datahygiene

Data hygiene is the set of practices and processes used to maintain data quality and reliability across an organization. It focuses on detecting and correcting defects in data such as errors, duplications, missing values, inconsistencies, and outdated records, enabling more accurate analysis and operations. Data hygiene sits within data quality and data governance efforts, and it is distinct from policy-level governance though it is enabled by governance structures.

Key practices include data profiling to understand quality issues; validation to enforce business rules; cleansing to fix incorrect values; deduplication to remove duplicate records; normalization and standardization to ensure uniform formats; enrichment to supplement data with reliable external sources; and ongoing monitoring and metadata management.

Data hygiene is applied across data lifecycles, including capture, storage, processing, distribution, and retirement. It relies on automated validation rules, referential integrity constraints, and data lineage to trace sources and impact of changes, as well as role-based stewardship to assign accountability.

Common metrics include completeness, accuracy, timeliness, consistency, validity, and uniqueness. Data hygiene benefits include improved decision making, operational efficiency, regulatory compliance, better customer experiences, and lower storage costs. Challenges include data silos, inconsistent definitions, incomplete data, privacy and compliance concerns, and resource constraints.

Related concepts include data quality, data governance, and data management frameworks such as DAMA-DMBOK or ISO 8000.