Comparison of Data Management Maturity Assessments (DMMA)

In this writeup, we evaluate some of the most commonly used models to assess Data Management maturity. We outline each model's history, practical uses, benefits, and drawbacks.

At the end, you'll be able to make a more informed decision about which is the right framework for your needs. Jump to the Conclusion to see a comparison of all seven models discussed in the article.

Contents

• About Data Management Maturity Assessments
  • What is a Data Management Maturity Model?
  • How do I choose the best Data Management Maturity Model for my organization?
  • What are the key steps involved in implementing a Data Management Maturity Model?
• Rationale for Conducting a DMMA
• History
• Comparison
  • IBM's Data Governance Council Maturity Model
  • Gartner's Enterprise Information Management Maturity Model
  • Stanford's Data Governance Maturity Model
  • EDM Council's Data Management Capability Assessment Model (DCAM)
  • CMMI Institute's Data Management Maturity (DMM) model
  • Data Crossroads' "Orange" Data Management Framework (DMF)
  • ZS' Data Maturity Compass (DMC)
• Other Data Maturity Models
• Common Elements of DMMA Models
• Conclusion

About Data Management Maturity Assessments

A Data Management Maturity Assessment (DMMA) is a structured evaluation designed to help you assess your organization's data practices across multiple areas. Conducting a DMMA helps measure the current maturity level of specific organizational data practices, identify strengths and weaknesses in Data Management capabilities, and prioritize steps to reach higher maturity levels. Many DMMAs include benchmarks to compare your organization's maturity against. These comparisons can help you understand if you're investing enough in your data capabilities to stay competitive.

A DMMA is a great first step if you're looking to enhance data quality, ensure regulatory compliance, and mitigate legal risks related to data, or align data strategies with business objectives. It provides a roadmap for continuous improvement and better utilization of data as an asset.

What is a Data Management Maturity Model?

A Data Management Maturity Model is a framework or set of frameworks for evaluating the maturity level of an organization's data-related capabilities. Improvements may be identified through internal assessment in addition to benchmarking against competitors. A DMMA can serve as a yardstick for measuring capability development over time, evaluating progress against specific objectives, or understanding gaps in best practices.

In short, a DMMA helps an organization to better understand its strengths, weaknesses, and opportunities in the domain of Data Management.

How do I choose the best Data Management Maturity Model for my organization?

Selecting the right model starts with understanding your organization's specific Data Management needs, industry requirements, organizational goals, and available resources. Next, evaluate each model's degree of alignment with your organization's objectives, scalability, adaptability, and ease of implementation to estimate how effectively they could address your specific Data Management challenges.

What are the key steps involved in implementing a Data Management Maturity Model?

Implementing a Data Management Maturity Model typically involves numerous steps. First, assess organizational needs, then select a suitable model, engage stakeholders, and plan for implementation. Next, pilot the model in a specific data domain and evaluate the results before rolling out to the broader organization. Some models can be implemented internally with external support being available while other models may require external support for implementation. As you and your team conduct the assessment, it's important to adapt the chosen model based on your organization's needs.

Rationale for Conducting a DMMA

A DMMA can be an effective tool for identifying and planning improvements to an organization's Data Management capabilities. Here are some specific benefits of conducting a DMMA.

• Finding the gaps:  an organization can understand its existing data practices and compare these against established benchmarks to identify areas of strength and areas for improvement
• Avoiding problems:  identify and stop possible issues from getting worse or becoming ingrained
• Working smarter:  an organization that's good at managing data operates more efficiently and reliably
• Alignment with objectives:  a DMMA can help an organization to align its Data Strategy and workforce with its business objectives
• Getting better over time:  regular assessments help an organization to monitor and track its Data Management maturity over time
• Improved data quality:  a DMMA can inform new Data Quality standards and processes across the data lifecycle
• Reduced data risk:  a DMMA can identify and mitigate Data Security and Data Privacy risks such as vulnerability to data breaches
• Enhanced data-driven decision-making:  helps organizations to make their data more accessible, understandable, and trustworthy; this can improve data-driven decision-making, increase innovation, and foster competitive advantages

History

In 1986, the Capability Maturity Model (CMM) was developed as the first capability maturity assessment by the Software Engineering Institute at Carnegie Mellon University. It was originally used by the US Department of Defense to evaluate the capabilities of software development teams. The five maturity levels assessment structure, complete with defined process areas, was only formalized for wider use in 1991.

New models were later derived from the CMM. The Capability Maturity Model Integration (CMMI) was developed by the CMMI Institute in 2002 as an integrated solution to the CMM's inefficient need to use multiple models in software development processes. Meanwhile, IBM's 2007 Data Governance Council Maturity Model represented the first Data Management Maturity Model to be developed. It uses a similar maturity levels structure to the CMM but specifically measures Data Governance competencies at each level.

Many additional models have since been developed for various Data Management contexts. One prominent model is the Data Management Maturity (DMM) model. Created in 2014 also by the CMMI Institute, the DMM remains widely used. On a scale of six levels of maturity, the model provided assessment criteria across the following processes: data management strategy, data governance, data quality, platform and architecture, data operations, and supporting processes. Within each process, the model also identifies sub-processes that should be evaluated.

As an increasing amount of data is produced annually, the need for DMMAs also increases. We've evaluated a selection of the most prominent models to help you select the right one for your organization.

Comparison

Data Governance Council Maturity Model

IBM

The IBM Data Governance Council Maturity Model was published in 2007 based on insights from a consortium of 55 organizations. This collaborative endeavor involved council members identifying best practices for organizations to evaluate their Data Governance capabilities.

The model measures maturity on a scale of five levels of maturity. It features 11 Data Governance categories composed of many subcategories. These categories and subcategories can be individually assessed for their current maturity level, for which concrete improvements are proposed. Assessing domains individually allows for the DMMA to be better tailored to the specific needs of a given organization.

Best suited for:  an organization looking to establish, evaluate, or refine their Data Governance office; the model helps assess current practices and design effective programs aligned with industry standards

Benefits:

• Builds on best practices contributed by real world data leaders
• Each of the 11 separate data domains identified by the model can be individually assessed, contributing to the ability to prioritize the assessment based on immediate business need
• Clear criteria for each of the five maturity levels

Drawbacks:

• Limited support for and guidance on how to implement the model
• Establishes near-unobtainable standards for Level 4 (Quantitatively Managed) and Level 5 (Optimizing)

Enterprise Information Management Maturity Model

Gartner

Gartner's Enterprise Information Management Maturity Model was first introduced in 2008 as a tool for organizations to evaluate current Enterprise Information Management (EIM). It was significantly updated in 2016 to address advances in information management requirements, technology skills, and the range of data sources.

Whereas the 2008 model provided six maturity levels, the 2016 model consists of five maturity levels for each of seven information management building blocks. These building blocks include vision, strategy, metrics, information governance, organization and roles, information life cycle, and enabling infrastructure. Indicators are outlined for each maturity level of each building block.

Best suited for:  a Gartner customer interested in thoroughly assessing their maturity level across different information management building blocks

Benefits:

• Uses a straightforward five-level scale to measure maturity
• Provides clear action items to achieve improvements
• Backed by research-based best practices and support

Drawbacks:

• Proprietary tool that requires a Gartner subscription, starting at $30,000 per year
• Effectiveness relies on continued subscription and Gartner's periodic updates and revisions

Data Governance Maturity Model

Stanford University

Stanford's Data Governance Maturity Model was produced in 2011 for internal use as part of their Data Governance Program. It assesses the interactions between six Data Governance components and three dimensions that further subdivide each component.

The six Data Governance components are evenly split between foundational components and project components.

• Foundational components:  awareness, formalization, and metadata; these components measure core Data Governance competencies and program resources
• Project components:  stewardship, data quality, and master data; these components measure how effectively Data Governance concepts are applied across data-related projects

Three dimensions subdivide these six components — people, policy, and capabilities. For example, there are people, policy, and capabilities aspects for the foundational component of awareness. The interactions between the six components and three subdividing dimensions highlight specific aspects of component maturation. Component maturation is assessed over five maturity levels. Assessments are made using a table of qualitative and quantitative metrics specific to each aspect of component maturation for each maturity level.

For example, for the interaction between awareness (a foundational component) and people (a subdividing dimension) at a maturity level of 2 includes the following metrics:

• Qualitative:  executives are aware of existence of program; little knowledge of program outside upper management
• Quantitative:  training sessions * attendees as its quantitative metric

Assessments for each aspect of component maturation are then arranged on separate scorecards as 3x3 tables, one for foundational components and one for project components.

Benefits:

• Practical and project-oriented
• Adaptable to different organizational contexts, customizable to meet specific needs

Drawbacks:

• May not be easily scalable for various types of organizations or industries; it might not cater well to specific niche sectors or unique structures, requiring substantial customization
• May not encompass all aspects of modern Data Governance, especially those related to advanced analytics, AI/ML, or newer data technologies, which are continuously evolving

Data Management Capability Assessment Model

Enterprise Data Management Council

The Data Management Capability Assessment Model (DCAM) was first launched in 2014 by EDM Council. The organization was founded by C-level executives from financial institutions with the goal of improving data handling practices in a neutral forum. Today, EDM Council serves data practitioners of all industries through their publications and thought leadership.

DCAM emerged as a framework for evaluating governance, quality, and architecture within data functions. The framework helps organizations to identify Data Management areas needing improvement.

The DCAM framework is available for download and use exclusively by member firms of EDM Council for their in-house data management programs. DCAM Authorized Partners are also entitled to use DCAM in their client assessments and engagements.

Best suited for:  an organization in the finance industry or another heavily regulated field, particularly one that may prefer support in assessment and implementation

Benefits:

• Rigorous evaluation criteria
• Benchmark Data Management practices against industry standards
• Simplifies regulatory compliance such as with the EU's Global Data Protection Regulation (GDPR)
• Potential support from DCAM-certified consultants

Drawbacks:

• Potential lack of adaptability to non-financial institutions
• May not be appropriate for assessing the capabilities of smaller organizations
• Access requires becoming or partnering with a member of EDM Council

Data Management Maturity (DMM) model

Capability Maturity Model Integration Institute

In 2014, the CMMI Institute introduced the Data Management Maturity (DMM) model, designed to evaluate and enhance how organizations handle their data. Developed in collaboration with industry experts and refined through iterative releases, this framework provides a structured approach, delineating maturity levels and domains for assessing Data Management practices. Its subsequent versions have evolved with technological advancements, gaining traction across various industries.

Although ongoing support for the DMM was discontinued in January 2022, the DMM is still widely used today. It enables businesses to fortify Data Governance, adopt effective practices, and navigate complex data challenges.

Best suited for:  an organization seeking to understand alternative methodologies to compare fundamental principles of different approaches

Benefits:

• Based on how organizations typically build their Data Management program
• Scores are based on the scope of the organization, so this model scales well for small organizations
• Focused on creating effective, repeatable processes from assessments

Drawbacks:

• No longer supported by CMMI Institute

The "Orange" Data Management Framework (DMF)

Data Crossroads

The "Orange" Data Management Framework (DMF) is a Data Management Maturity model developed by Data Crossroads in 2019. It is a combination of models, methods, and templates whose design has been informed by assessments of other common models such as DCAM to improve Data Management practices such as DMMAs.

Best suited for:  organizations seeking to implement Data Management functions from scratch or develop a new Data Management sub-capability

Benefits:

• Scoping and planning a data management initiative
• Defining a Data Management strategy and designing capabilities
• Methodology for documenting data lineage and developing a knowledge graph of data assets

Drawbacks:

• The model's broad scope may make capability assessment inefficient

Data Maturity Compass

ZS

The Data Maturity Compass (DMC) is a largely automated DMMA system that uses Generative AI, standard benchmarks, and best practices to streamline the assessment process within organizations. It can be used either with the support of a ZS consultant or independently with a self-managed option and may be tailored to the particular needs of the organization. In January 2024, a Master Data Management (MDM) module was added also.

The DMC consists of three modules:

• Input:  surveys are administered to individuals across the organization to best inform the current state of Data Management practices
• Analysis:  real-time assessments of current data maturity are made on 12 core capabilities — inspired by the DAMA wheel from the DMBOK — including Data Governance and Data Quality. The assessment criteria for each organizational capability include the following five maturity dimensions:  Strategy, People/Talent, Processes, Technology, and Adoption. The assessment results are then used to automatically generate a tailored roadmap for improvement.
• Insights:  makes the analytics interactive and easily navigable

Best suited for:  an organization ready for an automated approach to Data Management Maturity Assessment

Benefits:

• Offers tailored recommendations in the form of strategic profiles
• Logical roadmap for improvement is auto-generated in real time
• Automated end-to-end process using cloud-native infrastructure that efficiently connects individual system components and automates key processes, reducing costs

Drawbacks:

• Given the recent development of the model, there are limited customer reviews and a lack of proven effectiveness
• Reliance on complex automations could generate unexpected results and difficult to identify errors

Common Elements of DMMA Models

Existing models will only meet the needs of some organizations. Understanding the common elements of DMMAs can support data practitioners to design their own assessments.

The list below specifies some common practices and associated tools:

• Identify and categorize data domains:  define key data capabilities; e.g., the 11 data categories of IBM's Data Governance Council Maturity Model
• Define maturity levels and assessment metrics:  use rubrics to describe what each capability involves at different levels of maturity, how capabilities interact with each other, and how to measure a capability's maturity using specific metrics; e.g., the capability and maturity level definitions of CMMI's Data Management Model
• Collect data:  collect data on baseline performance; e.g., running surveys and questionnaires about employees' current data practices for ZS' Data Maturity Compass
• Assess maturity and present results:  use assessment metrics to make quantitative assessments of each capability's maturity level; e.g., use of scorecards within Stanford's Data Governance Maturity Model to featuring assessments for each aspect of component maturity or radar charts as suggested in the DMBOK
• Contextualize results:  compare results to internal or external benchmarks to understand relative maturity; e.g., evaluate the maturity of an organization's programs against industry peers using EDM Council's DCAM
• Plan and communicate next steps:  outline the time-based actions for the organization to take in order to improve on its DMMA results; e.g., automatically generated roadmap from ZS' Data Maturity Compass

Other Data Maturity Models

In the list below, we've included our findings related to the broader space of data maturity models:

• OvalEdge Data Governance Maturity Model from OvalEdge
• Data Governance Maturity Assessment from Informatica
• Data Maturity Framework from University of Chicago, Center for Data Science & Public Policy
• Data Maturity Model from SafeGraph
• Data Maturity Framework from Data Orchard
• Data Maturity Model from The Big Story
• DataFlux Maturity Model from DataFlux
• Data Management Maturity Model from TDWI
• Capability Maturity Model Integration from Information Systems Audit and Control Association
• Advanced Analytics Capability Maturity Model from Analytics Center of Excellence
• Information Governance Implementation Model from ARMA
• Principles Maturity Model from ARMA
• Data Interoperability Maturity Model from National Archives of Australia
• Asset Management Maturity Model from Āpōpō
• Digital Preservation Capability Maturity Model from Charles Dollar and Lori Ashley
• Capability Maturity Model for Research Data Management from Syracuse University
• Digital Preservation Maturity Model from Preservica
• Records Management Maturity Model from JISC
• Digital Asset Management (DAM) Maturity Model from The DAM Playbook
• Information Governance Maturity Model from E-ARK
• Snowplow Data Maturity Model from Snowplow
• Anmut’s Data Maturity Assessment from Anmut
• Dell Data Maturity Model from Dell

Conclusion

Choosing the best model depends on organizational needs, resources, and specific objectives. Organizations should assess individual requirements and align them with the strengths and limitations of each model before making a final decision.

After comparing different ways to manage data effectively, the next step is to put those ideas into action within your organization. Here are straightforward steps to make the most of these methods:

• Assess needs:  figure out what your organization needs for managing data better
• Select model(s):  choose the method that best fits your organization's needs
• Plan implementation:  make a plan to put the chosen method into practice
• Involve everyone:  get all relevant stakeholders on board and thoroughly communicate the plan
• Try and improve:  test and seek feedback
• Keep an eye on progress:  continuously monitor and make adjustments
• Get help if needed:  seek expert advice
• Keep learning and improving:  always be ready to improve based on changes and new things happening in the industry

Other Data Maturity Models

In the list below, we've included our findings related to the broader space of data maturity models:

• Data Maturity Framework from University of Chicago, Center for Data Science & Public Policy
• Data Maturity Model from SafeGraph
• Data Maturity Framework from Data Orchard
• Data Maturity Model from The Big Story
• DataFlux Maturity Model from DataFlux
• Data Management Maturity Model from TDWI
• Capability Maturity Model Integration from Information Systems Audit and Control Association
• Advanced Analytics Capability Maturity Model from Analytics Center of Excellence
• OvalEdge Data Governance Maturity Model from OvalEdge
• Information Governance Implementation Model from ARMA
• Principles Maturity Model from ARMA
• Data Interoperability Maturity Model from National Archives of Australia
• Asset Management Maturity Model from Āpōpō
• Digital Preservation Capability Maturity Model from Charles Dollar and Lori Ashley
• Capability Maturity Model for Research Data Management from Syracuse University
• Digital Preservation Maturity Model from Preservica
• Records Management Maturity Model from JISC
• Digital Asset Management (DAM) Maturity Model from The DAM Playbook
• Information Governance Maturity Model from E-ARK
• Snowplow Data Maturity Model from Snowplow
• Anmut’s Data Maturity Assessment from Anmut
• Dell Data Maturity Model from Dell
• Data Maturity Assessment for Government framework from the UK Government

Conclusion

Choosing the best model depends on organizational needs, resources, and specific objectives. Organizations should assess individual requirements and align them with the strengths and limitations of each model before making a final decision.

After comparing different ways to manage data effectively, the next step is to put those ideas into action within your organization. Here are straightforward steps to make the most of these methods:

• Assess needs:  figure out what your organization needs for managing data better
• Select model(s):  choose the method that best fits your organization's needs
• Plan implementation:  make a plan to put the chosen method into practice
• Involve everyone:  get all relevant stakeholders on board and thoroughly communicate the plan
• Try and improve:  test and seek feedback
• Keep an eye on progress:  continuously monitor and make adjustments
• Get help if needed:  seek expert advice
• Keep learning and improving:  always be ready to improve based on changes and new things happening in the industry

Finally, for your organization to benefit from conducting a DMMA, it needs to be prepared to change based on the assessment's results. As we discuss in our Data Management Master Class chapter on Change Management, people across the organization need to embrace change for change to happen.

But overcoming resistance to change can be difficult. Fear of the unknown, having workflows disrupted, and job security can make many hesitant to embrace change. Drive the improvements to your organization's data practices by finding a compelling “why” that convinces key people of the DMMA's value and working with people to address their fears.