In DAMA-DMBOK, what is a key aspect of data integration?

The key aspect of data integration is indeed the combination of data from different sources to provide a unified view. This process is critical in the DAMA-DMBOK framework as it enables organizations to consolidate disparate data streams into a cohesive and comprehensive dataset.

By integrating data, organizations can achieve a holistic perspective, which is essential for effective analysis, reporting, and decision-making. A unified view not only enhances the quality of insights derived from the data but also promotes consistency and accuracy across various business functions. This integration is often achieved through various methods, such as Extract, Transform, Load (ETL) processes, data warehousing, and real-time data integration, which collectively work to ensure that data from different systems can be worked with as if it originated from a single source.

While data visualization, data accuracy testing, and locating missing data are important aspects of data management, they do not capture the central focus of data integration as outlined by the DAMA-DMBOK. Data visualization serves to present data effectively, data accuracy testing ensures data validity, and locating missing data addresses completeness, but the essence of integration lies in the harmonization of disparate data sets into a single, coherent framework.