SAP Announces New AI Capabilities for SAP Datasphere, SAP Analytics Cloud, and an Expanded Partnership with Collibra

⇨ SAP has announced new data innovations within SAP Datasphere, and generative AI capabilities in SAP Analytics Cloud, aimed at transforming enterprise planning and analytics.

⇨ SAP also announced an expanded partnership with Collibra, a data intelligence company, to deliver end-to-end data governance (a complete data catalog with data lineage) across both SAP and non-SAP systems and allow organizations to build a privacy foundation.

⇨ SAP's latest additions offer significant productivity improvements which organizations can achieve by embracing a data fabric architecture. This approach simplifies understanding for business users through contextualization.

SAP has announced new data innovations within SAP Datasphere, and generative AI capabilities in SAP Analytics Cloud, aimed at transforming enterprise planning and analytics. These added AI capabilities will enable SAP customers to simplify their data landscapes and augment intuitive data interaction for intelligent business transformations. SAP also announced an expanded partnership with Collibra, a data intelligence company, to deliver end-to-end data governance (a complete data catalog with data lineage) across both SAP and non-SAP systems and allow organizations to build a privacy foundation.

SAP Datasphere’s open data ecosystem is the foundation for a business data fabric and enables organizations to deliver meaningful data to every data consumer — with business context and logic intact. The introduction of new capabilities AI copilot Joule  in SAP Analytics Cloud and HANA Cloud vector database capabilities will ensure business context remains constant in generative AI outputs while the introduction of SAP Datasphere knowledge graph will enable users to uncover hidden insights and patterns in complex data.

The key capabilities announced by SAP are:

Generative AI Copilot: SAP will integrate its AI Copilot, Joule, into SAP Analytics Cloud. This will streamline and automate the creation and development of assets including reports, dashboards, and plans. Powered by SAP HANA Cloud vector capabilities, available as part of the SAP HANA Q1 2024 release, this automation will combine large language models with organizational data and ensure that business context remains a constant element in all generative AI outputs. This will empower users to interact with data through conversational commands and includes tasks such as summarizing data and creating professional-looking dashboards or planning models using natural language input. The system’s understanding of the underlying data will facilitate seamless execution of these commands, resulting in significant time savings for users.

End-to-end AI Governance: SAP will also integrate Collibra’s Data and AI Governance platform with SAP data services like SAP Datasphere and HANA Cloud. This will enable organizations to connect data catalogs in SAP with Collibra accessing both SAP and non-SAP systems for an enterprise-wide data governance foundation, and allow organizations to maintain transparency and accountability, while also ensuring adherence to regulatory, compliance, and privacy policies.

Daniel Yu, senior vice president of solution management and product marketing for data and analytics at SAP, emphasizes, “Data governance holds paramount importance, serving as the pivotal point for companies to effectively transition into production use cases. It necessitates a comprehensive understanding of data quality, provenance, and trustworthiness. Questions surrounding the origins and protection of data, as well as concerns about privacy and potential leaks, underscore the criticality of data governance. Data governance and AI governance represent two sides of the same coin, inherently intertwined and indispensable for success. SAP’s partnership with Collibra has been expanded to encompass these aspects as well.”

SAP Datasphere knowledge graph: The new SAP Datasphere knowledge graph will enable organizations to uncover latent insights and patterns across applications and systems. This will allow technical and business users to better understand the relationships between data, metadata, and business processes, and enhance machine learning and efficiency of large language AI models (LLMs).

Yu underscores, “The knowledge graph not only facilitates more advanced analytics, but also enhances the contextual understanding for large language models. By providing a deeper insight into the relationships between data points, the knowledge graph elevates the quality of insights generated by these models. Ultimately, the knowledge graph serves a dual purpose by enabling sophisticated analytics and enriching the contextual understanding for language models. The symbiotic relationship between knowledge graphs and language models enriches the overall user experience, enhancing its magical essence through nuanced context.”

Unified data management system: SAP Datasphere’s integration with SAP Analytics Cloud will offer a single data management system and advanced analytics to enable cross-organizational planning. Users can leverage a single flexible model for data preparation, modeling, and planning. This will enable users to integrate financial, operational, supply chain, and workforce planning, leveraging native connections to SAP applications and third-party data sources. The bi-directional integration between SAP Datasphere and SAP Analytics Cloud will enable seamless communication between data models and analytical tools. This integration allows users to store models in SAP Datasphere and run analyses directly from it, streamlining the process of conducting simulations and planning at scale.

What Does This Mean for SAPinsiders?

SAP’s latest additions offer significant productivity improvements which organizations can achieve by embracing a data fabric architecture. This approach simplifies understanding for business users through contextualization. Yet, it also ventures into unexplored territories, posing questions previously unasked, now feasible due to a paradigm shift in mindset. Rather than solely expanding existing dimensions, users now delve into new realms, uncovering profound insights. This transformative analytical shift epitomizes true data-driven ethos—not just about acquiring answers, but about posing more incisive questions to reveal deeper truths.

The big benefit here is in the data fabric that SAP is talking about being underneath these capabilities. You can read more about the data fabric here, but the bottom line is that it is about connecting and managing data in real time across multiple systems and applications. This is something that SAP has already been working on, but it is also part of the deeper collaboration with the Collibra data governance platform. Also big is the unification of advanced planning and analytics with the integration of Datasphere and SAP Analytics Cloud. Having a single model across data sets and even data locations could be extremely helpful.

Incorporate data intelligence: Companies that merge SAP applications data with other operational sources like Salesforce, ServiceNow, and third-party data suppliers for insight generation must lay down a robust groundwork for their data fabric architecture.

Address complexity with integrated AI functionalities: Organizations must future-proof their enterprise data strategy by investing in a robust AI foundation that streamline tasks, facilitates faster decision-making, and delivers valuable insights. Combine machine learning and human expertise to enhance the understanding and relevance of data assets and automate tasks to ensure that governance practices remain current as your business progresses.

Establish adaptive data and analytics governance: Establish a data catalog and governance platform that automates governance activities and enables cross-functional teams to access all data at a single location to control the data environment, boost operational process efficiency, and mitigate risks related to data and compliance.