Crafting a data quality scorecard

Some questions, such as whether there is milk in the fridge, have straightforward answers, but others, like assessing data quality for AI initiatives, are more complex. Data quality management aims to ensure data is accurate, complete, consistent, and reliable, yet it is often likened to playing whack-a-mole due to the unpredictable issues that can arise across its eight dimensions. While the goal is for data to be pristine and ready for analytics immediately after entering a warehouse, this ideal is difficult to achieve in practice, requiring constant vigilance and problem-solving to maintain data integrity for all systems and consumers.

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