The most important aspect of DQM is data quality analysis. The analysis shows how well certain data is suited to the respective problems or applications. There are many factors that are decisive for making statements about data quality. The most important factors include the correctness of the data, the timeliness of the data, the consistency of the data, the completeness of the data and the relevance of the data for the respective area of ​​application. In order to better understand the processes of DQM, it is worth going through some typical steps in data quality management.

Data quality management helps to ensure the quality of the data.

First, a data catalog should be used. This contains all data including so-called metadata. Metadata provide information about the properties of the data and can also be described as “data about data”. They ensure a complete and consistent data record and thus uniform values, which are an important aspect of data quality management.

The second best practice for DQM is the so-called first-time-right principle. The English mnemonic states that an incomprehensible, incomplete or inaccurate entry of data should be avoided during creation. The focus is therefore on correct data entry. Automatically created data must also be checked by humans for correctness and completeness.

What must also be taken into account in DQM is data cleansing and data profiling. Data Cleansing keeps data tidy by deleting or converting redundant data. These processes can now be automated and rarely require manual intervention. Data Cleansing deletes duplicate data, completes incomplete records and checks data types. With data profiling, data is analyzed and summarized in a clear way. The systems behind data profiling use analysis algorithms to recognize the most important characteristics of data sets and check this data for errors, inconsistencies and contradictions.

Finally, there is the closed-loop principle in data quality management. Optimizing data quality is an iterative process and should be carried out on every project from start to finish. Only when the closed-loop principle is applied and data quality management becomes a matter of course in the company can long-term success strategies work with the help of company data.

What are the benefits of Data Quality Management?

The advantages of data quality management are manifold and lead, among other things, to an improvement in business operations by making them more efficient. One of the greatest advantages of DQM is the recognition of new opportunities. When business decisions are based on qualitative data, it is more likely to produce positive results. Decisions for important processes in the company can thus be made more easily.

Consumers and buyers also benefit from data quality management. Companies can thus get an exact picture of the preferences of their customers. Thanks to the data quality management, the desired information can be forwarded to the customer more quickly. Another benefit is simplified data migration. This often fails in companies because large amounts of data with poor data quality have to be transmitted. By implementing data quality rules, erroneous data records are already prevented before data migration.

Poor data quality can have a high financial impact on companies. This is due to the fact that correcting incorrect data manually is time-consuming. By ensuring correct data, DQM helps save time and money in data processing.


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