What practices are involved in data quality management?

Data quality management encompasses a range of practices aimed at ensuring that data is accurate, consistent, and reliable throughout its lifecycle. Maintaining data accuracy and cleanliness is fundamental to this process, as it involves processes such as data cleansing, validation, and verification.

This practice ensures that the data meets the necessary quality standards required for effective analysis and decision-making. It addresses issues like removing duplicates, correcting inconsistencies, and ensuring that the data adheres to defined formats and standards, which protects against errors that could lead to flawed analyses and poor business decisions.

While the other choices involve relevant aspects of data handling or usage, they do not directly address the core objectives of data quality management. Encryption relates more to data security, visualization pertains to how data is presented and interpreted, and collecting user feedback is part of the user experience management rather than a direct practice of data quality management.