Transformation Rules

Transformation rules are rules that change raw incoming data before storage or display: cleaning, standardizing, enriching, or mapping one value to another.

Transformation rules are where raw data becomes reportable data, and that makes them both powerful and risky: any rule that improves the data can, if wrong, quietly corrupt it. Common transforms: normalizing case, mapping a raw source to a channel, removing or masking a sensitive part, or adding a derived field. The key distinction is that a transform affects the meaning of the data, not just its shape, so it must be documented and transparent; a transformation rule no one knows about is the source of unexplained discrepancies between two reports. The healthy rule is that transforms be few, documented and reviewable, and wherever possible the raw data be kept alongside the transformed data so that if a rule was wrong, you can go back.