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Last updated on 2024年8月5日

How can you use the CMMI data management maturity model to improve your data governance?

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Data governance is the practice of managing the quality, security, availability, and usability of data in an organization. It involves defining roles, responsibilities, policies, standards, and processes for data-related activities. Data governance can help you achieve better business outcomes, comply with regulations, mitigate risks, and foster a data-driven culture. But how can you measure and improve your data governance maturity?

One of the tools you can use is the CMMI data management maturity (DMM) model. The DMM model is a framework that helps you assess your current state of data management and identify gaps and opportunities for improvement. It also provides best practices and guidance for advancing to higher levels of maturity. The DMM model covers six critical data management capabilities: data governance, data quality, data operations, data architecture, data development, and data security.

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Phyllis Ogo Ogah

Nutrition Clinical Exercise Physiology Circular Economy & sustainability.
Art Freas

1 What are the DMM levels?

The DMM model defines five levels of maturity, from initial to optimized, each representing a different degree of capability and performance in data management. At Level 1, data management is ad hoc, inconsistent, and reactive with no formal policies, processes, or standards. Data quality and security are low and data issues are frequent and costly. At Level 2, data management is project-based, repeatable, and responsive with some basic policies, processes, and standards that are not widely applied or enforced. Data quality and security are variable and data issues are common and moderate. Level 3 is enterprise-wide with clear and comprehensive policies, processes, and standards that are aligned with business goals and stakeholder needs. Data quality and security are high and data issues are rare and minor. Level 4 is data-driven with metrics and indicators for measuring and monitoring performance. Data quality and security are continuously improved while data issues are anticipated and prevented. Finally, at Level 5, data management is innovative, optimized, and adaptive with mechanisms for learning best practices and applying emerging technologies. Data quality and security are excellent while data issues are resolved quickly and effectively.

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Art Freas
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As a co-contributor to the model there are a few things that stand out in the DMM. 1) The model identifies the types of models that support the various process areas and levels of maturity. It gives you a lot more of an ability to know how to move between levels. 2) The model was built by practitioners, all of the team had extensive real world experience with data management. 3) The contributors came from vastly different industries aerospace, finance, computer software, consulting, and others. This resulted in a model that was tested by many perspectives, resulting in a widely usable mode.

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Elisha I.

Senior Data Science & Strategy Consultant at EY | AI & Machine Learning | Risk Assessment | ???
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The DMM levels provide a structured ladder from initial chaotic data practices to a refined, optimized state. Personally, having worked with various organizations, I've seen that starting at Level 1, where everything is ad hoc, feels overwhelming. But it’s also where you see the most immediate improvements.

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Muhammad Rawish Siddiqui

Manager - Data Management (DAMA - CDMP, CDPO, GDPR, OCP, EDRP, KSA PDPL, NDMO)
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The Data Management Maturity (DMM) model provides a framework to assess and improve data management practices. It is designed to help organizations evaluate their current data management capabilities and develop a roadmap for improvement. The DMM typically includes several maturity levels, which represent the progression from initial, ad-hoc practices to optimized and continually improving data management processes.

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2 How can you use the DMM model?

The DMM model can help you improve your data governance in several ways, such as conducting a DMM assessment, developing a DMM roadmap, implementing and monitoring the roadmap, and reviewing and updating it. A DMM assessment is a structured process that evaluates your current data management capabilities and maturity level. It will help you identify your strengths and weaknesses, as well as gaps and risks in your data management practices. The DMM roadmap is a strategic plan that outlines your data management vision, goals, objectives, and actions. It defines the roles, responsibilities, resources, and timelines for implementing the plan. To execute and track the progress of your data management activities, you can use the DMM model as a benchmark to measure and report your data management performance and outcomes. As your data management matures, you need to review and update your DMM roadmap regularly. The DMM model can be used as a framework to evaluate and improve your data management capabilities and maturity level. Ultimately, it will help you ensure that your data governance is effective, efficient, and consistent.

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Elisha I.

Senior Data Science & Strategy Consultant at EY | AI & Machine Learning | Risk Assessment | ???
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In my experience, using the DMM model is like having a GPS for your data management journey. Initially, conducting a DMM assessment can be eye-opening. It lays bare the strengths but, more critically, the weaknesses. Crafting a DMM roadmap isn’t just about compliance or organization—it’s about aligning data management to real business outcomes.

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3 What are the benefits of using the DMM model?

The DMM model can bring many benefits to your data governance and organization, such as improved data quality, enhanced data security, increased data availability and usability, and strengthened data culture. By using the DMM model, you can establish and enforce data quality standards and processes that ensure accuracy, completeness, timeliness, and consistency of your data. This will reduce errors, rework, and waste while increasing trust in your data. Additionally, the DMM model can help you implement and monitor security policies and controls that protect your data from unauthorized access. It can also help design and maintain data architectures that enable easy access, integration, and analysis of your data. Finally, the DMM model can foster a data culture that promotes awareness, literacy, ownership, and collaboration across the organization. This will empower and engage your stakeholders while leveraging your data as a strategic asset.

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Phyllis Ogo Ogah

Nutrition Clinical Exercise Physiology Circular Economy & sustainability.
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The DDM provides a structured framework for assessing digital maturity across multiple dimensions, including strategy, technology, data, and culture. This comprehensive approach allows organizations to gain a holistic view of their digital readiness.

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Mathias Vercauteren

Data Governance expert & strategic advisor | Building corporate data governance programs in weeks | PhD candidate | Author "Data Governance Sprint"
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The big benefit of using the DMM model is that it sets a benchmark for future measurement. I regularly utilize the DMM model to comprehensively assess a client's current state, as well as to envision their desired future state and bridge the gap in between.

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Nikolai Sokolov

IT Executive | Digital Transformation | IT Strategy | Effective Teams
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A few years ago, our organization recognized the escalating need for Business Intelligence (BI) and informed decision-making, prompting us to elevate our data management maturity and enhance data quality. This initiative enabled us to delineate clear goals, prioritize them effectively, and develop a roadmap for systematically advancing data management and quality, with a focus on critical areas.

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Elisha I.

Senior Data Science & Strategy Consultant at EY | AI & Machine Learning | Risk Assessment | ???
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Implementing the DMM model also naturally leads to better data availability, making the data not only more accessible but also more actionable, fostering a proactive data culture across departments.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ariel Montejo

Business Intelligence | Applied Statistics | Certified Enterprise Data Management Associate
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Yo obtuve la certificación de associate en 2021, luego el modelo fue retirado y absorbido parcialmente dentro del modelo de CMMI, el contenido y los beneficios siguen vigentes, pero ya no es posible certificarse o elevar el nivel de la certificación obtenida. En el sitio web de CMMI la página de DMM ya fue retirada

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How can you use the CMMI data management maturity model to improve your data governance?

1

2

3

4

1 What are the DMM levels?

2 How can you use the DMM model?

3 What are the benefits of using the DMM model?

4 Here’s what else to consider

Data Governance

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How can you use the CMMI data management maturity model to improve your data governance?

1

2

3

4

1 What are the DMM levels?

2 How can you use the DMM model?

3 What are the benefits of using the DMM model?

4 Here’s what else to consider

Data Governance

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