What does Müller’s Departure Mean for Datasphere Adoption

⇨ Jürgen Müller, SAP's Chief Technology Officer, will depart from the SAP Executive Board effective September 30th, 2024, due to inappropriate behavior, leading to questions about the future direction of SAP's innovation initiatives like Datasphere.

⇨ Companies considering a transition to SAP Datasphere need to establish a clear data integration strategy, implement strong data governance practices, and plan for performance requirements to ensure smooth and efficient operations.

⇨ A phased approach to migration is advised, beginning with non-critical systems, accompanied by a comprehensive data migration plan to maintain data integrity and minimise risks during the transition.

In an unexpected development last week, SAP announced that Chief Technology Officer (CTO) Jürgen Müller will depart the SAP Executive Board effective September 30th, 2024. According to the press release, Müller is departing due to inappropriate behavior at a company event. In a quote, Müller said that his actions “did not reflect our values at SAP” and that he would “take full responsibility and believes that stepping down is the best for the company”.

Müller joined the SAP Executive Board in 2019 as CTO and in that role has led SAP’s platform and development unit. Müller has been instrumental in the expansion and success of SAP Integration Suite, the transformation of SAP Cloud Platform into SAP Business Technology Platform (BTP), and SAP’s push towards data federation with SAP Datasphere.

With Müller’s departure, questions arise regarding who will drive the innovation agenda behind Datasphere and what, if any, impacts will be felt by customers. It will likely take months to replace Müller as CTO, during which time Christian Klein will undoubtedly keep the ship on course.

So, for SAP customers considering the move to Datasphere, nothing materially changes in the immediate term. Companies still need to carefully consider several factors to ensure a smooth transition and maximise the value of the platform.

First, companies need to have a clear data integration strategy. They need to ensure that SAP Datasphere can integrate seamlessly with existing on-premise and cloud systems, including ERP, CRM, and other data sources. A robust integration strategy is essential for real-time data access and consistency. Also, companies should assess the compatibility of their current data sources with SAP Datasphere, including structured and unstructured data, and plan for any necessary data transformations.

Second, companies need to implement strong data governance practices to ensure data quality, accuracy, and consistency across the organisation. This includes setting up data validation rules, cleansing processes, and master data management. In addition, SAP customers must ensure that their data management processes within SAP Datasphere comply with industry regulations (e.g., GDPR, CCPA) and internal security policies and implement necessary controls to protect sensitive data and maintain data sovereignty.

Third, companies should plan for the performance requirements of their data workloads, including high-volume transactions, real-time analytics, and complex queries. Steps should be taken to optimise the infrastructure to handle peak loads and ensure fast data processing. Companies would be ill-advised to overlook scalability. They should consider the future scalability of their data environment, including the ability to handle growing data volumes, additional data sources, and increased user demand.

Fourth, SAP customers need to develop a unified data model that aligns with their business processes and analytics needs. A good option is to leverage SAP Datasphere’s modeling capabilities to create a coherent data architecture that supports both operational and analytical use cases. Further, companies should define the layers of data storage and processing within SAP Datasphere, including staging, processing, and consumption layers, ensuring that the architecture supports efficient data flows and minimises redundancy.

And fifth, companies should consider a phased approach to migration, starting with non-critical systems or data sets. This allows you to test the platform, address any issues, and minimise risks before moving critical operations. Still, develop a comprehensive data migration plan, including data extraction, transformation, loading (ETL), and validation processes. Ensure that data integrity is maintained throughout the migration.