Importance of validating data

Here are a few data validation techniques that may be missing in your environment.

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.

Internal validity refers to the validity of the measurement and test itself, whereas external validity refers to the ability to generalise the findings to the target population.

Both are very important in analysing the appropriateness, meaningfulness and usefulness of a research study.

This is a lesson that is very costly to learn the hard way. After all, it's not like you didn't have validation checks as part of your standard process.Face validity-This refers to whether a technique looks as if it should measure the variable it intends to measure.For example, a method where a participant is required to click a button as soon as a stimulus appears and this time is measured appears to have face validity for measuring reaction time.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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