Data Quality Management, or DQM, is an important component of data management. However, many Data Owners and Data Stewards stumble when it comes to implementing DQM successfully. Should you profile data or define data quality first? What can you use to report data quality metrics? What should you do if data quality issues arise when you’re trying to make improvements based on your initial assessment?
This month’s infographic is an example operating model of proven and successful Data Quality Management. It touches on the steps that are critical to a successful Data Quality Management Program and includes the outputs and tools necessary to operationalize data quality. Following these steps enables organizations to improve the quality of their data, but more importantly, it ensures that organizations will be able to achieve any business objectives that rely on high-quality data.
View the infographic below:
Want a closer look at how to manage your data quality effectively? Best practices and other information on DQM can be found in our white paper, Building a Successful Data Quality Management Program, now available on our corporate website.