The Data Management Maturity Model (DMMM) is a set of recommendations that organizations can use to develop, improve, and assess their corporate data management capacity. It’s a standardized, company-wide framework for implementing data management policies. As a result, data throughout the entire business is accurate, timely, and accessible. The DMMM adheres to industry-standard data strategies, rules, and regulations.

What does the DMMM provide?

1. A step-by-step guide to implementing, embedding, and measuring data management capabilities.
2. A common approach for tracking progress that any divisional data office can employ.
3. A standard against which data councils can be compared (i.e. groups of data stakeholders).
4. A detailed list of artefacts that can be used to show proof and progress.

The Software Engineering Institute (SEITM) at Carnegie Mellon University began developing a maturity framework to help firms improve their software processes in August 1986, with assistance from the MITRE Corporation.

Data management models have developed since then, and there are now a myriad of solutions to select from. DAMA-DMBOK, DCAM v2, CMMI CERT-RMM, IBM Data Governance Council Maturity Model, Stanford Data Governance Maturity Model, Gartner’s Enterprise Information Management Maturity Model, and Orange Maturity Model are some of the most well-known. To find out which maturity models are most widely employed, we performed a survey.

According to the findings, 50% of respondents utilise DCAM v2 (the EDM Council’s Data Capability Assessment Model), while 17% use DAMA, the CMMI custom maturity models. DCAM v2 is certainly a popular choice, but it is critical to ensure that it is deployed appropriately.

Collibra – or any other metadata management solution – can be used to link your metadata to generate an objective assessment of a data council’s maturity. The results of this evaluation can then be shown on a graphical dashboard that includes the following data:

1. DMMM Heat Map: A heat map picture of the data council’s maturity model progress.
2. Maturity Plans: A complete perspective of the maturity model, including its current state, goal dates, and next stages.
3. Details at the row level: A thorough view of the maturity model taken directly from Collibra or another metadata management solution.
4. Framework for Data Policy and Compliance (DPCF) The DPCF scorecard can be accessed via this link.

Maturity: Getting There and Staying There:

Data modelling, issue reporting, and IT estate documentation are all constantly growing activities, therefore it’s inevitable that not all data governance and management parts of a company will be mature at first. It is possible to increase one’s maturity level by making the necessary changes, implying that maturity is not a static state. However, once maturity is attained, it is regarded stable, therefore the occurrence of challenges does not instantly result in a loss of maturity. For example, the process of switching data management solutions may lead an organisation to temporarily fail to comply with regulations, but this is not a problem as long as the migration period is permitted.

Summary:

There are many different data management and governance maturity models to choose from, each with its own methodology. DCAM v2 is extensively used across the industry, according to our survey, and serves as a benchmark for data management maturity.

OSTHUS has expertise in most of the maturity models and can help firms coordinate their data strategy and select the optimal maturity assessment toolkit as an EDM Council member and DCAMv2 partner. We’ve completed many maturity evaluations for various life science companies and also provide our own proprietary data management maturity model. Our philosophy is to use naturally available tools to deliver the best solution to your data management difficulties rather than reinventing the wheel. Please schedule an appointment with one of our data governance specialists at OSTHUS as soon as possible for additional information.