SAP Data Intelligence - SAP enables self-service AI-projects

Self-service solutions are also becoming increasingly popular in the field of artificial intelligence (AI). While experienced data scientists work on the complex problems and enable new business models based on data, many business issues and the analyses based on them can also be solved by business users. These users have in-depth domain experience and can therefore better interpret the insights gained. A supporting platform paves the way for specialist users. By taking over deployment - and scaling, which is becoming more important due to Big Data - a self-service for AI-projects is being developed.

Our whitepaper "Self Service AI-Projects in SAP Data Intelligence" is primarily about the introduction of SAP Data Intelligence for data management in AI-projects in companies. The related challenges and trends are presented and reflected. To subsequently help you decide on a suitable data management system, we have compared SAP Data Intelligence with the use of an open-source-based architecture. In our presented application scenarios, you will also get an in-depth insight into the wide range of possible uses and also learn about the system's limitations. 

Download the whitepaper and learn why you should establish self-service in AI-projects with SAP Data Intelligence from now on.

SAP Data Intelligence as a
data management solution for AI-applications

SAP Data Intelligence as a self-service system 

As a self-service system, SAP Data Intelligence brings community thinking in addition to automating and simplifying key steps in the AI-project. In the data management system, data, users and analyses are all connected with each other. Used correctly, for example, a prepared OpenData dataset can be shared company-wide and multiple places can benefit from it.
 

Data management and artificial intelligence combined

SAP's data management system has been enriched with useful features in the field of AI. Specifically, it covers data merging, data preparation, the analysis and modeling step, and model deployment. 

Users merge different formats, such as streaming data, relational data and Excel files, using the self-service platform. Then, analyses are implemented on a low-code basis and finally run regularly with new data. Thus, the end-to-end lifecycle of an AI-project is mapped in the system.

Business users and AI experts in self-service systems

While expert users as AI beginners benefit from the intuitive usability and many workflow templates, more comprehensive functions and as well as optional extensions by own code are available for experts. Depending on the number of users and ongoing analysis processes, the system can be scaled appropriately in the manufacturer's or the company's own cloud.

If you are facing a concrete problem or have specific questions about this or other topics, send us a message or make an appointment for an exchange. We are happy to assist you in the strategic selection of a data management system - open source architecture or licensed - and support you in the implementation and conceptual design of initial use cases.