Dataquality

Data quality refers to the degree to which data meet the requirements of a given use. It describes the fitness of data for its intended purpose and is assessed in the context of how or where the data will be used. High-quality data support reliable decision making, effective analytics, regulatory compliance, and smooth operations, while poor data quality can lead to errors, inefficiency, and increased risk.

Common quality dimensions include accuracy, completeness, consistency, timeliness, validity, and uniqueness. Additional factors such as relevance, granularity, and traceability can also influence usefulness. Data quality is typically evaluated using metrics, rules, and profiling techniques that compare data against defined expectations and business definitions.

Data quality management is usually part of broader data governance efforts. Roles such as data owners, data stewards, and data quality analysts are responsible for defining quality requirements, monitoring data, and coordinating remediation. The data quality lifecycle spans data definition, capture, storage, processing, distribution, and archiving, with ongoing monitoring and improvement.

Techniques to improve data quality include data profiling to understand current state, data cleansing to fix errors, deduplication to remove duplicates, standardization and normalization to align formats, and enrichment to add missing attributes. Tools and processes often support data validation, lineage tracking, and automated quality checks within ETL/ELT pipelines and master data management initiatives.

Standards and frameworks commonly referenced in data quality programs include ISO 8000, DAMA-DMBOK, and ISO 25012/25024. Effective data quality practices help reduce risk, improve analytics, and support regulatory and operational objectives.