As part of the Spotlight series, we will investigate one of SAP Datasphere’s key components: The Analytic Model. Some of the features you can expect from Analytic Model include:
Developing an Analytic Model in SAP Datasphere
Rich Measure Modeling
With calculation after aggregation, restricted measures & exception aggregation as well as the possibility to stack all of these, users can build very complex calculation models and even refine them in SAP Analytics Cloud stories.
Calculated measures are calculations based on other, already existing measures – like source measures, restricted measures, or other calculated measures. This means that these other measures are calculated first and then the calculation is performed. That’s why calculated measures are often called “calculation after aggregation”: the aggregation of the other measures are done and only then does the calculation start.
Restricted measures build on existing Measures but apply flexible filter expressions. The possibilities to work with Calculated and Restricted Measures is not new at all. However, this functionality was not available in SAP Datasphere until now! For more info, please navigate to the Calculated & Restricted Measures blog.
Exception aggregation can be added in order to express complex subquery relationships. Typical examples include counting customers w special properties, reporting warehouse stock levels that cannot be aggregated along the time axis or reporting on the total sales of best-performing products. For more details please check the Exception Aggregation blog.
How Analytics Users View Data
Modelers can curate which measures, attributes and associated dimensions to expose to users. This helps analytics users to see exactly the data that is relevant to them, reduces likelihood for errors & boosts performance. More information about the analytic model editor is given in this blog.
User Input Via Prompts In SAP Analytics Cloud
These can be used for subsequent calculations, filters & time-dependency. Value helps are provided too, of course. Variables are mutable values that are captured by a user and used for calculations and filtering within the Analytic Model. When adding a variable, the user is prompted to enter a value for the variable in the data preview in SAP Datasphere or when consuming the Analytic Model in a story in SAP Analytics Cloud. Created variables must be used within the Analytic Model, otherwise an error message will be displayed. For more info, please navigate to the Using Variables blog.
Rich Previewing Possibility
Users can inspect the result of their modelling efforts in-place because the Data Analyzer of SAP Analytics Cloud is tightly embedded into the Analytic Model editor. So slice & dice, pivoting, filtering, hierarchy usage and many more features are available to help users understand the data how it’ll be presented for consumption. Previously, the only way you could preview data was by navigating to SAP Analytics Cloud and create a story. Now, we can leverage the Analytic Model’s Preview feature to preview data without the need of creating a story, which increases the user experience, but also saves valuable time when modeling. For more info, please navigate to the Data Preview blog.
Master data often changes over time. This is known as “time-dependency”. Analytic Models support this critical feature to let users travel back & forth in time while Lines of Business, structures & organizations are constantly evolving. The Analytic Model allows for rich analytical modelling in a targeted modelling environment and is the analytic consumption entity for SAP Datasphere. For more info, please navigate to the Time Dependency blog.
Complex analytic projects require careful planning and a sophisticated toolset for managing the dependency and lifecycle of all modelling artefacts. The Analytic Model is fully integrated into the SAP Datasphere repository and thus benefits from impact & lineage analysis, change management & transport management. It’s multi-dimensional by design and allows for rich modelling via rich semantics, thus making consumption of analytical data very simple for tools like SAP Analytics Cloud. It does so by combining a rich expressiveness for measure modelling, flexibility in designing how users will consume the data and how to collect input from them with a powerful data preview environment, time-dependency support and professional management of artefacts along their lifecycle.