The technique can be one of the best ways to ensure completeness of data. Although this seems like an easy validation to perform, enterprises don't often use it.
For example, if you are pulling information from a billing system, you can take total billing for a single day and ensure totals match on the data warehouse as well.
Source system loop back verification: In this technique, you perform aggregate-based verifications of your subject areas and ensure it matches the originating data source. Here are a few data validation techniques that may be missing in your environment. 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. 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.įull data-quality frameworks can be time-consuming and costly to establish. This is a lesson that is very costly to learn the hard way. Taking data quality seriously can be difficult if agility and speed-to-market are the name of the game for your business users. 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. Six Validation Techniques to Improve Your Data Qualityīoost the data quality of your data warehouse with six practical techniques.
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