Tableau Extract Refresh and Data Freshness Audit
Check schedules, failures and stale dashboards.
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Audit extract refreshes. Schedules, history for the last 30 days (success, failure, duration) and error messages: [REFRESH_HISTORY]. Dashboards that depend on them and their freshness needs: [DEPENDENCIES]. Start by listing the refresh jobs and the dashboards each feeds so I can confirm the mapping. Calculate the success rate (successful divided by attempts), average and longest duration, and the total hours dashboards showed old data after each failure; state the sample size and avoid assuming causes. Group failures by cause (credentials, source unavailable, timeout, schema change) and check that the freshness label on dashboards is accurate. Propose changes: schedule times away from peak, alerts reaching a named person, incremental refresh where suitable [VERIFY], a standard message to dashboard users when data is late, and a rule for manual refresh. Return a table, a first-response runbook and a freshness label design. Refresh options depend on my deployment and licence [VERIFY]. Keep the runbook to six steps a colleague can follow at night, and list who to tell when a refresh fails. Mark any cause you are guessing at, so it is checked before anyone changes a schedule.
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