Looker Studio Blended Data Not Matching: How to Find Join and Aggregation Problems
When Looker Studio blended data does not match, the blend returns wrong totals, duplicate rows or missing rows. The cause is almost always the join keys, the granularity, the aggregation or null values in the join dimension. Blending is not a simple merge, so oversimplifying it leads to wrong results.
Table of Contents
- 01.Why blending produces different numbers
- 02.What to check first
- 03.Join keys and duplicate rows
- 04.Granularity and aggregation
- 05.Null values in the join dimension
- 06.Date alignment
- 07.How to test the fix
- 08.How to validate the final result
Why blending produces different numbers
Blending joins multiple data sources on shared dimensions. The result depends on the join keys, the granularity of each source, how metrics aggregate after the join and how null values in the join dimension are handled. A blend that looks correct can return inflated, deflated or missing totals because of any of those factors.
| Cause | What happens | How to check |
|---|---|---|
| Wrong join key | Rows match incorrectly | Confirm the join dimension is unique per row |
| Granularity mismatch | Sources at different detail levels | Match the granularity across sources |
| Duplicate joins | Rows multiply | Test each source separately |
| Null join values | Rows drop out of the blend | Check for nulls in the join dimension |
| Date alignment | Different date ranges or time zones | Align date dimensions across sources |
What to check first
- Test each source in a separate chart and confirm each total is correct.
- Confirm the join key is the same dimension across all sources.
- Check for null or empty values in the join dimension.
- Confirm the granularity of each source matches before blending.
- Align the date dimension and range across all joined sources.
Join keys and duplicate rows
The join key determines how rows match across sources. If the join key is not unique per row in one source, the blend duplicates rows in the other source. A cost source joined to a conversions source on campaign name can duplicate conversions when one campaign has multiple cost rows. Confirm the join key is unique per row in each source, or accept that the blend will multiply rows and aggregate accordingly.
Granularity and aggregation
Each source in a blend has its own granularity. If one source is daily and another is monthly, the join produces inconsistent rows. Match the granularity across sources before blending. After the join, metrics aggregate based on the chart configuration. A sum of a blended metric can differ from the source total if the join changed the row count. Confirm the aggregation type after the blend matches the intent.
Null values in the join dimension
Rows with null or empty values in the join dimension drop out of the blend, because they cannot match anything. A blend that returns fewer rows than expected often has nulls in the join dimension. Check each source for nulls in the join field and either filter them out or fill them before blending.
Date alignment
When sources use different date dimensions or different time zones, the blend aligns rows incorrectly. A cost source in one time zone joined to a conversions source in another can shift rows between days. Align the date dimension and the time zone across all sources before blending. Date range behavior is covered in the guide on Looker Studio date range not working.
How to test the fix
- 1.Confirm each source total is correct in a separate chart.
- 2.Confirm the join key is unique per row in each source.
- 3.Check for and handle null values in the join dimension.
- 4.Confirm the blended total matches the sum of the source totals where it should.
How to validate the final result
The blend is correct when each source total is correct on its own, the blended total matches the expected aggregation and no rows are dropped or duplicated due to nulls or join keys. If the blended total still differs after confirming each source, the join or granularity is the cause, not the source data.
Frequently Asked Questions
Why does my blended data total not match the source totals?
The join is duplicating or dropping rows. Confirm the join key is unique per row in each source, check for nulls in the join dimension and match the granularity across sources. Test each source separately first to confirm each total is correct before the blend.
Why does my blend return fewer rows than expected?
Rows with null or empty values in the join dimension drop out of the blend because they cannot match. Check each source for nulls in the join field and either filter them out or fill them before blending.
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