The Case for the Data Management Maturity Model

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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.

Success Examples

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. It’s also seen a significant revenue increase thanks to being able to use its embrace of the DMM Data Governance program in the sales cycle as an asset – a way to let customers be assured of its Data Quality guarantee, Mecca says. It’s also able to publish a Business Glossary and Metadata about the data sets required by their customers’ vertical industries.

Neoway product VP and Data Steward Rodrigo Barcia noted that the Data Management Maturity Model program has added value beyond process and governance. It also has added value “in actual product development. DMM brings a direct technical and business impact to our company,” he said.

In another instance, CMMI also recently reassessed early customer Fannie Mae, a leading source of residential mortgage credit in the U.S. secondary market. A little over two years ago, its first assessment of Fannie Mae showed that the organization was quite strong in some areas but needed beefing up in others, Mecca says:

“Now they implemented all the recommendations from the assessments and have done much, much more. The big story is they have succeeded in integrating Enterprise Data Management and the protection and future design of the future data layer with Agile delivery. So they delivered a securities master, a reference master, a single family loan enterprise data store with Agile methods and without sacrificing the care and feeding of the data assets.”

Take A Look at the Next Wave

That’s where a lot of organizations want to go, she says – to be a data-driven organization but one that delivers fast results, too. Tension could result, though, if the need to maintain quality data interferes with Agile iterations. One of the things Fannie Mae has done is to combine the two worlds by having their Agile delivery teams adhere to an Agile readiness set of conditions that also depends on business architecture, the Business Glossary, the logical data model, and so on. CMMI Institute, she says, is combining the Agile profile for software delivery, which it has been developing over many years, with the Data Management Maturity Model to help other organizations make similar leaps.

CMMI Institute also has been focused on delivering a next-generation architecture that lets organizations build their own umbrellas, custom choosing areas of focus – from DMM, which can be split by category or process area; from CMMI-DEV; from the Agile profile for software delivery; and, from its acquisitions best practices, people management best practices or service management best practices – for a seamless assessment. “It will be very, very flexible,” she says.

In harmony with that, Mecca says, is the work it’s undertaking around the DMM appraisal method, which will allow people to use the Data Management Maturity Model as the basis of a true audit of their Data Management capabilities.

Now that CMMI Institute is owned by ISACA, there’s also the opportunity to work together on new products. For instance, CMMI has seen many requests to link ISACA’s COBIT IT governance framework with the DMM. It’s something CMMI has long wanted to do, but now that it’s owned by the organization that owns COBIT, it will be easier to bring the two together. “We may have opportunities with a couple of organizations to turn the DMM onto risk operational controls and audit,” she says.

As it turns out, ISACA was “a really great buyer that we think is perfect for us going forward,” she says. Mecca notes that over the next year or two a main goal is to work with ISACA on version 2.0 of the Data Management Maturity Model, which will include business and programmatic aspects of data security programs, which currently are not in the model.


Photo Credit: Sergey Nivens/

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