DatenqualitätsScores

DatenqualitätsScores, often translated as data quality scores, are quantifiable metrics used to assess the accuracy, completeness, consistency, timeliness, and validity of data. These scores provide a standardized way to understand the overall health and reliability of a dataset or data system. A higher score generally indicates better data quality, while a lower score suggests potential issues that may need addressing.

The calculation of a DatenqualitätsScore typically involves defining specific data quality dimensions and then assigning weights to each dimension based on its importance to the business or application. Rules are established for each dimension, and data is evaluated against these rules. For example, a completeness rule might check if all mandatory fields are populated, while a validity rule could verify if a date format is correct.

The resulting score can be a single aggregated number or a set of scores for each individual dimension. Organizations use these scores for various purposes, including prioritizing data cleansing efforts, monitoring the impact of data governance initiatives, and making informed decisions based on reliable information. A consistent and objective measurement of data quality empowers businesses to identify weaknesses, track improvements over time, and ultimately enhance the trustworthiness of their data assets.