The reliability and application efficiency of data. It is a perception or an assessment of dataset’s fitness to serve its purpose in a given context. Aspects of data quality include: Accuracy, Completeness, Update status, Relevance, Consistency across data sources, Reliability, Appropriate presentation, Accessibility. Within an organization, acceptable data quality is crucial to operational and transactional processes and to the reliability of analytics, business intelligence, and reporting. Data quality is affected by the way data are entered, stored and managed. Maintaining data quality requires going through the data periodically and scrubbing it. Typically this involves updating, standardizing, and de-duplicating records to create a single view of the data, even if it is stored in multiple disparate systems. Data quality assurance (DQA) is the process of verifying the reliability and effectiveness of data.