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Data Quality can be defined as fitness of your data for its intended use. Data Quality can have a direct impact on intended functions and therefore, normalizing and staging your data outside of the production data environment can be considered as the first step in creating a program to discover and profile data. Data quality rules should be set up that defined constraints on which data would be assessed against. These are based upon the core data quality dimensions (completeness, validity, accuracy, consistency and timeliness.). Assessing data quality depends on five core dimensions - completeness, validity, accuracy, consistency and timeliness. Reporting data quality assessment results to the concerned person/owners who are actually responsible for quality is an important part of the program. Data quality program is a repeatable process that provides the organization with an up-to-date view of the quality of its data asset. The framework for decision makers for their thought processes is data collected and used by auditors to ensure regulatory compliance, among other reasons. Data used on the basis of fitness of the data for intended use is called Data Quality. The consequences of poor data quality depend on the specified use of data but consequences could include lost revenue, dissatisfied customers or, in extreme cases, fines or even imprisonment due to regulatory violations.
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