This workflow automatically audits shared Omni dashboards each Monday, identifying stale content by tracking view counts over time in a persistent volume and inferring recency from usage patterns. It posts a read-only digest to Slack listing candidates for retirement, helping data teams keep their Omni Hub clean without manual review. The workflow excludes personal folders and legacy content, and becomes more accurate as historical data accumulates.

Data teams struggle to keep shared Omni dashboards clean because manual auditing is time-consuming and never prioritized, leading to clutter that confuses users and dilutes trust in the analytics catalog. This workflow automates the audit by scanning the organization's Omni Hub every Monday, identifying stale dashboards using low lifetime views and no recent activity over a trailing 90-day window, and posting a review-ready digest to Slack. Because Omni's API lacks a "last viewed" timestamp, the workflow builds its own recency signal by snapshotting view counts into persistent storage on each run, becoming more accurate over time. It's fully read-only and excludes personal folders and legacy content, so the data team receives a scannable shortlist of retirement candidates without risk of accidental deletion.

The workflow eliminates manual dashboard auditing by delivering a weekly Slack digest that lists every stale Omni document with its owner, view count, last-edited date, and a direct link, so the data team can review and retire candidates in minutes instead of hours. Keeping the Hub clean means downstream users find the right dashboard faster, reducing confusion, duplicate work, and support requests. It's zero-risk and FYI-only—humans decide what to retire—and accuracy improves automatically as view-count history accumulates, with no operator intervention required.