Data quality refers to the overall fitness of data for its intended use. It encompasses several attributes such as accuracy, completeness, consistency, and relevance. Indicators of data quality include: Accuracy: The degree to which data accurately reflects the real-world phenomena it represents Completeness: The degree to which all relevant data is captured Consistency: The degree to which data is consistent across different sources and over time Relevance: The degree to which the data is relevant to the task at hand Timeliness: The degree to which the data is current Validity: The degree to which the data conforms to the rules of the data model Uniqueness: The degree to which records have a unique identifier. It's important to note that data quality can vary depending on the specific use case and context. Therefore, organizations should establish and implement specific data quality measures and indicators tailored to their needs. Accuracy : Accuracy refers to how closely the dat...