Datalaatu

Datalaatu is a term used in information management to describe the degree to which data fulfills the needs of its intended use. It encompasses several dimensions, commonly including accuracy, completeness, consistency, timeliness, validity, and uniqueness. Datalaatu is important for decision making, operational processes, analytics, and regulatory compliance, because poor quality data can lead to incorrect insights or faulty actions.

Assessment of datalaatu relies on data profiling, quality metrics, and data lineage. Common metrics include error rates, missingness, duplication rate, and data freshness. A data quality program typically follows a lifecycle: define quality requirements, measure against them, remediate issues, and monitor ongoing quality. Data governance structures assign responsibilities to data stewards and data owners to enforce standards and accountability.

Practices to improve datalaatu include data cleansing, normalization and standardization, deduplication, enrichment from reference data, and validation against business rules. Data quality is often integrated into data pipelines and enterprise data warehouses using validation checks, automated tests, and quality dashboards. Standards and frameworks such as ISO 8000, ISO 25012, and DAMA-DMBOK guide data quality management in organizations.

Common challenges include data silos, inconsistent metadata, varying data definitions across sources, and the pace of data growth. Benefits of strong datalaatu programs include more reliable reporting, better risk management, reduced operational costs, and enhanced trust in analytics.