SAP Predictive Analytics: The End Result of a Long History of Data-Mining Tools from SAP (Part 1)

  • by Ned Falk, Senior Education Consultant, SAP
  • July 1, 2015
This is the first installment of a series of three articles that help with understanding the basics of the SAP Predictive Analytics toolset and its history. In this part, learn about the automated analysis component of SAP Predictive Analytics (specifically, a classification analysis) and see how it can be used to generate money-saving analysis of your data.
Learning Objectives

Reading this article, you will learn how to:

  • Describe the available SAP tools related to data mining and predictive analysis of views
  • Understand the purposes and target audience for SAP Predictive Analytics and its sub-components: Automated Analysis and Expert Analysis
  • Understand how to create an automated classification analysis using basic functions of SAP Predictive Analytics
Key Concept

In this first part of a series, Ned Falk discusses all the data-mining/predictive analysis tools SAP offers (for a wide perspective) and then focuses on one of the newest tools, SAP Predictive Analytics. Learn how to use the Automated Analysis component within SAP Predictive Analytics (previously marketed separately as SAP InfiniteInsight) to classify your customers as well as to predict the profile of a customer who would actually buy your products.

SAP has a long history with data-mining tools. The newest tools provide robust statistical analysis with guided user interfaces (GUIs) that are very easy to deploy and understand. The newest toolset, SAP Predictive Analytics, is the culmination of this long history. In this article I cover the basics of what you need to know to best use this tool.

A Definition of Data Mining and a Brief History

For many, the term data mining means drilling down or selecting more rows of data. For example, most people would think that (using an output listing sales by month and customer to illustrate) filtering on a specific month and drilling down to the part numbers for the customer in that month would be an example of data mining. However, that is not what data mining is at all. Rather, a common definition of data mining (from is “the practice of searching through large amounts of computerized data to find useful patterns or trends.”

Over the years, the ability to mine data has spread, moving from being the domain of a few math geeks at universities with supercomputers on to business experts who apply easy-to-use data-mining tools to basic business situations. Over the course of my career I have worked with data-mining tools at SAP for many years. The first SAP toolset for data mining was SAP BW-based, but its development was driven by the SAP CRM team because data mining is a central focus for improving CRM business processes in many ways. For example, using:

  1. Clustering to group together customers for targeted marketing
  2. Decision trees to predict buying behavior
  3. Association analysis to suggest additional secondary products to purchase when a primary product is selected

This original toolset for data mining consisted of the Analysis Process Designer (APD) and the Data Mining Workbench. Both of these tools were part of the basic license cost of SAP BW and were highly integrated with SAP CRM, features that made this tool very compelling for companies.

Ned Falk

Ned Falk is a senior education consultant at SAP. In prior positions, he implemented many ERP solutions, including SAP R/3. While at SAP, he initially focused on logistics. Now he focuses on SAP HANA, SAP BW (formerly SAP NetWeaver BW), SAP CRM, and the integration of SAP BW and SAP BusinessObjects tools. You can meet him in person when he teaches SAP HANA, SAP BW, or SAP CRM classes from the Atlanta SAP office, or in a virtual training class over the web. If you need an SAP education plan for SAP HANA, SAP BW, BusinessObjects, or SAP CRM, you may contact Ned via email.

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