Data is the foundation of modern companies - especially in volatile markets and complex supply chains. For decision-makers and IT strategists, this means that they need to provide systems that not only guarantee robust data availability, but also enable fast and well-founded decisions. The SAP Business Data Cloud (BDC) offers a technical solution for precisely this: a centralised data platform that relieves the burden on IT departments and at the same time strengthens specialist departments through self-service functions.

What is the SAP Business Data Cloud?

The SAP Business Data Cloud bundles the existing SAP Analytics Cloud and SAP Datasphere products and supplements them with Databricks, Foundation Services and Joule as an AI solution. Combined in one integrated product, the BDC enables the seamless consolidation and centralisation of data, whether physical or virtualised, in a central catalogue. It also offers clear migration paths for modernising existing SAP Business Warehouse (BW) systems as well as predefined content such as data products and insight apps. For IT departments, this means a reduction in the effort involved in data integration and the development of reporting applications, as well as the ability to bring existing BW system landscapes into the cloud.

Modernisation of the SAP BW systems

The modernisation of existing SAP BW systems is a key issue for many IT departments. BDC offers a clear migration path to BW Private Cloud Edition (PCE) for this purpose. IT managers benefit from the seamless integration of existing BW data into the Business Data Cloud. This allows companies to approach the transformation to the new solutions at their own pace and at the same time enables investment in the existing BW environment by allowing established solutions to continue to be used.

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Holistic data platform for IT and specialist departments

With the Business Data Cloud, SAP is expanding its offering with a comprehensive solution for AI, data, reporting and planning.

The SAP Business Data Cloud comprises established, mature products such as SAP Analytics Cloud, Datasphere and Databricks and complements these with the strong integration of the AI solution Joule. As a central platform, the BDC enables the consolidated provision of data and offers powerful self-service functions for various user groups, from specialist departments to data science and data engineering teams. This creates a common basis for developing data-based solutions efficiently and in short iteration cycles.

With the right initial use cases and the right implementation strategy, the SAP Business Data Cloud can help to accelerate the implementation of the data strategy and the shift towards data-driven decisions.

Insight Apps: Directly usable applications

With Insight Apps, BDC provides ready-to-use reporting and planning applications. These can be flexibly customised and expanded as required. By reducing the time between identifying a reporting requirement and making it available, BDC supports rapid data provision and thus effectively relieves the IT department in the development and operation of analytical solutions. In addition, IT positions itself as an enabler for data-driven work by providing the specialist departments with applications that can be used quickly.
Data Science und Machine Learning mit SAP Databricks

The BDC comes with a dedicated Databricks environment that can be seamlessly linked to the Datasphere in order to develop machine learning use cases based on SAP and IoT data, for example. The zero-copy approach and delta sharing allow data processing without the need to physically duplicate data. IT departments and data science teams can thus develop their own AI solutions that are customised to the challenge at hand.
Joule AI: Reliable data analyses

Joule, the Business Data Cloud's AI assistant, makes it much easier for business users to analyse data. Complex data queries can be carried out via a chat-based interface. Joule accesses the Knowledge Graph for this purpose, which provides the semantic correlations centrally. The development and continuous maintenance of these semantic models will be essential in the future to ensure the reliability and acceptance of AI chatbots and agents.