SAP’s internal IT organization—specifically its People Analytics team—acts as “customer zero,” serving as the first adopter of new SAP products before they are introduced to the broader market.
Operating within a complex landscape that spans all aspects of ERP, including HR with SAP SuccessFactors HCM, SAP’s People Analytics team faced growing demand for HR data and insights—from HR, as well as Sales, Finance, and Operations. What started as a challenge soon became an opportunity for a more governed, self-service approach.
Centralized dashboards hit their ceiling
For years, SAP’s People Analytics function operated from a centralized model focused on the consumption layer: building, maintaining, and optimizing dashboards tailored to specific business requirements. The MyTeam Dashboard—a 360-degree view of workforce data available to every people manager at SAP, covering upcoming birthdays, salary, and performance information—became the company’s single most used report. That success was a testament to how much the business valued easy access and visibility into key HR data.
But it also revealed the limits of the model. As demand grew, the analytics team found itself permanently on the defensive, saying “no” far more than “yes,” managing backlogs of individual KPI additions, and negotiating timelines for incremental changes. Furthermore, data management, maintenance, and governance proved to be a challenge. The centralized dashboard approach could not scale to meet the breadth of data needs across an organization of SAP’s size and complexity.
Drive better people and business decisions across hiring, retention, pay, and more.
The solution: using data products with People Intelligence in SAP Business Data Cloud
SAP IT made a strategic decision to shift the center of gravity in its analytics architecture, moving to govern and open up the data layer beneath the consumption layer. At the heart of this change is the data product, a managed asset that ingests data from systems, transforms it, and exposes it in a governed, reusable form so downstream analytics can rely on consistent, trusted building blocks.
Data products fall into two categories: primary data products, which are sourced directly from transactional systems, such as a job structure data product from SAP SuccessFactors HCM, and derived data products, which combine these primaries to answer broader questions. An example of this is a total employee and external workforce data product that fuses multiple sources into a single, harmonized view.
This is where People Intelligence in SAP Business Data Cloud became transformative for SAP’s People Analytics team. Rather than building all foundational HR data products from scratch, People Intelligence delivers a catalog of pre-built, SAP-tested data products and derived insights directly on top of SAP SuccessFactors HCM. Workforce composition insights alone include 69 data products, encoding hundreds of joins, tested and documented by SAP product teams, which is complexity that even AI-assisted modeling tools cannot yet reliably replicate without extensive testing and governance work.
SAP IT’s approach is deliberate: adopt SAP-delivered data products out of the box, build differentiating derived products on top, and free IT capacity for what actually differentiates and optimizes SAP’s HR processes.
Data sensitivity is also top of mind for the SAP team. With People Intelligence, the same data product is made available in multiple “flavors”—a full PII (personally identifiable information) view and a mini view with common company-visible information—helping to ensure the right data reaches the right consumer in the right format. This is especially useful with regards to Works Council’s sensitivity requirements of PII data. This data governance can help humans and agents work with the data in a compliant way.
“There is a meaningful difference between data you can trust and data sourced informally,” Oliver Huth, head of Platform, Corporate Functions, & Analytics at SAP, states. “Building that trust at scale is what the shift to data products—powered by People Intelligence in SAP Business Data Cloud—is making possible for our teams at SAP.”
What’s changed and what’s coming
The outcome for both IT and the business is clear. SAP IT populated its internal data product catalog rapidly, reaching the critical mass needed for broad adoption. HR data that was previously locked behind dashboard requests now powers use cases across functions. For SAP’s business teams, the outcome is faster time to insight. Pre-built intelligent content in People Intelligence serves as an 80% starting point for business conversations, replacing blank-sheet requirement gathering with focused discussions. Leaders and managers can also access personalized KPI views through MyMetrics, choosing a KPI, seeing an overview and AI summary, and jumping to the dashboard if additional information is needed. Users can also turn to Joule to ask questions in natural language and get replies with visualized charts. This pre-built, self-service approach has significantly reduced the volume of HR data inquiries and dashboard requests
SAP’s next steps for People Intelligence include recreating the MyTeam Dashboard by composing it from the readily available data products.
In addition, SAP IT is very excited to adopt AI agents that can operate directly on top of governed data products, querying a variety of data including employee, salary, and skills. As Huth notes, “Investing in a data product strategy is the essential first step. It is what enables governed data access and produces AI-ready models as a result.” SAP’s standard development teams are currently building Joule Assistants and Joule Agents, including a People Intelligence Assistant to be released in November 2026
Learning from SAP’s experience
For HR and people analytics teams facing growing data demand, fragmented access, and the pressure to deliver more with less, SAP IT’s experience offers a clear road map: adopt People Intelligence in SAP Business Data Cloud, use SAP-delivered data products out of the box, invest now in a governed data product architecture, and treat intelligent content as a starting point. This can improve analytics delivery today and is the infrastructure that will make AI agents trustworthy tomorrow.
Learn more about People Intelligence.
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