Introduction to Data analytics and Cross analytics
In addition to process, function, and interaction analyses, you can now evaluate process-independent data using Data analytics. Data analytics enables the analysis of comprehensive data in table format, consisting of multiple linked tables. In Data analytics, analysis criteria, that is, dimensions and measures, are based on the table structure of the data basis, with each table column representing an analysis criterion. In combination with the relevant configuration, the tables form a separate analysis realm. Within the analysis realm, all tables are interlinked by means of association criteria. An association criterion is a criterion in the configuration of an analysis realm that is used for linking (associating) a table with another table.
Cross analytics enables users to combine Data analytics and process analysis. Data tables are linked with a process table in a mixed analysis realm. A mixed analysis realm in Cross analytics is basically a Data analytics analysis realm with additional, specific process criteria.
Data analytics and Cross analytics provide multiple independent analysis realms for your analyses. The individual analysis realms usually have their own data basis as well as specific dimensions and measures. The analysis tools and the procedure to create queries are mostly the same as in process analyses and interaction analysis.
The tools available and the configuration of your analyses in Data analytics and Cross analytics are basically identical with those of process and interaction analysis. However, in Data analytics and Cross analytics, several specific functions and contents of the process and interaction analyses are not available.
For detailed information on Data analytics and Cross analytics, please refer to the PPM Data Analytics documentation.
The Data analytics and Cross analytics analysis realms provide you with all functions and tools required for running your analysis.