What is data quality management (DQM)?
Data quality management (DQM) is a set of practices to detect, understand, prevent, address, and enhance data to support effective decision-making and governance in all business processes. These practices help to gain insights into data health by utilizing diverse processes and technologies on larger and more complex datasets. Effective DQM is a continuous process as data quality is pivotal for extracting actionable and accurate insights from your data.

Why do we need strategic data quality management?
Data quality management is vital for making sense of your data, and ultimately improving the bottom-line. Here are some reasons to practice DQM across your organization:


To provide for and enforce data quality and management rules across departments, while ensuring high-quality information at every step
Reduces costs, prevents errors and oversights by providing a solid information foundation for understanding and controlling expenses
For meeting compliance and risk objectives, implementing policy at the database level within a framework of clear procedures and communication
For visualizing overall data health with the help of graphs and BI reports facilitating functional and tactical decision-makingComprehensive guide to Data Quality Management, covering strategies, tools, and best practices for ensuring accurate, reliable data for improved decision-making