In real life, there are data quality issues. Many data quality issues have ‘solutions’ (Osborn (2024), Rao (2024) and Elahi (2022)). In this article, we present a few ‘limits to data quality’. Issues that we feel are root-causes, hard to resolve. We suggest that they deserve more attention and may well influence data quality expectations.
DNB (2017) describes the requirements for a data quality management framework in various areas. We identify root causes for data quality issues in three of these: (1) data architecture, (2) external providers and (3) objectives. We end with concluding comments.
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- Limits to data quality?! .pdf • 0,1 MB
