From Business Question to Governed Workforce Data
What repeated HR data rebuilds teach us about infrastructure, shared metrics and AI readiness
PAWorld26 San Francisco · 23–24 September 2026 · San Francisco
About this session
Workforce data platforms often solve the immediate reporting need, then become harder to reuse as questions, metrics and users multiply. Drawing on repeated rebuild experience and one Adobe application, this session shows how better requirements, shared metric meaning and reusable engineering choices can reduce reconciliation, improve consistency and prepare workforce data for self-service and AI.
Learning outcomes
- Recognise infrastructure choices that create future scaling problems.
- Explain the role of a semantic layer in shared metric meaning and reusable BI.
- Clarify ownership between data engineering, BI and business stakeholders.
- Ask better questions before calling a workforce-data environment AI-ready.
Strategic questions
- How do People Analytics teams turn a working data platform into governed infrastructure and a semantic layer that can support consistent BI, changing business questions and AI?
- Where should agreed metric meaning live and who owns changes?
- What should be reusable in the data/semantic layer rather than rebuilt in individual reports?
- What is genuinely live today versus future enablement?
Context
As workforce questions change faster and AI increases demand for reusable data, organisations cannot afford every answer to depend on bespoke logic and reconciliation. Shared metric meaning, clear ownership and scalable engineering are becoming prerequisites for trusted workforce decisions.
Speakers and contributors
Justin WardPresenterSenior Manager, People Data Engineering & GovernanceAdobe