data smells

In detail, we define data smellsas context-independent, data value-based indications of latent dataquality issues caused by poor practices that may lead to problems inthe future.

Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based SystemsData Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems 2023

Similarly, everyone who reports on data can name a few of their favorite “data smells”—e.g., Benford’s Law, large standard deviations, double-counted or omitted records, category fields that are manually entered—but there is no central repository for this information.

Distrust Your Data Jacob Harris 2025

We referred to thesepotential data issues as data smells by analogy with code smells.

Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based SystemsData Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems 2023