Datakvalitets

Datakvalitets is a term used in Scandinavian contexts to describe the quality of data and the discipline that manages it. In English, it is commonly referred to as data quality. It denotes the degree to which data are correct, complete, consistent, timely, and fit for their intended purpose. Effective datakvalitets programs support reliable analytics, operational efficiency, risk management, and regulatory compliance.

Common dimensions of datakvalitet include accuracy, which measures closeness to the true value; completeness, indicating the presence of required fields or records; timeliness, reflecting how current data are; validity, ensuring data conform to defined formats and business rules; consistency, avoiding contradictions across datasets; and uniqueness, eliminating duplicate records.

Datakvalitet initiatives are typically implemented through data profiling, quality metrics, data cleansing, standardization, enrichment, deduplication, and robust data governance. Data stewardship assigns accountability to data owners, while data lineage tracks origin and transformations, and metadata management provides context. Tools and processes are often aligned with data quality frameworks such as DAMA-DMBOK or ISO 8000, and are integrated into data pipelines and master data management programs.

Challenges include data silos, heterogeneous sources, incomplete metadata, evolving business rules, and privacy constraints. Benefits of strong datakvalitet practices include more reliable reporting, improved decision-making, reduced operational errors, and enhanced compliance with data protection regulations through traceability and auditability. Overall, the discipline aims to ensure that data effectively supports business goals and trusted insights.