SAP DI
SAP was founded in 1972 as a business applications company but really came into prominence in the 1990s with the ERP boom. Its ERP solution (R/3) was first released in 1992. The vendor employs more than 100,000 people worldwide and had revenues of $27.84 billion in 2021. Today, SAP is one of the largest business software vendors in the world.
SAP Data Intelligence is SAP’s technology platform. It supports the development, operationalization and operation of data-driven processes. Data Intelligence embeds itself in the HANA-centric solution portfolio but does not depend on HANA. The aim of the tool is to support the complete iterative development of data and analytics processes. This extends from the connection of data sources to the development of data pipelines, analytics models and the entire workflow through to operationalization. SAP Data Intelligence has been on the market since autumn 2017 and is currently available as an on-premises version or a cloud edition. The product is subject to very agile further development to meet emerging customer requirements at short notice. In addition, it is not yet easy for users to compare Data Intelligence with other data and analytics-oriented solutions.
SAP’s Enterprise Information Management (EIM) portfolio combines its strategic solutions for data management and preparation. The SAP EIM tools have been developed for specific application areas. SAP Data Intelligence covers additional application areas with new functions. It can also use and combine established solutions for developing advanced analytics processes. However, its positioning in relation to the SAP EIM portfolio is still complicated by the seemingly overlapping data integration functions across SAP Data Intelligence, the other EIM tools and the HANA portfolio, such as HANA database, SAP Data Services, SAP HANA Smart Data Integration, SAP System Landscape Transformation Replication Server (SLT) and SAP Information Steward. Depending on the source and target systems and the application scenario, various combinations of SAP integration tools are possible. In this regard, SAP Data Intelligence can be compared to a classic data integration platform only to a limited extent. While a DI platform usually focuses on concrete transformation support, SAP Data Intelligence specializes in the combination of many sub-processes from different environments and tools and adds advanced metadata and governance capabilities. This is functionally broader than classic data integration processes, especially through the integration of machine learning components. The processability of machine-generated and human-generated data also sets it apart from classic DI tools.
However, SAP Data Intelligence is part of SAP’s family of Data and Analytics Cloud solutions, which also includes SAP Data Warehouse Cloud and SAP Analytics Cloud. The database foundation for all – SAP HANA Cloud – delivers the required elasticity and performance. SAP is currently addressing a deeper family integration with ‘Project Data Suite 2023+’, marking the shift from dedicated products to integrated services.
User & Use Cases
SAP Data Intelligence offers a broad range of functions. 73 percent use the tools for data integration or to build data pipelines and thus also the capabilities to orchestrate sub-processes. 33 percent use it in the context of advanced analytics use cases, either for data engineering or as a framework for developing and operationalizing models. Its use in the field of data governance is also interesting. Here, the effect of the continuous enhancements for metadata and data governance is evident. It remains to be seen how the release of SAP One Catalog, planned for the end of 2022 / beginning of 2023, will affect the use of SAP Data Intelligence.
80 percent of customers are large enterprises and the product scales from small user scenarios to large deployments with well over 2,000 users. Surprisingly, only 7 percent report using the tool company-wide. It is mostly used across multiple divisions or for specific use cases. Against this background, the figures for ‘number of users’ and ‘extent of usage’ seem somewhat contradictory, even if the mean of 2,000 users were to comprise mostly consumers of data or information. The figures for the number of developers using the tool, which indicates the number of technical experts who actually work with SAP Data Intelligence, seem more meaningful.
Use cases
n=30
Extend of usage in the company
n=28
Total number of users per company
n=30
Total number of developers per company
n=30
Company size (number of employees)
n=30
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