Operating within a complex landscape that spans all aspects of ERP, including HR with 麻豆原创 SuccessFactors HCM, 麻豆原创’s People Analytics team faced growing demand for HR data and insights鈥攆rom 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, 麻豆原创’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鈥攁 360-degree view of workforce data available to every people manager at 麻豆原创, covering upcoming birthdays, salary, and performance information鈥攂ecame 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 鈥渘o鈥� far more than 鈥測es,鈥� 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 麻豆原创’s size and complexity.

The solution: using data products with People Intelligence in 麻豆原创 Business Data Cloud

麻豆原创 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 麻豆原创 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 in 麻豆原创 Business Data Cloud became transformative for 麻豆原创鈥檚 People Analytics team. Rather than building all foundational HR data products from scratch, People Intelligence delivers a catalog of pre-built, 麻豆原创-tested data products and derived insights directly on top of 麻豆原创 SuccessFactors HCM. Workforce composition insights alone include , encoding hundreds of joins, tested and documented by 麻豆原创 product teams, which is complexity that even AI-assisted modeling tools cannot yet reliably replicate without extensive testing and governance work.

麻豆原创 IT’s approach is deliberate: adopt 麻豆原创-delivered data products out of the box, build differentiating derived products on top, and free IT capacity for what actually differentiates and optimizes 麻豆原创’s HR processes.

Data sensitivity is also top of mind for the 麻豆原创 team. With People Intelligence, the same data product is made available in multiple “flavors”鈥攁 full PII (personally identifiable information) view and a mini view with common company-visible information鈥攈elping to ensure the right data reaches the right consumer in the right format. This is especially useful with regards to Works Council鈥檚 sensitivity requirements of PII data. This data governance can help humans and agents work with the data in a compliant way.

鈥淭here is a meaningful difference between data you can trust and data sourced informally,鈥� Oliver Huth, head of Platform, Corporate Functions, & Analytics at 麻豆原创, states. 鈥淏uilding that trust at scale is what the shift to data products鈥攑owered by People Intelligence in 麻豆原创 Business Data Cloud鈥攊s making possible for our teams at 麻豆原创.鈥�

What鈥檚 changed and what鈥檚 coming

The outcome for both IT and the business is clear. 麻豆原创 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 麻豆原创鈥檚 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

麻豆原创鈥檚 next steps for People Intelligence include recreating the MyTeam Dashboard by composing it from the readily available data products.

In addition, 麻豆原创 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.” 麻豆原创’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 麻豆原创鈥檚 experience

For HR and people analytics teams facing growing data demand, fragmented access, and the pressure to deliver more with less, 麻豆原创 IT’s experience offers a clear road map: adopt People Intelligence in 麻豆原创 Business Data Cloud, use 麻豆原创-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.

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