Data validation and cleansing is an increasingly popular methodical discipline. But how do you know where the best place to start is?

Start with a set of rules (or tests) to identify anomalies – for example, if a data item hasn’t changed (and is possibly stale), if it has changed beyond typical norms (and is possibly an error) or if two sources differ in their value for the same item (suggesting one may be wrong).

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