Looker Studio Dashboard Audit Checklist: How to Validate Every Important Metric
A Looker Studio dashboard audit checks every important metric from the data source to the final chart. The goal is to confirm each number is correct for its configuration, not to force every total to match a source exactly. Use this checklist to validate data sources, authentication, date ranges, dimensions, metrics, filters, calculations, blended data and the final dashboard totals.
Table of Contents
- 01.How to use this audit
- 02.Step 1: Confirm the data sources
- 03.Step 2: Confirm authentication and access
- 04.Step 3: Check the date range
- 05.Step 4: Validate dimensions and metrics
- 06.Step 5: Check filters
- 07.Step 6: Check calculations
- 08.Step 7: Check blended data
- 09.Step 8: Compare source platform totals
- 10.Step 9: Validate the final dashboard
- 11.When a difference is expected
How to use this audit
Work through the checklist in order. Each step confirms one layer of the dashboard. If a step fails, fix it before moving on, because a broken layer invalidates every layer after it. The audit ends with a comparison against the source platform totals, which is the final validation.
Step 1: Confirm the data sources
- List every data source the dashboard uses.
- Confirm each source connects without an error.
- Confirm each source is linked to the correct account or resource.
- Check the data freshness setting on each source.
Step 2: Confirm authentication and access
- Confirm each connector is authenticated with the correct account.
- Confirm viewers have access to the report and the connected resources.
- Reconnect any source that shows an authentication prompt.
Step 3: Check the date range
- Confirm the date range control is linked to the correct date dimension.
- Confirm every chart uses the control, not a fixed range.
- Confirm the date field is a proper date type.
- Confirm the time zone matches the source expectation.
Step 4: Validate dimensions and metrics
- Confirm every dimension and metric still exists in the source schema.
- Confirm the metric aggregation type matches the intent.
- Confirm no field shows a warning icon for a removed or renamed field.
Step 5: Check filters
- List every report, page and chart filter.
- Confirm the include or exclude logic matches the intent.
- Review the AND and OR logic between conditions.
- Remove filters and confirm the unfiltered total is correct.
Step 6: Check calculations
- Confirm every calculated field references existing fields.
- Confirm field types are compatible with the operations.
- Confirm null values are handled.
- Confirm the calculated field aggregation matches the chart.
Step 7: Check blended data
- Test each source in a separate chart and confirm each total is correct.
- Confirm the join key is unique per row in each source.
- Check for null values in the join dimension.
- Confirm the blended total matches the expected aggregation.
Step 8: Compare source platform totals
The final validation is to compare the dashboard totals with the source platforms. For each important metric, open the source platform and confirm the same total for the same date range, filter set and definition. Conversions need their definition and attribution confirmed first. Costs need the account scope confirmed. CRM metrics need the date field and stage mapping confirmed. A explained difference is acceptable, an unexplained large difference is not.
| Metric type | What to confirm in the source | Common mismatch cause |
|---|---|---|
| Conversions | Conversion definition and attribution | Different definition or window |
| Cost | Account scope and date range | Manager account vs single account |
| Sessions | Time zone and date range | Property time zone difference |
| CRM leads | Date field and filter set | Different date field used |
| Pipeline value | Stage mapping and date field | Different stage set |
Step 9: Validate the final dashboard
- Confirm every chart renders without an error.
- Confirm the dashboard loads in an acceptable time.
- Confirm the dashboard is readable on mobile.
- Have a second user open the dashboard to confirm access and rendering.
When a difference is expected
Not every difference is an error. Sampling, processing lag, attribution windows and connector freshness can all produce legitimate small differences. The audit distinguishes a explained difference from a configuration error. A large unexplained difference always points to a configuration problem in one of the earlier steps.
Frequently Asked Questions
How do I audit a Looker Studio dashboard?
Work through the layers in order: data sources, authentication, date range, dimensions, metrics, filters, calculations, blended data, then compare the final totals with the source platforms. Fix each layer before moving on, because a broken layer invalidates the layers after it.
Should every dashboard total match the source platform exactly?
Not always. Sampling, processing lag, attribution windows and connector freshness can produce legitimate small differences. The goal is to explain each difference. A large unexplained difference points to a configuration problem in an earlier audit step.
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