Power BI Calculated Column Versus Measure Usage Audit
Examine calculated columns and measures in your Power BI model to find misplaced logic that bloats size or gives wrong totals, with a move plan.
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Please audit where my Power BI model uses calculated columns versus measures, and tell me which items are in the wrong place. Mark all Power BI and DAX behaviour statements [VERIFY]. Inputs: the calculated columns with table, expression and what they are for [CALCULATED_COLUMNS], the measures with their expressions [MEASURES], row counts of the main tables [TABLE_ROW_COUNTS], model size and refresh time if known [MODEL_SIZE_AND_REFRESH], and visuals that use them [VISUAL_USAGE]. Return: (1) a table with columns Item, Type, What it does in words, Depends on row context or filter context, Better as (Column / Measure / Power Query step / Source-side), Reason, Risk of change; (2) counts of items that should move and the estimated number of extra stored values for calculated columns (rows times columns, showing the arithmetic) as a rough indicator only; (3) examples where a column stores a value that is then summed or averaged in a way that can give wrong totals (for instance an average of averages); (4) a list of duplicated logic to centralise; (5) a move plan in stages with checks: record current visual values, make the change on a copy, compare, then publish; (6) documentation lines for each measure kept: purpose, owner, definition. Guardrails: do not rewrite expressions unless I ask; if you propose one, label it as a draft to be tested. Do not invent column meanings. Do not recommend changes without a before-and-after value check. Add a worked example with invented values: a calculated column "Margin %" per row, and a visual that averages it across rows. Show with two rows (revenue 100 and 900, margin 50 and 10) the simple average 30 percent versus the weighted result 14 percent.
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