ABSTRACT
The definition of data quality is associated with the fitness-for-use principle. Data quality is a prerequisite for data-driven decision-making and monetization, requires focus in any organization, and is business critical.
Data quality includes the intrinsic data quality dimensions of completeness, accuracy, format, and currency. In addition to intrinsic data quality, there are three other dimensions: contextual, representational, and accessibility.
There is a positive relationship between data integration and business intelligence representational fidelity. Nevertheless, data integration supports the exchange of data in different systems, databases, and data lakes and contributes to data quality. The need for data integration is increasing due to the emerging data mesh trend. The data mesh will be addressed in the next chapter on data governance.
To improve data quality, organizations must cleanse their data. Many organizations use tooling to identify and fix data quality issues for data cleansing. Furthermore, legal aspects need to be addressed. Monitoring and improving data quality is a continuous process and requires dedicated focus in any organization. Senior management should take a close look at data quality management projects to understand the impacts of data quality on the organization and provide guiderails to their organization.
