The 6 data quality dimensions

To comprehensively address the problem of data quality, an analysis should be performed for each of the data quality dimensions, thus managing to solve each of the doubts in the procedure and thus mitigate the risks of failure in the procedure. projects of this type.

To that end, the important and priority is to have a starting point, a metric to identify the current state of the data.

For this, an initial audit or profiling of the data is essential, in order to know what state they are in and from there, detect what must be corrected and in turn determine control parameters that help measure progress in quality processes. .

These parameters are known as the six dimensions of data quality and are considered the key points that data quality must cover to guarantee our cleanliness and quality processes..

What are the six dimensions of data quality?

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In some cases, data that does not exist is irrelevant, but when they become necessary for a business procedure, they become critical.

Accordance

The data found in the fields of the table must be in a standard and readable format.

Consistency

When comparing the information with the records, should avoid contradictory information.

Precision accuracy

If the data is not accurate, they cannot be used. To that end, to detect if these are accurate, data is compared to a reference source.

Duplication

It is essential to know if you have the same information in the same or similar formats within the table.

Integrity

Another important quality dimension lies in knowing if all the relevant information in a record is present in a usable way..

Understanding these six dimensions is the first step in driving data quality. Be able to identify and separate data flaws, classifying them by these dimensions, enables us to apply the appropriate techniques to drive both the information and the processes that create and manipulate information.