Check Data Quality at the Source
Evaluate whether data is fit for use by checking completeness, accuracy, consistency, timeliness, uniqueness, and validity.
A tidy table can still be the wrong table. Completeness Are the fields and rows needed for the decision present? Missingness is most dangerous when it is systematic, such as one channel, region, or time period. Accuracy and validity Do values represent reality, and do they follow allowed formats or ranges? A valid email format is not the same as an accurate customer record. Consistency and uniqueness Do systems use the same definitions, and is each real-world thing counted once? Inconsistent labels and duplicate IDs are common sources of false rankings. Timeliness Is the data recent enough for the decision?…
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