A Catalog of Data Errors

TL;DR

This paper presents a comprehensive catalog of 35 data error types in tabular data, providing formal definitions and examples to aid detection and correction in data quality practices.

Contribution

It introduces a detailed taxonomy of data errors, including both errors and indicators, with formal definitions to improve error detection and data cleaning strategies.

Findings

Catalog includes 35 distinct error types and indicators.

Errors are classified into missing, incorrect, and redundant categories.

Provides formal definitions and practical examples for each error type.

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.