Trace a single column from a source table — through every dataflow, every semantic model, every report — down to the individual visual that renders it. End-to-end. No gaps.
Microsoft Purview maps assets but not visuals. Power BI's own lineage view shows datasets but not the columns inside them. Data-catalog tools see the warehouse but lose the trail at the dataflow boundary.
Measure Killer already tracks column-level lineage inside a semantic model, and end-to-end report-to-source at the table level. The piece coming next closes the last gap — column-level, all the way through, ending at the visual.
Tables and columns in the sources we trace — launch and planned:
All column changes tracked through Gen1 and Gen2 dataflows — every Power Query M transformation followed end to end.
Every place a column or measure can be referenced inside the model:
Every report-level usage point — outside of the individual visuals:
The visual layer itself — fields bound to visuals, filters scoped to a single visual, and inline visual calculations.
DBA wants to rename or drop a source column. Today: pray. With source-to-visual lineage: get a precise list of every visual that will break, who owns each report, and which dataflows or measures need to change first.
An executive asks "why is this number wrong?". Click the visual, walk the lineage backwards through the report, semantic model and dataflow to the exact source column. No more 30-minute spelunking sessions.
Your data warehouse is paying to compute columns nobody reads. Identify source columns that don't reach any visual — anywhere in the tenant — and decommission them safely.
Measure Killer already removes unused columns and measures from semantic models. With end-to-end lineage we extend that one step further upstream: trim unused columns and entire unreferenced queries out of your Gen1 and Gen2 dataflows — fewer refresh minutes, smaller bills, simpler models.
Strip out unused columns, tables and calculation steps from your semantic models in minutes, with the confidence that nothing downstream will break. Smaller models refresh faster, consume fewer Fabric capacity units (CUs), and present a clean, well-named schema that Copilot for Fabric and other LLMs can actually reason about.
Prove which reports and visuals consume PII columns. Audit-grade answers in seconds instead of weeks.
The first wave will cover SQL Server, Azure SQL Database, PostgreSQL, Snowflake, Fabric Warehouse, Fabric SQL and Fabric Lakehouse as sources. Databricks, Google BigQuery, Amazon Redshift, Oracle and SAP HANA are on the roadmap for subsequent releases.
If you want a preview build, hands-on guidance, or to put your specific source on the priority list — reach out and we'll get you into the early-access cohort.
Measure Killer