Practical enablement: catalog, lineage, metrics & self‑service path

Exec summary:  

Analytics self-service doesn’t appear because you buy a tool. It appears when people own definitions and there’s a simple way to find and trust data. 

Here’s what worked for an e-commerce team: 

• Emphasis on Metric products + semantic-layer MVP → one team, a handful of KPIs, clear success criteria. 

• Named leads → KPI governance and architecture/tooling. 

• Targeted upskilling → catalog/lineage where it matters, cost practices on the platform. 

Outcome: more discoverability, clearer KPIs, and a practical path to Gen BI implementation choices with quick enablement — without breaking what already works. 

Audience takeaway: We make analytics self‑service real — grounded in metric governance, access control, and a semantic layer your analysts can run. 

 Challenges (before we started): 

• Metric inconsistency and limited discoverability; homemade catalog. 

• Cross‑domain lineage missing; access management unclear. 

• Fragmented BI tooling and no semantic‑layer know‑how. 

• Steep learning curve and cost management on the platform. 

 What we did (advisory, training & coaching)

• Launched a Value & AI enablement session and set a 4‑month plan for a semantic layer (target user group, priority metrics, success criteria) - clarify at first.

• Ensured there are named leads: KPI governance and architecture/tooling got dedicated leads, with clear scoping questions and POCs. 

• Brought in external sparring for Databricks practices (e.g., cost management) and made a deep-dive workshop on metric products, analytics tooling, KPI governance and semantic layer including specific recommendations for GenBI choices. 

 Outcomes so far: 

• The team reports higher visibility and trust in the data function; leadership appetite for DS/AI is rising. Team empowers itself with ownership and drive-to-results.

• A pragmatic self‑service path: who owns metrics, how the semantic layer will work, and which tools to test - all work in progress, with regular check points.

 What made the difference: 

• We kept it practical and people‑centered: tangible ownership, targeted upskilling — so architecture and BI choices translates into real-life adoption.