Planning and Development Research

 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

                                                                                                                                                          Guidelines for  Data Quality Assessment (DQA) What is Data Quality Assessment (DQA)? DQA stands for Data Quality Assessment or Data Quality Audit. It is a systematic process of evaluating the quality of data that is being collected, processed, stored, and used in a program or project. The objective of DQA is to identify and address any issues or challenges related to data quality that may affect the validity, reliability, and usefulness of the data. The DQA process typically involves a review of data collection methods, data entry processes, data management systems, data analysis procedures, and data reporting and dissemination processes. The DQA may also include a review of the quality of the data itself, including data completeness, accuracy, consistency, and timeliness. The results of the DQA are used to identify areas for impr