The Gartner Master Data Management maturity model provides MDM leaders and other information managers with a framework to measure the maturity level of their organization's MDM capabilities. It can also be used as a basis for creating a future MDM vision and roadmap to reach it. All rights reserved. Gartner is a registered trademark of Gartner, Inc. While the information contained in this publication has been obtained from sources believed to be reliable, Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartner research may address legal and financial issues, Gartner does not provide legal or investment advice and its research should not be construed or used as such.
As data continues to grow exponentially, business leaders have access to more raw performance data than ever before. However, many organizations have no idea how to harness the power of data for their business. The DMM Program provides the best practices roadmap and services to help organizations build, improve, and measure their enterprise data management function and staff. The program centers around the Data Management Maturity DMM model , a comprehensive framework of data management practices in six key categories that helps organizations benchmark their capabilities, identify strengths and gaps, and leverage their data assets to improve business performance. The identification of current and target states supports the elimination of redundant data and streamlines processes. The DMM model outlines data process improvement across business lines, allowing executives to make better and faster decisions using a strategic view of their data. A DMM assessment allows an organization to quickly evaluate its current state of data management maturity relative to key goals and achieve actionable improvements, both strategic and tactical, to its data management program.
This means that organizations who successfully do this consider the who — what — how — when — where and why of data to not only ensure security and compliance, but to extract value from all the information collected and stored across the business — improving business performance. This is data governance , and most organizations are doing some sort of this without even knowing it. According to the State of Data Management , data governance is one of the top 5 strategic initiatives for global organizations in Since technology trends such as Machine Learning and AI rely on data quality, and with the push of digital transformation initiatives across the globe, this trend is likely not going to change any time soon.
To improve Data Management DM processes within a business first requires a detailed assessment of the current state of Data Management within the organization and must include some sort of best practice framework to measure against. In the presentation Mrs. Reeve discussed the process that she implemented with a mortgage bank the actual name of the bank was not disclosed , the results of the assessment and how other organizations could use such a plan for their own assessments. Reeve also added Data Integration Management to the list for a total of ten areas that needed to be assessed. The list of Data Integration Management activities and tools were actually taken from the other nine areas, but used specifically in reference to data integration.