Self-Service Analytics

Self-service was the goal. Nobody owned the metrics.

How an e-commerce team built a semantic layer MVP, named governance leads, and created a practical path to self-service analytics — without breaking what already works.

The Situation

The team was modernizing on Databricks while managing multiple BI tools and growing AI ambitions. Metric inconsistency, limited discoverability, missing cross-domain lineage, and no semantic layer know-how meant self-service remained theoretical despite real investment.

Our Approach

01

Reset the engagement rhythm: 30-minute monthly check-ins for progress; 1-hour advisory sessions for deep problem-solving

02

Launched a Value & AI enablement session and set a 4-month MVP plan for a semantic layer with a target user group, priority metrics, and success criteria

03

Named dedicated leads for KPI governance and architecture/tooling with clear scoping questions and proof-of-concepts

04

Brought in external sparring on Databricks cost practices and ran a focused session on metric products, KPI governance, and GenBI options

4-Mo

Semantic Layer MVP Plan

2

Named Governance Leads

↑

Data Team Visibility & Trust

The Results

Higher visibility and trust in the data function; leadership appetite for DS/AI actively rising

A pragmatic self-service runway: defined metric ownership, semantic layer plan, and tools shortlisted for testing

BI consolidation options and access-management approach on the table with clear next sessions scheduled

What Made the Difference

"We kept it practical and people-centred: lighter rhythms, tangible ownership, and targeted upskilling — so architecture and BI choices translated into adoption rather than shelf-ware."

Why Data Masterclass