Data Professional Introspective: Capability Maturity Model Comparison

For the Enterprise Data Management Council (EDMC), I recently concluded a detailed comparison and mapping of the DMM to the DCAM with the Council’s Product Manager, employing the Soladatus modeling tool with its robust model mapping features.

As you might expect, it revealed both structural differences, strengths, and gaps in both models. In this column, I’m going to summarize key aspects of comparison between the two models and offer suggestions about transitioning from the DMM to the DCAM.

At this writing, I’m about to teach my first DCAM Course and also engaged in leading my first DCAM Assessment, after a decade of conducting DMM Assessments to evaluate enterprise data management programs and teaching DMM courses. My next column will feature lessons learned from these experiences and some practical assessment tips for you.

Status of the DMM

As you may know, ISACA / CMMI Institute discontinued support for the Data Management Maturity (DMM) Model and the corresponding online certification course in January 2022. ISACA included some selected portions of the content in the latest version of their CMMI Model. However, the primary audience for CMMI is, and has always been, software development and engineering, and the audience for the DMM, by design, emphasized the business units and the enterprise data management program. Both because it does not contain the complete DMM content and it is not targeting the overall data management program, the CMMI Model is therefore less useful for evaluating the program or serving as a development guide.

For those who were (or are) using the DMM as their reference model framework, whether to conduct assessments or to guide development of a robust data management program, the good news is that it has a lifetime license. If you have it, you can continue to use it. While it has not been updated recently, its fundamental practices are still applicable to every organization. In addition, certifications you may have earned are still recognized, though new ones are not being issued.

Since the DMM has officially been retired, many organizations are turning to the only remaining comprehensive independently developed data management reference model, the Data Management Assessment Capability Model (DCAM), published by the EDMC. The DCAM is well supported by basic training and certifications, Authorized Partners, and a community of practice. With original roots in the financial industry, its reach has expanded into many other industries and countries.

Regarding their basic content, the models are quite similar, which is unsurprising, because:

  1. The data management industry has a long history and has developed many near-universal best practices over four decades and
  2. The creation journey for both models started with a unified content model, jointly developed over a period of 15 months, with the Software Engineering institute, the EDMC, and corporate sponsors.

There are no major conceptual divergences between the models when the topics addressed are the same.

With respect to its existence as a reference model product, the DCAM has undergone several updates, and more are planned. It is a living model, its influence will continue to grow, and it will continue to be supported.

Structural Differences

 The DMM’s top level structure is six Categories, which contain 25 Process Areas corresponding to key data management topics. The Process Areas contain appraisable Functional Practices organized into five levels. Each practice statement can be scored Not Met, Partially Met, or Fully Met, and the final score is aggregated to Process Areas.

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The DCAM’s top level structure is eight Components, which contain Capabilities. The Capabilities, in turn, contain Sub-Capabilities, which can be scored from 1 (Not Initiated) to 6 (Enhanced), and the scores can be aggregated to the Capability level.

Basically, the DCAM’s Sub-Capabilities and the DMM’s Functional Practices are requirements which are scored, allowing an organization to receive a detailed snapshot of the current state of data management. We’ll be using the terms ‘requirements’ or ‘practices’ throughout.

DMM’s Structural Approach

The DMM’s structure is based on a synthesized view of how most organizations typically proceed in building their data management programs, therefore Levels 1-3 are not only specific to the requirements within the level, but also take into account the current scope of implementation.

Level 1 – Performed practices are not very demanding, and each can be met by one (or more) Projects, on the assumption that if you’ve met the requirement anywhere in the organization, you can perform it elsewhere, similar to lifting a five-pound weight.

Level 2 – Managed practices are more advanced, but must be performed across a broader vertical or horizontal scope. A vertical scope would apply to all major data stores within a business line, and a horizontal scope would apply either to a shared repository used by multiple business lines, such as a data lake or data warehouse, or to a major application used by multiple business lines, for example, a loan tracking system, an inmate management system, or a product lifecycle system. This reflects a typical advancement path, where one business line or one shared repository takes the lead, then the practices and work products are available and generalizable for broader adoption.

Level 3 – Defined requirements reflect the scope of ‘enterprise data,’ that is, all of the critical data management activities that are required for an active, effective data management program. If an organization scored a 3.0 or better in all of the DMM’s Process Areas, it would be ‘Green, Good, Go,’ and the organization could be confident that it is doing a great job in managing its data.

Level 4 – Measured introduces quantitative measurements and statistics used to refine and perfect the requirements implemented at the enterprise level.

Level 5 – Optimized requires that the requirements are fully embedded into the operational processes of the organization and continuously improved.

DCAM’s Structural Approach

Most Sub-Capability statements in the DCAM can be considered as roughly equivalent to DMM Level 3 requirements, i.e., what the organization should have implemented if it is doing a good job (i.e., Green, Good, Go).

Instead of presuming an implementation scope for a group of requirements and scoring (DMM Process Area Levels), the DCAM takes a lifecycle (or ‘phased’) view of each stated requirement.

DCAM Score Level Sub-Capability Score Description
1 – Not Initiated Data management activities are ad hoc, and likely to vary project to project, documentation slim to non-existent
2 – Conceptual The organization has considered and is thinking about or planning an effort to establish or improve a capability
3 – Developmental There is a project or effort funded and underway, with IDed stakeholders, developing policies, processes, and standards
4 – DefinedActive governance participation, approved policies, processes, and standards. Data is defined and prioritized, funding is sustained, compliance program is defined
5 – AchievedMandated by executive management, full business engagement, adherence audited
6 – EnhancedFully embedded across the organization, continuous improvement in capabilities

This progression implies organizational scope, but since the DCAM is intended to be scalable from the enterprise to various segments of an organization, such as regions and business lines, it is not prescribed.

A quick comparative summary of differences in scoring:

  • DCAM Score Level 1 is not equivalent to the DMM Level 1, since the organization may score a 1 in the DCAM if the practice has been unaddressed, versus stated project-level requirements in the DMM.
  • DCAM Score Level 2 – same as above.
  • DCAM Score Level 3 – is roughly equivalent to a score of “Partially Met” for a practice in the DMM.
  • DCAM Score Level 4 is equivalent to DMM Level 3, minus the DMM’s built-in mandatory application to the overall scope of enterprise data.
  • DCAM Score Levels 5 and 6, combined, are roughly equivalent to DMM Level 5.
  • While DCAM does specify metrics and measures within the context of some Sub-Capabilities, for the most part it doesn’t render these activities as scorable requirements as DMM Level 4 – Measured does for each Process Area.

That’s somewhat confusing, right? I’d say that an underlying model design choice for each framework led to the differences. The DMM aims to encourage organizations to build and expand their program through following a scope-based yardstick; the DCAM encourages organizations by assigning the Sub-Capability score on a lifecycle yardstick.

I would say that from an Assessment point of view, the DCAM is likely to produce lower scores for smaller organizations, which may lack staff resources or formal data management programs, whereas the DMM would, in effect, give them more credit for meeting requirements at Level 2, Program-level achievements. That said, I’ll find out soon and will share my thoughts.

Content Mapping

The following diagram was produced by Element-22, an organization which has been involved with both models from the earliest days.

The DMM is on the left, the DCAM on the right. Items in orange illustrate content gaps from one model to the other. The orange lines from the DMM to the DCAM summarize content that is presented in the DCAM, but not in the DMM, and vice versa. When we were engaged in the Functional Practice to Sub-Capability mapping, we analyzed the textual descriptions as well as the suggested work products for both models to reach our conclusions (i.e., map, doesn’t map).

Overall, there are five DMM Process Areas that do not exist in the DCAM, though they may be alluded to in a few Sub-Capability statements or textual descriptions:

  • Provider Management
  • Data Integration
  • Process Management
  • Process Quality Assurance
  • Configuration Management
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And there are Capabilities in the DCAM that are not in the DMM, other than allusions in DMM textual descriptions:

  • Business Architecture
  • Technology Architecture
  • Data Control Environment
  • Analytics Management

The DMM has Process Areas addressing requirements for Business Glossary, Metadata Management, Data Requirements Definition, and Data Lifecycle Management. The DCAM does have some Sub-Capabilities that directly address these requirements, but some are in the context of other Capabilities or referenced in textual descriptions, rather than being presented as major areas of focus. This reflects the DMM’s greater emphasis on business /governance activities and the DCAM’s overall emphasis on the broader Data Management Program.

We analyzed DMM practices in Levels 2-5 for the detailed mapping exercise, since with respect to DMM Levels, the DCAM ‘flattens’ them in favor of the phased approach to scoring. Due to that fact, we often had to look at practices in multiple DMM Process Areas and multiple DCAM components, yielding a large number of M:1 and 1:M results.

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Can I Translate DMM Scores to the DCAM?

This is a screen shot from Soladatus, showing a mapping view of the DMM’s Data Quality Category Process Areas to multiple Sub-Capabilities in multiple Components of the DCAM. The lines depict the requirements-level commonality. We established 795 ‘transitions’ (mapping links) between the models.

You can visually determine that it is not really possible to create an ‘apples to apples’ comparison of scoreable data management requirements.

The answer to that question is NO.

There is no easy way to translate an organization’s assessment scores against the DMM to DCAM Sub-Capability scores, at least with any expectation of precision. EDMC members will be able to access the detailed mapping contained in Soladatus, and Soladatus may make it available if a license is purchased.

The chart below highlights some observations about comparison between the two models.

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Conclusions – the Road So Far

The DCAM is, as mentioned, a comprehensive, useful model that is well supported and will be updated periodically to reflect improvements. Bottom line: Your organization should adopt it.

As to the transition between models, if you have a need to develop a detailed comparison and the time to get into the weeds, I recommend starting with the work products (artifacts) mentioned in both models. I say this because for the most part, they concur – Policies, Processes, Standards. (There aren’t too many options for passing through the needle’s eye, after all).

In preparing for a comprehensive assessment of your data management program using the DCAM, the most important consideration is the organizational and/or data domain scope. The DCAM’s flexibility and less hierarchical structure allows you to easily tailor the application of the model to a selected segment of the organization, with the corresponding scoring.

For instance, if your organization has many geographical regions, you might want to concentrate on one major shared data domain, for example, master data for Products or Customers. You could also concentrate on a business line, such as Marketing, or even on a single critical application.

If you swing for the fences and assess the overall data management program across the entire enterprise, prepare to discover that there are activities and work products you haven’t yet addressed. In other words, don’t be surprised if your initial scores are lower than you thought. The DCAM is an excellent and comprehensive source of best practices, so post-assessment, use it as a guide for what your organization needs to work on.

In my next column, I’ll share lessons learned from teaching the DCAM Course and completing the DCAM Assessment currently underway.

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Melanie Mecca

Melanie Mecca

Melanie Mecca, CEO of DataWise Inc., an Enterprise Data Management Council Partner and Authorized Instructor for DCAM certification courses, is the world’s most experienced evaluator of enterprise data management programs. Her expertise in evaluation, design, and implementation of data management programs has empowered clients in all industries to accelerate their success. As ISACA/CMMI Institute’s Director of Data Management, she was managing author of the DMM and has led 35+ Assessments, resulting in customized roadmaps and rapid capability implementation. DataWise provides instructor-led courses leading to DCAM certification, as well as for data stewardship and the proven Assessment method that she developed. DataWise offers a suite of eLearning courses for organizations aimed at elevating the data culture and providing practical skills to a large number of staff. Broad stakeholder education is the key to data management excellence! Visit to learn more.

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