Data quality management refers to a continuous cycle of measurement, monitoring, cleansing, validation, and improvement of data from end-to-end of its lifecycle. The practice allows companies to ensure that their information is accurate, complete, consistent, reliable, and up to date. Data quality management is essential since bad-quality data could cause inaccurate reporting, inefficiency, poor customer experience, and decision-making issues in businesses. The best practice often involves such key steps as data profiling, cleansing, validation, constant monitoring, and governance. Companies may also apply some automated software for detecting duplicates, missing fields, formatting issues, and inconsistencies. Data quality solutions will help in conducting all these tasks in a more convenient way through decreasing the amount of manual effort and ensuring constant data quality visibility. Organizations with reliable data will be able to make better decisions and improve analysis, automation, and customer experience. Overall, data quality management helps businesses to transform their data into business data.