Key Takeaways
- Assessing data governance maturity is a valuable practice for ensuring a successful, sustainable data governance program.
- Commonly used data governance maturity model frameworks have distinct features that cater to different organizational needs.
- Data professionals can seek out a variety of training options to fill skills gaps found in data governance maturity models and assessments.
What Is Data Governance Maturity?
In the rapidly evolving landscape of data management, the concept of data governance has gained prominence as organizations strive to manage, protect, and leverage their data effectively. While developing and implementing a data governance program, it is important to consider how to measure the organization’s current state of data management and develop a plan for regular improvement. This process involves assessing data management and data governance maturity.
Data governance maturity frameworks help organizations assess their data governance capabilities and guide their evolution toward optimal data management. To implement a data governance or data management maturity framework (a “model”) it is important to learn what data governance maturity is, explore how and why it should be assessed, discover various maturity models and their features, and understand the common challenges associated with using maturity models.
Data governance maturity refers to the level of sophistication and effectiveness with which an organization manages its data governance processes. It encompasses the extent to which an organization has implemented, institutionalized, and optimized its data governance practices. A mature data governance framework ensures that the organization can support its business objectives with accurate, trusted, and accessible data.
Maturity in data governance is typically assessed through various models that measure different aspects of data management such as data quality and compliance and examine processes for managing data’s context (metadata) and its security. Maturity models provide a structured way to evaluate where an organization stands and how it can improve for a given function. Assessing the current state of data governance maturity can support development and revisions to an organization’s data governance policies.
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The Role of a Data Governance Maturity Model
Each data governance maturity model has distinct features that cater to different organizational needs:
- Assessment Criteria: Models define specific criteria and metrics for evaluating data governance practices. These criteria typically cover aspects such as data governance policies, data quality, data security, metadata management, and compliance.
- Stage Descriptions: Maturity models outline various stages or levels of maturity, providing a roadmap for organizations to follow as they improve their data governance practices.
- Best Practices: Models often include data governance best practices and recommendations for advancing through maturity stages. These practices guide organizations in implementing effective data governance processes.
- Benchmarking: Many models offer benchmarking capabilities, allowing organizations to compare their maturity levels with industry standards or peers.
- Continuous Improvement: Mature models emphasize the importance of continuous improvement and adaptation to changing business needs and regulatory requirements.
Why You Should Perform a Data Governance Maturity Assessment
Assessing data governance maturity is crucial for several reasons:
- Benchmarking and Improvement: By evaluating maturity, organizations can benchmark their current state of data governance and data management against industry standards and best practices. This helps identify strengths, gaps, and areas for improvement. Most commonly used data governance frameworks offer a variety of data governance metrics to help organizations evaluate the assessment’s results.
- Strategic Alignment: Understanding maturity levels can support the organization’s plans for improving their data governance practices so they align with organizational goals and regulatory requirements, facilitating better decision-making and confidence with data used for compliance and regulatory reporting.
- Resource Allocation: The results of an assessment can help to prioritize resources and investments in data governance and related data management functions. Organizations can allocate resources more effectively, identify areas where training would be useful, and support investment in areas to provide sustained improvements and benefits.
- Risk Management: Mature data governance practices mitigate risks related to data breaches, inaccuracies, and compliance failures. Regular assessments help in identifying and addressing potential vulnerabilities and offer improvement goals based on best practices and data management industry standards.
- Performance Measurement: Maturity assessments provide an opportunity to develop and refine metrics and benchmarks that an organization can use to measure the performance of their data governance and related data management initiatives over time.
Comparing Popular Maturity Models
Several data governance maturity models provide frameworks for assessing and improving data governance and related data management practices.
| Framework | Source/Publisher | Levels | Focus |
|---|---|---|---|
| DAMA DMBoK | DAMA International | 5 | * Covers various aspects of data management and governance in a comprehensive framework * Emphasizes the importance of establishing data management processes, data quality, and data stewardship |
| Gartner Data Governance Maturity Model | Gartner, Inc. | 5 | * Focuses on key aspects such as data ownership, quality, and compliance * Highlights the need to progress from reactive, fragmented practices to a proactive, integrated approach to governance |
| IBM Data Governance Maturity Model | IBM, Inc. | 4 | * Emphasizes the need for clear data policies, standardized processes, and metrics to measure governance effectiveness * Useful for organizations looking to implement structured governance practices and achieve consistency across data management functions |
| CMMI Data Management Maturity Model | CMMI Institute | 5 | * Integrates data management practices with overall process improvement * Focuses on continuous improvement and integration of data management (and data governance) processes with organizational goals |
| Data Management Capability Assessment Model | EDM Council | 6 | * Provides a structured framework across the data lifecycle – from establishing data management to applying analytics * Data governance is not the primary focus, but governance concepts and processes are integrated |
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Step by Step: How to Conduct a Maturity Assessment
Assessing data governance maturity involves several steps, and effective assessments include all of them:
Identify a Data Governance Assessment Team
Identify a small group that will be responsible for all aspects of the data governance maturity assessment process. The team should include some data governance and data management senior professionals and at least one person with appropriate experience in assessment and evaluation using maturity models. This requirement may lead to using an external resource, which is often considered a best practice, offering objective viewpoints as well as assessment expertise.
Define Objectives
Determine what the organization wants to achieve with the assessment and its plans for data governance. Objectives may include enhancing the current data governance practices, improving data quality, enhancing compliance with more transparent data access and usability, or streamlining data management processes such as creating organizationally accepted data definitions or formalizing business participation in data operations. Often, the assessment is part of the organization’s development of a data strategy.
Select a Maturity Model
Choose an appropriate maturity model based on the organization’s specific needs and goals. Different models focus on various aspects of data governance. Some models are focused on data governance only, while others include related data management functions in their frameworks.
Gather Data
The team should collect information through surveys, interviews, and documentation reviews. Engaging stakeholders across the organization to get a comprehensive view of current practices is essential, but the number of stakeholders selected should be balanced (neither too few nor too many).
Evaluate Current State
Compare the collected data against the maturity model’s criteria to assess the current state of data governance and related data management functions such as metadata management and data security.
Identify Gaps and Opportunities
Analyze the assessment results to identify gaps in data governance practices, identify related data management functions that contribute to the strengths and gaps, and determine opportunities for improvement.
Develop a Roadmap
Create an action plan that outlines steps to sustain strengths, address gaps, and enhance the organization’s data governance maturity. This plan should include realistic timelines, responsibilities, and resource requirements. Most maturity experts recommend using a phased approach for the roadmap, to reduce the effects of many, sometimes competing, improvement opportunities.
Develop Reports and Presentations
Write one or more reports to identified audiences (e.g., leadership, stakeholders, relevant departments/teams, etc.). Each report should outline the assessment’s processes and clearly state the results and plans for achieving the assessment’s objectives. Deliver appropriate presentations that summarize the reports and communicate next steps to all stakeholders.
Implement Changes
Execute the roadmap’s action plans, monitor progress, and adjust as needed to ensure that improvements are effectively implemented. The assessment team may lead some of the implementation activities or different teams may execute plans as appropriate for the organization.
Review and Reassess
Regularly review and reassess data governance and data management maturity to ensure continuous improvement and alignment with organizational objectives. Most organizations that conduct data governance maturity assessments rely on six-month evaluation intervals for the first two years, and then annually thereafter.
Data Governance Maturity Scorecard
After following the steps above for selecting and using a data governance maturity model, the data governance team must evaluate and interpret the results. Many organizations adopt a structured approach that includes a clear framework and practical processes for analysis and application. One effective tool is a data governance maturity scorecard, which translates qualitative data about the various maturity components into actionable insights.
The scorecard framework is based on which maturity model is used (e.g., DAMA, Gartner, IBM, etc.). Each domain or capability, such as data quality, data governance policies, data stewardship, alignment with IT, etc., is scored on a consistent scale, typically from 1 (ad hoc) to 5 (optimized). The assessment data is collected through surveys, interviews, and documentation reviews. Experienced data governance team members assess the collected data and identify a score for that capability against the maturity model’s benchmarks. Those results are consolidated into a matrix or dashboard that visualizes maturity across key areas.
Once the scorecard is developed, using it effectively requires three main processes:
- Analysis: Review scores across domains to identify strengths, weaknesses, and inconsistencies. Look for patterns, such as higher maturity in technical capabilities but lower maturity in organizational culture or alignment with other data management functions (e.g., data quality, data/information security, etc.). Encourage education and training for lower maturity areas and functions with targeted programs such as the Data Governance Program Scope and Maturity Assessment course and the Applied Data Governance Certification.
- Prioritization: Use the results to prioritize focus areas based on business risk, regulatory needs, or strategic importance. Domains with low scores and high business impact should be addressed first. Some “quick wins” should be the initial focus, and more strategic efforts should be integrated into the data governance roadmap through phased activities.
- Action Planning: Create a roadmap that includes short-term and long-term actions for maturing governance practices. Assign ownership, define success metrics, and set timelines. Communicate all results and the roadmap regularly to all stakeholders to support transparency and promoting the business value and benefits of data governance. Effective scorecards are updated periodically, usually quarterly, using this process.
Challenges of Using a Maturity Model
While data governance maturity models provide valuable frameworks for assessing and improving governance practices, they come with challenges:
Model Complexity
Many maturity models are complex and may require significant time and resources to implement. Organizations need to ensure they have the capacity to effectively handle the complexity involved in using these models. Additionally, some data governance maturity models do not address the relevant related data management functions, such as metadata management, data quality management, or data security to a sufficient level of detail for some organizations. Using any of the standard data governance maturity models requires the organization adapt some content or approach to support the organization’s culture.
Subjectivity
Assessing maturity can be subjective, as it often relies on self-assessments and qualitative data. Organizations need to ensure that assessments are as objective as practical, and are based on accurate information. This is one reason that best practices recommend the use of an assessment expert as part of any assessment team.
Dynamic Nature of Data
Data governance practices and requirements evolve over time. Maturity models may need frequent updates to remain relevant and effective in addressing emerging data governance challenges and technologies.
Cultural Resistance
Implementing changes based on maturity model assessments can face resistance; organizational culture may not accept the views discovered in an assessment. Adopting and sustaining effective change management strategies and choosing a maturity model carefully can help overcome resistance and ensure successful implementation.
Integration with Existing Processes
Integrating maturity model recommendations with existing data management processes can be challenging. Organizations need to align new practices with current workflows and systems. Most data management experts recommend the use of a phased approach with integrated teams for all implementation initiatives.
Resource Constraints
Implementing and advancing through maturity stages may require significant resources, including time, money, and expertise. Organizations must carefully plan and allocate resources to achieve desired outcomes. Developing a phased and detailed data strategy roadmap can help manage resources accordingly.
Industry-Specific Maturity Considerations
Developing data governance maturity can require tailoring strategies to the unique regulatory, operational, and risk profiles of some industries.
- Banking and Insurance: In insurance and banking, regulatory compliance (e.g., Solvency II, Basel III, BCBS 239), customer data privacy, and risk data aggregation are primary drivers, necessitating strong controls, well-trained data stewards, data lineage tracking, and documented auditability.
- Healthcare: Healthcare organizations must align data governance with HIPAA, interoperability mandates, and clinical data integrity, requiring rigorous data stewardship across provider organizations, and controlled access management to protect data and other resources.
- Utilities: Utilities face challenges with integrating legacy systems, managing real-time operational data, as well as regulatory reporting that emphasizes data standardization and reliability supported by enterprise data governance processes.
- Government: Government agencies prioritize transparency, data sharing, and public accountability, which requires robust metadata management, privacy protection for data and processes, and inter-agency collaboration especially by data stewards.
- Energy: The energy sector, especially oil and gas, must manage complex asset data from a variety of sources, and evaluating sensor data across global operations requires strong data governance to manage data often from separate systems. In energy and many other industries, data governance maturity depends on aligning technical and operational data standards to support safety, compliance, and performance analytics.
Core Skills and Training to Climb the Maturity Ladder
| Roles | Focus | Challenges | Recommended Training |
|---|---|---|---|
| Chief Data Officer | Strategic alignment of data governance with business value, and return on investment, regulatory oversight, and executive reporting | Chief data officers and other leadership roles often have insufficient exposure to good training focused on this level | Data Governance Learning Plan Data Governance Best Practices Organizational Change Management to Ensure Governance Adoption |
| Data Steward | Data quality, metadata management, stewardship workflows, and operational implementation; knowledge of data quality concepts | Many data stewards are “thrown into the role” and are not given opportunities to learn the concepts and how to apply them properly; lack of continuing training | Data Stewardship Learning Plan Data Governance Skills, Activities, Tools, and Data Ethics Best Practices for Data Definitions and a Business Glossary |
| Data Architect | Designing systems that support governed data lifecycles, data integration, and security-by-design principles that include data as well as process | Data architects may lack understanding of how data governance and related data management functions affect and are affected by data architecture | Data Governance and Data Architecture The Tools of Data Governance and Stewardship |
| Data Analyst / Business Intelligence (BI) Analyst | Trustworthy data sourcing, metadata usage, and alignment with governance policies; non-tool-specific knowledge in fundamentals of BI and data analysis | Many analysts lack training in data management functions, especially in data governance and data quality | Role of Data Governance in Other Data Disciplines, Drivers, and Goals What Is Data Quality? Introduction to Data Visualization |
| Data Manager | Data governance operations, team coordination, and alignment with organizational objectives | Often focused on physical implementation aspects; must learn more about how governance and related functions affect data management roles | What Is Data Governance? Data Governance and Its Relationship to Other Data Management Activities Data Lineage Fundamentals Learning Plan |
| Data Governance Lead | Program development and leadership, stakeholder engagement, communication, and metrics, the “face of data governance” for the organization | May lack access to best practices-based data governance program development training or connections with other data governance professionals | Data Governance Specialist Learning Plan Applied Data Governance Certification Program Implementing Data Stewardship and Training Data Stewards |
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