The Case for the Data Management Maturity Model
The CMMI Institute announced its Data Management Maturity Model (DMM) back in 2014 to enable organizations to improve Data Management practices across their entire business. Some two-and-a-half years later, the technology-independent model continues to help organizations optimize their data assets is being successfully used by organizations across all industries to meet many Data Management objectives.
Based on the foundational principles of the Capability Maturity Model Integration (CMMI) that has been of help to more than 10,000 organizations worldwide, the DMM aims to help companies become more proficient in their management of critical data and to provide a consistent and comparable benchmark for help in controlling operational risk. “Our objective for DMM is to be a practical, implementable global standard for measuring capabilities and maturity,” says Melanie Mecca, Director of Data Management Products and Services at CMMI, which last year was acquired by ISACA.
“Our primary purpose is to unify [Enterprise Data Management as a whole from the] perspective of the lines of business, IT and the Data Management core,” she says.
The model spans six categories – Data Management Strategy, Data Governance, Data Quality, Data Operations, Platform Architecture and supporting processes – and 25 process areas. For instance, Data Quality includes Data Quality Strategy, Data Profiling, Data Quality Assessment and Data Cleansing, while the Data Governance category includes Governance Management, Business Glossary, and Metadata Management.
“We’ve distilled the successive weight-lifting capabilities organizations demonstrate over time that are signs of having a well-engaged and effective program” in each category that composes the framework, Mecca explains. The focus is on the activities and work products that are typically produced in performing these activities, and on helping organizations to understand where the gaps lie in their own pursuit of these progressive levels and on helping them identify a gradated path to improvement that is easily tailored to their business strategies, strengths, and priorities.
“It’s the what [to implement], not the how, that makes it very usable and it’s completely flexible,” says Mecca of the Data Management Maturity Model.
The DMM’s approach is to let organizations implement categories and processes according to what will bring their businesses the greatest value. Mecca points to Neoway Business Solutions, a Brazilian-based Analytics-as-a-Service vendor, as one organization that is taking advantage of the Data Management Maturity Model’s flexibility to meet its most pressing business needs.
Neoway started with Data Governance and Data Quality, using the DMM Model to create an organizational vision and definition for Data Quality; disseminate knowledge regarding data and its usage to the organization; develop and manage documentation to support the Data Governance Program; establish responsibility flow around data processes; define processes to manage data more effectively; and, implement a defined process for handling issues related to Data Quality. As a result, the company has been able to track and resolve more than 70 major Data Quality production issues, and is now better able to pinpoint Data Quality issues and determine if solutions are meeting expected results.
