Case study · Logistics & Mobility
Self-serve analytics at Porter: a Metric Store and Semantic Layer
How a Metric Store and Semantic Reporting Layer cut SQL-to-insight from 4 days to 2 hours and reduced ad-hoc report requests by 90%.
- SQL-to-insight time
- 4d → 2h
- Dashboard build time
- 7d → 2d
- Ad-hoc report requests
- −90%
What
Analytics at Porter had grown request by request. Each team kept its own queries and dashboards, so the same metric could mean different things in different meetings. I led the move to self-serve analytics, built on a Metric Store and a Semantic Reporting Layer.
Why
- Slow answers. Taking a question from SQL to insight took around 4 days, and a new dashboard took about 7.
- Analysts stuck on report requests. A large share of analyst time went to ad-hoc pulls instead of decision support.
- No single version of the truth. Metric definitions drifted across business lines, which undermined trust in the numbers.
How
- Central Data Product. A unified warehouse and governance layer spanning all business lines.
- Metric Store and Semantic Reporting Layer. Each metric is defined once and reused everywhere, so dashboards, analysts and business users read the same governed definitions.
- Governance built in. Data catalogue, privacy and security standards, access controls, PII masking and audit trails.
- Operating model. The Analytics Engineer role owns analytical data products, and the Compass framework sets the bar for analytical rigor.
Who
Senior Analytics Manager, leading a 14-person analytics team and partnering with Data Engineering.
When
2023 to present, at Porter.
Result
SQL-to-insight time fell from 4 days to 2 hours. Dashboard build time fell from 7 days to 2, and ad-hoc report requests dropped by 90%.
Next: AI-native analytics with MCP builds on the same governed metrics.