Implementing data quality control in a data warehouse

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Learn how to approach implementing data quality control in DWH and whether or not it is a good idea to use specialized tools.
Examining the characteristics of data quality assessment processes , assessing the different options that can be used for their implementation and having clear criteria in relation to the duration and particularities of the DWH project or the type of tools used, is necessary to successfully complete the implementation of data quality control in a data warehouse .

The objective is to guarantee the quality of the data with all its attributes and, therefore, after the implementation of a data quality control process , it is assumed that at least the following circumstances occur:

- Stability of the technical quality of the data source , according to the fusion database of the DWH model and the ETL processes.

- Definition of business data quality by business users.

- Maturity in the data quality analysis methods used.

Therefore, during the implementation phase it is necessary to take into account that:

- The lack of suitability of data that does not meet quality requirements will prevent it from being loaded into the Data Warehouse and will require its correction.

- Data that is adequate in quality for one business user may not be adequate for another.

- Business data quality requirements are subject to constant transformation, which is the result of the evolution of analysis techniques.

- Data that does not meet the requirements of one of the business users should not be used in their methods. In these cases, data correction can be performed at the same time as data correction is performed.

One of the aspects that must be clear when trying to manage control over one of the most critical attributes of data is the criteria necessary for its technical quality :

- Be unique in your field.