When it comes to information quality, terms like verification and data validation. Both methods contribute to increase the reliability of every bit of information available, and the application of the two sets of techniques is necessary even when, the data validation tends to be a bit more complex.
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Why data validation is not as simple as verification
First, the fundamental reason for this difference is the fact that se puede realizar una verificación sobre los datos contenidos en una databaseA database is an organized set of information that allows you to store, Manage and retrieve data efficiently. Used in various applications, from enterprise systems to online platforms, Databases can be relational or non-relational. Proper design is critical to optimizing performance and ensuring information integrity, thus facilitating informed decision-making in different contexts.... sin tener que recurrir a ningún otro elemento. The only exception would be in cases where no metadata specifications or business rules are included.
But nevertheless, data validation necessarily needs external references to the database. And here comes another aspect that adds that extra level of complexity to a procedure of this type, since In this comparison of the information with that obtained from external sources, you need to examine the data based on:
- Objective criteria: which answer the question of whether a data item correctly represents that aspect of the real world that is intended for the model.
- Subjective criteria: are those used to perform the evaluation of the suitability of the data for a specific purpose.
This means that, it will not only be necessary to know how to select the correct tool to carry out the data validation, but also it will be necessary to have the profiles that can contribute to the final result adding more value, that they are not other than:
- Information users: whose contribution will be very useful whatever the case, especially in data validation in all aspects related to the subjective aspect of validation.
- The data owners: that can be used when there are specific objective evaluation criteria.
Best practices for data validation
In practice, when initiating a procedure data validation the following recommendations should be observed:
- Employing a measurement procedure identical to the original to validate a measurement does not guarantee the same level of precision and reliability as if an alternative method were chosen..
- The same goes for fonts, since data validation it is necessary to contrast with many different, and do it as part of the validation routine.
- Expert judgment should be viewed as a last resort validation technique., especially if it is the only source of validation for a data value.
- Validation should be viewed as more elaborate than simply comparing data values to other known values.. The transformations of the data themselves must be validated to ensure that the result is of the expected quality..
Finally, it is convenient record sufficient documentation and information about the procedure (as well as the tools used and the people involved in it) to meet future needs that may arise.
