Which BI tool should we pick for self‑service?

Pick the one that best fits your metrics layer and security model. Superset and Metabase are great for open tooling and GitOps; Looker is strong if you’ll commit to its semantic model. The tool is secondary to contracts, tests, and a metrics layer. Thin BI on top of governed models beats feature‑rich BI on raw data every time.

dbt tests or Great Expectations?

Both. Use dbt tests for structural guarantees (nulls, uniques, referential integrity). Use Great Expectations for richer distribution and field‑level checks. Run both in CI and fail the pipeline on violations.

How do we measure if self‑service is delivering business value?

Track adoption (MAU on golden dashboards), time‑to‑answer for key questions, MTTR for incidents, and SLO compliance on tier‑1 datasets. Tie dashboards to OKRs—if a dashboard doesn’t support a decision, it’s a candidate for deprecation.

We have a lot of LLM‑generated SQL. Safe to use?

Treat it as scaffolding. Run a vibe code cleanup: refactor into dbt models, add tests, and codify metrics. We routinely see 20–40% performance and reliability gains by replacing AI‑generated ad‑hoc SQL with modeled transformations.

Do we need data catalog and lineage from day one?

Turn on lineage early (OpenLineage + Marquez/DataHub). A catalog (DataHub/OpenMetadata) becomes important as you scale past ~50 models or multiple teams. Lineage is crucial for cutting MTTR during incidents and for safe deprecations.

Data-engineering · Nov 24, 2025 · 10 minute read

Self‑Service Analytics Without the Monday Morning Pager: Building a Data Viz Platform That Actually Holds Up

Dashboards people trust, without a hero culture. The blueprint we use to ship self‑service analytics that don’t burn the team.

Back to all posts

Self‑Service Analytics Without the Monday Morning Pager: Building a Data Viz Platform That Actually Holds Up

Key takeaways

Implementation checklist