DATA OPS
Weekly Dashboard Staleness Audit from BigQuery Usage Logs
Scans BigQuery dashboard view-event logs every week, scores each dashboard by days-since-last-view and unique viewers, and posts a ranked retirement candidate list to Slack.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires the audit
- ActionQuery 90-day dashboard view events in BigQueryBigQuery
- LogicScore decay: days-since-view + unique viewers
- LogicBranch retire-now vs watch-list
- OutputPost ranked candidates to SlackSlack
What it does
Reads dashboard impression events from your BigQuery analytics tables, computes a decay score per dashboard (days since last view, 30-day unique viewers, total opens), and surfaces the coldest dashboards as retirement candidates in a Slack channel your data team watches.
When to use it
Run this when your BI tool has accumulated hundreds of dashboards and nobody knows which ones are still load-bearing. Ideal for data teams doing quarterly hygiene who want a recurring, evidence-based shortlist instead of guessing.
How it works
- 1A weekly schedule fires the audit.
- 2A BigQuery query aggregates view events by dashboard_id over the trailing 90 days, returning last-viewed date and distinct viewer count.
- 3A scoring step flags any dashboard with zero views in 45+ days or fewer than 3 unique viewers as a retirement candidate.
- 4A branch separates "retire now" (90+ days cold) from "watch" (45-90 days).
- 5The flow formats a ranked table and posts it to Slack, tagging the data platform owner so a human makes the final call.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect SlackChannels, DMs, threads, mentions.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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