models. The support of geospatial data allows for example to examine the premise that customers visited based on their location. InfiniteInsight 7.0 also strengthens support for the application database by adding coverage for Hadoop Hive 11 and 12, with Greenplum.

InfiniteInsights 7.0 provides native support for large number of sources of data- SQL-add-on Hadoop Hive, scalable database Greenplum 4.2 from Pivotal, and geospatial coordinates and related information for XML-languages ​​Keyhole Markup Language (KML) and Geography Markup Language (GML).

According to the company, this allows SAP to bring the computing to the data instead of moving the data to an analytical computer. The software can combine any couple of variables containing latitude and longitude to create a variable of position that can be used by the modeler feature in SAP InfiniteInsight and with SAP InfiniteInsight Social. This allows you to make the most of geographic data and provides you with much more information than simply using ZIP codes. By eliminating the time it takes to move data, the analysis speeds up.

The tasks InfiniteInsights can do include forecasting operations support, rendering the probability churn to competitors, fraud, and social network activity. In addition, consumers can be better segmented data to suite business requirements. Users can now upload their models in tools such as Google Earth on Geo-data in the KML, GML or shapefile formats.

With the support of coordinates in the predictive models, companies can better predict which places will visit potential customers as next based on the analysis of historical data. Companies can also combine with new external data sources from all industries advanced analytics to generate new revenue and profit opportunities.

While much of the big data hype has focused on the storage, structured and unstructured processing technologies, some of the most exciting developments are in the fields of Predictive Analytics and Advanced Visualization. The “Exabyte Research Report” by London based investment bank GP Bullhound noted that 17 percent of information processing individuals eventually use big data analytics and the number is expected to progress to over one third of information workers by 2016.

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