SAP Predictive AnalyticsPerfect predictive models to identify patterns
SAP Predictive Analytics is a statistical analysis and data collection solution for building predictive models that reveal hidden patterns and relationships in your data, from which you can predict future events.
At the heart of SAP Predictive Analytics is the Data Mining approach – revealing hidden patterns or relationships between variables in large arrays of raw data.
Data mining is of great value to managers and analysts in their daily activities. Data mining is usually divided into solving problems of classification, modeling and forecasting.
Data Mining includes methods and models of statistical analysis and forecasting. Data mining tools allow data analysis by subject specialists (analysts) who do not have the appropriate mathematical knowledge.
Automated Analytics includes the modules:
Data Manager
It’s used to facilitate the preparation of the data to be used in the analytics project.
Modeler
Enables the analyst to create in a homogenous and easy-handling workflow models such as classification, regression, clustering, time series, and association rules. Models can be exported in different formats so that you can easily apply them in your production environment.
Social
Extracts and uses implicit structural relational information stored in different kinds of datasets, improving the decision and prediction capacities of the models. It can represent data in the form of graphs that show how the different data are linked.
Dedicated Workflows
Help you to create colocation and frequent path analyses based on geo-referenced data.
Recommendation
Generates product recommendations for your customers based on a link analysis provided by Social.
Expert Analytics enables you to do the following:
- Produce deep analysis of the data using different visualization techniques, such as scatter matrix charts, parallel coordinates, cluster charts, and decision trees.
- Perform various analyses and build models on the data, including time series forecasting, outlier detection, trend analysis, classification analysis, segmentation analysis, and affinity analysis.
- Use a range of predictive algorithms, the R open-source statistical analysis language, and in-memory data mining capabilities for handling large volume data analysis efficiently.
