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Data Validation and Anomaly Detection

Data validation checks whether data follows declared rules, while anomaly detection finds unusual values or patterns relative to a baseline. Together they catch known defects and surface unexpected changes before unreliable data reaches reports, applications, or models.

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Don't Panic — Data Validation and Anomaly Detection

Data validation is the part of a data system that asks whether the data obeys the rules somebody meant to write down. An order identifier must be present. A customer identifier must resolve. A status must belong to the allowed set. This is not glamorous, but neither is discovering that a cheerful dashboard has been counting the same order twice. The dashboard will remain cheerful. That is how it gets you.

Anomaly detection asks a different question: does this batch look odd compared with its baseline, the recorded picture of normal behavior? A daily order count can fall sharply while every remaining row still passes its validation rules. The unusual count is a reason to investigate, not a court verdict. A holiday, release, source outage, or genuine business change can all make a number behave suspiciously without making it wrong.

The useful mental model has three pieces. Profile data to see row counts, null fractions, category frequencies, and similar evidence. Turn known requirements into rules. Then compare changing behavior with a baseline that has the right window and segment. A whole-table metric can conceal a failed partition, and a weekday baseline will become melodramatic about a normal weekend. Statistics, like office plants, require some knowledge of their surroundings.

The surprising bit is that a green result is not necessarily good news. Validation only catches expectations that were encoded. Anomaly detection only finds behavior that looks rare against its chosen history. A common but wrong value can pass both, while a legitimate new category can alarm the detector. That is why a result needs evidence, a response, and a record of the dataset, window, rule or model version, and threshold.

Read the Intro for the full control path and the limits of each method. Use the Slides when the vocabulary has multiplied like a spreadsheet with ideas above its station. Keep the Cheatsheet nearby for rule families, baselines, thresholds, and triage. The practice material turns the distinctions into failing-row queries and a partition gate. The quiz then checks whether the control you chose actually matches the failure you meant to catch.

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