Data quality describes how well data meets the requirements of its intended use. High-quality data is typically accurate, complete, consistent, timely, valid, and free from unnecessary duplication. Rather than being an abstract measure, quality depends on whether the data is reliable enough for a specific task, such as reporting, customer analysis, or machine learning.
For example, an inventory system may contain technically valid product records but still have poor data quality if stock quantities are outdated or product identifiers conflict across systems. Data quality processes identify and correct such issues through validation, cleansing, profiling, and monitoring. Related concepts include data integrity, data governance, data observability, and data cleansing.
