EDM Council breaks away from CMMI Institute data management model

Source: EDM Council

EDMC and the CMMI Institute were working together on the Data Management Maturity Model (DMM) until 2013 but each organization has now developed its own model regarding data management.

All of our activity on data management best practices has now been incorporated into the new Data Management Capability Model (DCAM). As a result, the Council no longer has any affiliation with the former Data Management Maturity Model now being administered by the CMMI Institute.

The DCAM is our enhanced compilation of everything we have been doing associated with the practice of data management (i.e. governance, operating model, data quality, data architecture, etc.). It is being coordinated by John Bottega on behalf of the Council. For more information on the DCAM, the scoring methodology, our facilitated assessment program and how we are linking the model to our overall best practices agenda - please contact John (jbottega@edmcouncil.org +1.908.510.3826)

Council members can download a copy of the DCAM via the website [member log-in required]. The working sessions to review the DCAM content, structure and scoring methodology are currently underway. Working sessions take place every Monday at 10:00 eastern via go-to-meeting. Participation is open to all EDMC members. Please contact Carole Mahoney for information on how to join the working sessions.

We are grateful to all EDMC members who have provided input and advice that has led to the production of the DCAM. We are in the process of mapping the April 2013 draft of EDM Council's original data maturity document to the DCAM to support our members who have been using the previous version as the benchmark for their internal data management activities.
 

Data Management Capability Assessment Model (DCAM)

The DCAM is organized into eight chapters (components) covering the full spectrum of requirements for effective data management. The 8 components include:

• Data Management Strategy: defines the elements of a sound data strategy, why it is important and how the firm needs to be organized for sustainable implementation
• Business Case/Funding Model: addresses the creation of the business case, its accompanying funding model and the importance of engaging senior executive stakeholders
• Data Management Program: identifies the organizational requirements needed to stand up a sustainable data management program
• Governance Management: defines the operating model and the importance of policies, procedures and standards as the mechanisms for alignment among stakeholders
• Data Architecture: focuses on the core concepts of “data as meaning” and how data is defined, described and related (data domains, metadata, critical data elements, taxonomies, common language/ontology)
• Data Quality: establishes the concept of fit-for-purpose data and defines the processes associated with establishing both data control and supply chain management
• Technology Architecture: addresses the relationship of data with the physical IT infrastructure needed for operational deployment (integration into operational environments)
• Data Operations: defines the data lifecycle process and how data content management is integrated into the overall organizational “ecosystem”


Each chapter begins with a short narrative describing what this component means, explaining why it is important and positioning it for organizational buy-in. These narratives are written in plain language and designed to demystify the subject area. Each component is organized into specific capabilities and objectives. They define the expected outcome and measurement criteria for implementation. Scoring is based in three core concepts: (1) degree of engagement (i.e. who is involved, at what level, with what responsibility/P&L authority), (2) formalization of practices (i.e. how documented, based on standard methodologies, aligned with budget cycles) and (3) level of activity (i.e. the evidence based output)

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