Affiliated with:

Data Governance Assessments

Evaluating the current state of data governance practices is an excellent way to learn about the organization’s approach to managing data as an asset, and to improve data governance performance.

Automated, On-Demand Data Governance Assessment

Organizations considering a new approach to data governance frequently wonder about the current state of their data governance efforts.  The enterprise may have started a data governance program at an enterprise level, it may have started several business-unit efforts, it may have started independent data stewardship programs with no data governance.  As a result, management does not know where to start, what should be repaired, what should be left intact, and how to align the changes and continuing tasks into a cohesive practice.  In this confused state, the organization may embark on less productive and sometimes conflicting paths, making data governance improvement a synonym for “failed initiative.”

Audits and Assessments

In the financial and accounting disciplines, audits are seen as a way to assess the current state of financial health and the solidity of the accounting practices used in the organization.  An assessment of the organization’s data governance efforts and practices is considered a proper first start in developing a valid representation of the data governance current practices and areas of strength and weakness.

The most general definition of an audit is an evaluation of a person, organization, system, process, project or product.  Audits are performed to ascertain the validity and reliability of information, and provide an assessment of a system’s internal controls.  The goal of an audit is to express an opinion on the system under evaluation based on work done in the past.

An assessment is the process of documenting, usually in measurable terms, knowledge, skills, attitudes and beliefs about a topic.  Since assessments can be objective or subjective, the optimal assessment combines attributes of an audit while maintaining the assessment qualities of documentation and impressions.

An assessment can help an organization measure its growth and maturity in a particular area of data management or prepare the organization for a new effort.  Many companies request a data management assessment or data governance assessment to determine:

  • readiness for developing a data warehouse or other business intelligence initiative
  • state of business and technology capabilities for metadata management
  • current state of enterprise data / information management capabilities, including data governance and stewardship
  • readiness for embarking on a technical or organizational change involving information management
  • current state and desired direction for infrastructure or enterprise architecture

Rather than fear the assessment process or dismiss its possibilities, organizations should embrace the concept of an independent assessment of their data governance practices.  A good assessment would include a review of the implementation of data governance and enterprise data management concepts and techniques.  This enables companies to learn what is working, what is not working, the reasons, and how to improve their data governance efforts.

Data Governance Assessment Steps

Each assessment should be tailored to the organization’s needs, but all data governance assessments should include the following points:

  1. Review existing key documentation concerning current data management practices, focusing on data governance / data stewardship and metadata management. Some assessments will include examination of data quality practices, since data governance is involved with data quality management.
  2. Identify all relevant stakeholders, business and technical, from all levels. Conduct interviews with stakeholders
  3. Assess current business needs for data governance and data stewardship, metadata management (and data quality if included). These requirements should include regulatory or compliance requirements as well as preferred business goals
  4. Assess current state of data governance practices against an industry standard data governance maturity model
  5. Create current state assessment document, using objective results
  6. Using desired requirements for data governance, data stewardship, metadata management (and data quality if included) develop future state recommendations, including gap analysis between current state and future state
  7. Recommend an overall strategy for delivering the desired state for data governance / data stewardship / metadata management, including project scope, overall methodology, based on best practices and industry standards.
  8. Develop phased implementation plan for each area
  9. Present results, begin detailed project planning for initial phase – frequently focused on implementation of new or improved data governance program

Data Governance Assessment Challenges

Performing any type of assessment has a variety of challenges, some common and some unique to the organization or to the type of assessment.  Experts from a variety of sources including the CMM Institute have listed the following issues encountered in developing and performing any type of assessment:

  • Internally-directed and performed assessments are extremely difficult; best results are obtained when external expert resources are used to develop and perform an assessment (industry knowledge, framework knowledge, objectivity, complete focus on assessment, etc.)
  • Lack of an industry framework for the domain to be assessed; without a clear understanding of the entire domain, scope discussions will continue to haunt the assessment for the duration. In data governance, most experts will rely on vendor-neutral enterprise data management framework such as EWSolutions EIM Framework
  • Lack of an industry standard maturity model for evaluating current state of each domain component and overall domain maturity, and for projecting target state desired maturity with competencies outlined. In enterprise data management, most experts will rely on the Data Management Maturity Model from CMM Institute or a vendor-neutral enterprise data management maturity model such as EWSolutions EIM Maturity model
  • Lack of metrics to measure activity, improvement, progress, etc.. Without metrics and the measurements performed regularly, no assessment is worthy of its name or the time and effort invested in it.
  • Lack of sustained attention to continuous improvement in data governance and its companion components of data stewardship and metadata management. If the organization has not made a conscious, determined and sustainable commitment to improve these areas continually, then performing the assessment will deliver no permanent results.


An assessment of the current state of data governance for an organization will provide many benefits to any organization.  These benefits include: an objective review of the current state of data governance based on best practices and industry standards where applicable, development of business goals for governing and managing data according to approved policies and standards, and refining the approach to data stewardship and the management of metadata.


Dr. David P. Marco, LinkedIn Top BI Voice, IDMMA Data Mgt. Professional of the Year, Fellow IIM, CBIP, CDP

Dr. David P. Marco, PhD, Fellow IIM, CBIP, CDP is best known as the world’s foremost authority on data governance and metadata management, he is an internationally recognized expert in the fields of CDO, data management, data literacy, and advanced analytics. He has earned many industry honors, including Crain’s Chicago Business “Top 40 Under 40”, named by DePaul University as one of their “Top 14 Alumni Under 40”, and he is a Professional Fellow in the Institute of Information Management. In 2022, CDO Magazine named Dr. Marco one of the Top Data Consultants in North America and IDMMA named him their Data Management Professional of the Year. In 2023 he earned LinkedIn’s Top BI Voice. Dr. Marco won the prestigious BIG Innovation award in 2024. David Marco is the author of the widely acclaimed two top-selling books in metadata management history, “Universal Meta Data Models” and “Building and Managing the Meta Data Repository” (available in multiple languages). In addition, he is a co- author of numerous books and published hundreds of articles, some of which are translated into Mandarin, Russian, Portuguese, and others. He has taught at the University of Chicago and DePaul University.

© Since 1997 to the present – Enterprise Warehousing Solutions, Inc. (EWSolutions). All Rights Reserved

Subscribe To DMU

Be the first to hear about articles, tips, and opportunities for improving your data management career.