A Catalog of Data Errors
2026-04-10 • Databases
Databases
AI summaryⓘ
The authors explain that errors in databases happen a lot and can cause problems in things like machine learning or business reports. They noticed that some common errors are well-known, like missing values or duplicates, but others, like hidden missing data or swapped words, are less understood. To help with this, the authors created a clear list of 35 types of data errors, covering missing, wrong, or extra data, plus errors like bias and outliers. They also fixed confusing terms and gave examples, so people can better find and fix these errors in their data.
data errorsmissing valuesduplicate tuplesconstraint violationsoutliersbiasdata qualityerror taxonomydata cleaning
Authors
Divya Bhadauria, Hazar Harmouch, Felix Naumann, Divesh Srivastava, Lisa Ehrlinger
Abstract
Data errors are widespread in real-world databases and severely impact downstream applications, such as machine learning pipelines or business analytics reports. Causes of such errors are manifold and can arise during both the design phase and the operational phase of a database. Some error types, such as missing values, duplicate tuples, or constraint violations, are widely recognized; others, such as disguised missing values or word transpositions, remain underexplored. Existing attempts to define and classify errors in data offer valuable but limited taxonomies, mostly informal and not covering the full range of error types. With the rise of AI, practitioners must increasingly detect and correct statistical errors such as bias and outliers, which are rarely considered within existing error taxonomies. This catalog presents a comprehensive list of 35 distinct error types, including both data errors (e.g., missing values, duplicate tuples) and error indicators (e.g., outliers, bias) for tabular data, classified into three non-overlapping categories: missing, incorrect, and redundant. For each error type, we provide a formal definition and practical example, and resolve terminological inconsistencies across related work. Our catalog enables researchers and practitioners to address various error types and systematically implement error-specific detection and cleaning strategies in data quality tools.