Högskolan i Skövde

This case study evaluates the results of data quality efforts in an integrated database at an IT service provider using the data quality framework TDQM and the holistic data management guide DAMA-DMBOK focusing on the data quality knowledge area. Using a method that combines quantitative and qualitative approaches, quantitative data quality metrics were measured using Python before and after the ETL process, and the results were compared with qualitative interviews with stakeholders using the “Inference to the Best Explanation” (IBET) method. The results indicate a dual impact of the ETL architecture: while it functions as a corrective mechanism that significantly improves accuracy (from 13.3% to 82.5% in one system), it simultaneously reduces the completeness of the CustomerName attribute from 100% to 94.3% and 96.8%, respectively. An analysis of the underlying causes shows that this deterioration is due to a lack of organizational oversight and proactive input controls during manual data entry in the source systems, causing the ETL’s deterministic mapping logic to fail. This lack of completeness directly leads to financial inaccuracies, inefficient manual workarounds, and reduced user confidence. Consequently, the study concludes that a shift from reactive corrections to robust data governance and input validation at the source level is crucial to ensuring that integrated business data remains fit for use.