The common factors associated with data quality may typically consist of accuracy, completeness, consistency, timeliness, validity, and uniqueness. Accuracy means that data reflects the correct description of an entity. Completeness means the absence of vital fields, whereas consistency involves the use of compatible formats for the same data. Timeliness stands for the up-to-date nature of data that is needed when required. Validity means that the data is valid in accordance with certain rules, formats, and allowable values. Uniqueness prevents data duplication to influence the process of business operations. Companies can measure such criteria usingdata quality assurance procedures. This task is especially relevant for firms that use extensive amounts of data for reporting, marketing, forecasting, compliance, and business intelligence.