The car was evaluated as both high quality and low quality, even though nothing about the car changed.
It was your perception of the car’s quality relative to a new purpose that changed.
When talking about data quality, we must therefore be clear about whose purpose, what requirements, established when, and by whom.
Within the context of the DMBoK definition, the answer is switzerland whatsapp number data that every consumer evaluates the quality of a data set independently. Data is considered to be of high quality when it is fit for my purpose, satisfies my requirements, established by me when I need the data.
Truly in the eye of the beholder.
Furthermore, data quality analyses cannot be leveraged by new consumers. For decades, we in decision support have been selling the benefits of leveraging data across applications and analyses. It has been the fundamental justification for data warehouses, data lakes, data lakehouses, etc. But misalignment between the purpose for which data was created and the purpose for which it is being used may not be immediately apparent. Especially when the data is not well understood. The consequences are faulty models and erroneous analyses. We reflexively blame the quality of the data, but that’s not where the problem lies.