How do we prevent dashboard sprawl and conflicting metrics?

Centralize metrics in a semantic layer (LookML/dbt SL/MetricFlow), enforce code owners on metric definitions, and require PRs for metric changes. Archive or deprecate redundant dashboards quarterly, and publish a single catalog entry per canonical KPI with clear owners and SLOs.

Live queries or extracts?

Prefer live queries with proper partitioning/clustering and query caching for consistency and freshness. Use extracts for gnarly joins or flaky sources—but wire them behind data quality checks and explicit refresh windows to avoid publishing bad data.

What are the minimum guardrails to call it ‘self-service’?

(1) Productized, documented gold datasets; (2) SLOs (freshness, completeness, accuracy) with tests; (3) semantic layer for shared metrics; (4) RLS/CLS on the warehouse; (5) versioned BI assets with CI/CD and previews.

Where does AI fit into this?

Use AI to scaffold queries or visuals against your semantic layer, not raw tables. Block AI-generated code from production paths unless it passes tests and code review. If you’ve got AI/vibe-coded models already, do a targeted refactor and add tests before expanding self-service.

When should we not do self-service?

If your source systems are unstable and you can’t meet basic freshness/accuracy SLOs, prioritize data reliability first. Also, for highly regulated data with nuanced access rules, keep exploration limited to curated, privacy-safe datasets.

Data-engineering · Dec 7, 2025 · 9 minute read

Stop Turning BI Into a Ticket Queue: Building Self‑Service Analytics That Don’t Break at 2 AM

How to stand up a reliable, governed self-service analytics platform that ships business value—without waking ops every time a dashboard refreshes.

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Stop Turning BI Into a Ticket Queue: Building Self‑Service Analytics That Don’t Break at 2 AM

Key takeaways

Implementation checklist