Data Dictionary Completeness Audit
Check that every field is defined, typed and owned.
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Audit a data dictionary. The dictionary table with field name, definition, type, allowed values, source, owner and last reviewed date: [DATA_DICTIONARY]. A sample of the real data columns: [REAL_COLUMNS].
Compare the dictionary with the real columns and list fields missing from either, fields with no definition, vague definitions ('the customer thing'), missing units, inconsistent types, undocumented codes, and fields with no owner. Count completeness by attribute with percentages and the arithmetic. Make a table with Field, Issue, Risk, and Fix. Rewrite five weak definitions to state what the value means, how it is created, units, allowed values and nulls. Propose a review cycle and a change log.
Use only the information I provide.
Add a worked completeness table for 40 fields: definition present 36 (90 percent), units stated 22 (55), allowed values 19 (47.5), owner named 14 (35), last reviewed in the last year 9 (22.5), with the lowest attributes first. Include a rewrite example: 'status: the status' becomes 'status: the order state, one of open, packed, shipped, cancelled; set by the warehouse system when the label is printed; never null'.
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