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What is Data Quality and Master Data Management?

Data quality can be defined in various ways. In the most general sense, good data quality is formed when the data is suitable for the use cases at hand.

This means that quality always depends on the context used, which leads to the conclusion that there is no absolute valid quality benchmark. If you want to know more about data quality management then you can search for various online sources.

Nonetheless, some of the definitions use the following rules to evaluate data quality:

Completeness: the values are missing?

Validity: whether the data in accordance with the rules?

Uniqueness: is there any data that is duplicated?

Consistency: is the data consistent across various data stores?

Timeliness: whether the data represent the reality of the required point in time?

Accuracy: the extent to which the data represent reality

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In the context of business intelligence, master data management's goal is to bring together and exchange of master data such as customer, supplier or product master data from different applications or data silos. master data management required …

… because apart from the "master" ERP systems, many companies are working with the CRM system or other SCM or Web services. Master data management ensures the consistency of data in these systems;

… to easily integrate these systems corporate mergers and acquisitions;

… to collaborate effectively with business partners;

… to provide optimal customer experience;

… to build a 360-degree customer requires the customer's address in the best way;

… to combine on-premise and cloud-based systems.

Valid data is located in the heart of steering the strategic, tactical and operational organization. Has the appropriate data quality processes in place directly correlated with the ability of the organization to make the right decisions and ensure economic success.