Datenqualitätsmerkmalen

Datenqualitätsmerkmalen, also known as data quality dimensions, are characteristics used to evaluate and describe the fitness of data for its intended purpose. These attributes provide a framework for understanding and improving the reliability and usability of data. Commonly recognized data quality dimensions include accuracy, which refers to the degree to which data correctly represents the real-world object or event it describes. Completeness assesses whether all required data is present, while consistency ensures that data does not contradict itself across different systems or records. Timeliness measures how up-to-date the data is relative to when it is needed. Validity checks if data conforms to defined formats, types, and ranges. Uniqueness guarantees that there are no duplicate records for the same entity. These dimensions are crucial for effective decision-making, efficient operations, and building trust in data. Organizations often define specific metrics and thresholds for each dimension to monitor and manage their data quality effectively. Addressing deficiencies in these characteristics is a fundamental aspect of data governance and data management strategies.