SAP Data Intelligence

Working with data inevitably means organizing it. Especially in advanced analyses with artificial intelligence (AI), the combining, preparation and cleaning of the data determine a large part of a analytical project. In order to achieve the defined analysis goal, different data formats such as streaming data, relational data and Excel files have to be joined. The merging and data preparation is ad-hoc and must withstand ever-increasing data volumes.

While data integration and preparation is also possible in traditional systems, these are mostly specialized for expert users. Modern data management platforms for AI projects, however, are a place for collaboration and bring diverse users close to data. Visual representations of processes, a flow supportive user experience, and a low-code mentality keep barriers to entry low and clear the way for self-service analytics.

SAP Data Intelligence

‣ Data Integration
Unite different types of data with ETL or alternative ELT processes. Operational connections to SAP systems as well as non-SAP systems are available. Data from an Excel spreadsheet can be used in the same way as from an IoT stream.

‣ Organized overview of your analysis data
The data is centrally bundled in the data management solution. An integrated data lake is available for heterogeneous file formats. Comprehensive metadata management and transferable rules to ensure data quality help with the necessary organization.

‣ Data Orchestration
In the Modeler component, you can create any desired complex workflow for your data processes using drag-and-drop. The visual representation ensures transparency and a recognition value of the operatores and steps.

‣ Ready for Artificial Intelligence
The data science process is optimally supported by an interaction of various components. With the ML Scenario Manager, the exploratory data analysis, the workflow and the models including the associated target metrics are provided in a versioned manner.

‣ Free creation with R and Python
A built-in JupyterLab instance allows scripts in the programming languages R and Python to be integrated. SAP is thus opening up to the rapid change in open source technologies and providing advanced users with up-to-date algorithms.

‣ Reusability
SAP Data Intelligence emphasizes reusability. Automatic rules to ensure data quality and workflow parts, as well as user-defined operators, are easily transferable. Development time benefits from the very beginning due to numerous templates for diverse application scenarios.

‣Automatic Scaling
With a strong backend of well-proven SAP technology in combination with open source systems such as Kubernetes and Docker, the technical realization of data processing is no longer a concern. Work is performed on a data extract and the overall processing is started at the push of a button.