SAP Predictive Analysis is the latest addition to the SAP BusinessObjects suite and introduces entirely new functionality to the existing Business Objects toolbox. Predictive Analysis integrates SAP’s Visual Intelligence data visualization tool with new predictive functionality powered by both open source R and SAP-written algorithms. Predictive Analysis includes algorithms for time series forecasting (for predicting sales, demand, price, and other time-dependent metrics), clustering (for identifying distinct groups of individuals based on numeric descriptive data), decision trees (for creating a tree-like set of decision support rules to categorize observations), and linear regression (for fitting linear relationships between a dependent variable and one or more predictors). These predictive algorithms can be used to extract insights and predictions, improving the value and actionability of the existing Business Intelligence infrastructure. Predictive Analysis combines these powerful predictive algorithms with a familiar and easy-to-use tool that integrates with existing BusinessObjects tools to make data preparation, model building, and implementation faster and easier than ever before.
SAP Predictive Analysis is installed locally on the user’s machine and accesses data for processing locally (from a CSV, Excel, or ODBC connection to a database) or on SAP HANA. Predictive Analysis can be used alone to analyze data on the client machine or can be paired with HANA, allowing PA to leverage the powerful in-memory processing power of SAP HANA.
On March 29, 2013, SAP released the 1st Quarter 2013 release of Predictive Analysis, version 1.0.9. This upgrade includes bug fixes and increased stability, as well as some new visualizations, specifically for apriori and decision tree algorithms.
New Model Result Visualizations
Of particular note is the new confusion matrix view included in the decision tree output. This is a new visualization, automatically-generated after a decision tree is run for both the HANA and local R decision tree algorithms. The confusion matrix is one of the key evaluation tools for a decision tree model—accuracy, false positive, and false negative rates are generally the first things at which an analyst looks after the visualization of the tree itself. Prior to this feature being added, the user had to create the confusion matrix manually in the Visualize portion of the Results output. However, it was unattractive and only allowed a vertical orientation, whereas the traditional cross-tab confusion matrix is much easier to read. I wish that SAP had included a few more metrics in the confusion matrix display, such as row/column percentages and an overall accuracy measure.
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About Hillary Bliss
Hillary is a Business Intelligence Consultant specializing in data warehouse design, ETL development, statistical analysis, and predictive modeling. Hillary work with clients and vendors to integrate business analysis and predictive modeling solutions into data warehouse based on their data and business needs. With Decision First Technologies, Hillary uses Data Services, Web Intelligence, Predictive Analysis, and HANA. Hillary has a Master’s in Statistics and an MBA from Georgia Tech.