ERtype

ERtype is a standardized taxonomy and specification used to categorize and describe errors in data and information systems. It provides a controlled vocabulary for labeling error types encountered during data collection, transformation, and storage, enabling consistent reporting, analysis, and remediation workflows across organizations. The term is commonly applied in data governance, data quality assurance, and systems integration contexts. ERtype defines a multi-level taxonomy with core categories such as structural errors (format, encoding, schema mismatches), semantic errors (incorrect values, misinterpretation of meaning), procedural errors (ETL defects, timing issues), and missing or incomplete data. Within each category, subtypes can be defined and linked to attributes like data domain, source, and impact level.

History and development: The concept originated in industry working groups in the mid-2010s and gained traction with published specifications in the 2020s. The core aim has been to enable interoperable error reporting across heterogeneous data landscapes and to support automated remediation rules and dashboards. Variants of ERtype have been adopted in different sectors, sometimes integrated with broader data quality standards or metadata registries.

Applications: ERtype is used to annotate data quality incidents, drive error detection rules in ETL pipelines, support data lineage tracing, and facilitate benchmarking of data quality across systems. It supports mapping to organizational data quality objectives and can be embedded in governance dashboards and remediation workflows.

Adoption and standards: There is no single universal standard for ERtype, and implementations vary by organization. In practice, ERtype harmonizes with existing taxonomies and metadata frameworks to improve interoperability and traceability in data ecosystems.

See also: Data quality, data governance, metadata, data lineage, error taxonomy.