Datenqualitätsänderungen

Datenqualitätsänderungen, often translated as data quality changes, refer to modifications in the accuracy, completeness, consistency, timeliness, validity, and uniqueness of data over time. These changes can occur due to various factors, including human error during data entry or updates, system malfunctions, changes in data sources or definitions, evolving business processes, or deliberate data manipulation.

Understanding and managing Datenqualitätsänderungen is crucial for organizations relying on data for decision-making, operations, and strategic planning. Unmanaged changes can lead to incorrect insights, flawed predictions, inefficient processes, and a loss of trust in the data itself. For instance, a change in the format of a customer address might render historical sales data unusable for geographical analysis if not properly handled.

Proactive measures to address Datenqualitätsänderungen involve establishing robust data governance frameworks, implementing data validation rules, conducting regular data quality audits, and employing data lineage tracking to understand the origin and transformation of data. When changes are detected, a systematic approach to remediation is necessary, which might include data cleansing, reformatting, enrichment, or the establishment of new data quality rules. The goal is to ensure that data remains fit for its intended purpose throughout its lifecycle.