I have data that doesn't match my dimension tables, resulting in '(Blank)' values in my reports. Should I handle this in Power Query by adding a dummy row, or is there a better way to do this in DAX to keep the data clean?
Standardizing dimension tables by inserting an explicit unknown member row during the ETL process ensures referential integrity and optimizes query performance by preventing implicit join issues in reporting.
2 answers
Injecting a dummy row within Power Query is the technically superior approach for maintaining referential integrity across your data pipeline. By standardizing your dimension tables to include an explicit 'Unknown' member, you prevent the engine from performing costly implicit outer joins during report rendering.
Why are you treating missing data as a display issue rather than a data quality failure? When I was architecting a streaming engine for a major retail client, we dealt with mismatched keys by routing them through an enrichment layer rather than patching them at the report level.
If you leave the burden of normalization to the reporting tool, you lose the ability to audit the source of the breakage, which makes debugging nearly impossible when the dataset grows to millions of rows. I recommend capturing these orphans in a separate error log table instead of creating phantom records that skew your aggregate metrics.
