Six Validation Techniques to Improve Your Data Quality
Have you ever had a set of reports that were distributed for years only to have your business users discover that the reports have been wrong all along and consequently lose trust in your data warehouse environment? Gaining trust is the foundation of user adoption and business value of your data management program.
Taking data quality seriously can be difficult if agility and speed-to-market are the name of the game for your business users. This is a lesson that is very costly to learn the hard way. How do you prevent these issues from occurring? After all, it’s not like you didn’t have validation checks as part of your standard process.
Full data-quality frameworks can be time-consuming and costly to establish. The costs are lower if you institute your data quality steps upfront in your original design process, but it is a valuable exercise to review and overhaul your data quality practices if you only have basic checks in place today.
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