Why do you need a data governance maturity model? #
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Developing a data governance maturity model is an important step in establishing data governance. It defines a vision and creates a roadmap with clear milestones to achieve it.
Using a maturity model, organizations can identify their current stage in the data governance journey and prioritize next steps.
These models support progress toward overall organizational goals, such as:
- Protecting sensitive data
- Meeting regulatory requirements
- Ensuring high data quality
- Accelerating time-to-value for analytics
To achieve the above goals, the data governance maturity model helps guide improvements across key governance areas like:
- Privacy and security standards
- Roles for stewardship, access, and technical management
- Data lineage, retention, and documentation
- Supporting infrastructure
What are the six stages in a data governance maturity model? #
A data governance maturity model template outlines a series of stages or levels. This includes specific criteria for each stage of maturity.
Here is one example of stages outlined in a data governance maturity model template, based on Atlan’s guidelines for starting a data governance program:
- Unaware: Data governance is an unknown concept.
- Aware: The company now knows what data governance is, and leadership is aligned on the data governance vision.
- Small scale and foundations: One department is following the data governance program. Teams are aligned on tracking key metrics and can access a data catalog.
- Expand and adapt: As the program is deployed to more departments, data metrics are easily accessible through a dashboard, and data is clearly labeled and defined.
- Scale up and optimize: Data governance continues to grow and expand. The benefits of the program are apparent in time to value and key performance indicators.
- Governance mastery: The entire company uses the data governance program, with easy access to data assets that serve as a single source of truth.
The various stages of a data governance maturity model - Image by Atlan.
Gartner’s Inaugural Magic Quadrant for D&A Governance is Here #
In a post-ChatGPT world where AI is reshaping businesses, data governance has become a cornerstone of success. The inaugural report provides a detailed evaluation of top platforms and the key trends shaping data and AI governance.
Read the Magic Quadrant for D&A Governance
Examples of data governance maturity models #
The data governance maturity model above is one of many. Others, like those from Oracle, Stanford, IBM, and Gartner, as well as standards like CMMI and DAMA-DMBOK, can serve as useful tools.
Let’s look at two examples of data governance maturity models and explore the differences between various templates.
What is the Gartner data governance maturity model? #
The Gartner Data Governance Maturity Model is a five-level framework that helps organizations assess and improve their data governance practices over time. It evaluates how well an organization manages its data assets across dimensions like strategy, people, processes, and technology.
The five levels of the Gartner model are:
- Initial (Ad hoc): No formal governance; data practices are inconsistent and reactive.
- Repeatable (Developing): Some awareness and early efforts; processes begin to emerge but lack coordination.
- Defined (Established): Governance roles, policies, and processes are formalized and applied across key domains.
- Managed (Advanced): Governance is embedded into business processes with clear accountability and performance metrics.
- Optimized (Transformational): Governance is proactive and adaptive, driving business value and innovation through high data trust and alignment.
What is the CMMI data governance maturity model? #
The CMMI Data Governance Maturity Model is a framework derived from the Capability Maturity Model Integration (CMMI). Its five stages are:
- Performed (Chaotic): Data governance is unstructured and reactive. Processes are ad hoc, and success depends on individual efforts rather than repeatable systems.
- Managed (Repeatable): Basic governance policies and procedures exist. Some roles and responsibilities are defined, and prior successes can be repeated under similar circumstances.
- Defined: Governance processes are standardized, documented, and communicated across the organization. Training and tools support execution.
- Quantitatively Managed: Governance activities are measured and monitored. Metrics are used to manage data quality, compliance, and risk proactively.
- Optimizing: Continuous improvement is a core principle. Governance processes are refined based on feedback and performance data, often enabled by automation and advanced technologies.
None of these templates provide a one-size-fits-all, prescriptive model. Instead, each must be adapted to your organization’s unique needs.
To choose a data governance maturity model, consider your company’s industry, sector, and size. Also, think about your organization’s data governance objectives and goals.
After choosing a maturity model, modify the template to accommodate your company’s stage, technology stack, regulatory requirements, and existing processes.
What are the key considerations for a data governance maturity model? #
A maturity model is only useful when it reflects your organization’s real context and capabilities. Before adopting or adapting a model, keep these considerations in mind:
- Organizational goals: Ensure your model supports specific business outcomes, whether it’s regulatory compliance, improved analytics, or stronger data trust.
- Current state vs. target state: Be honest about where your organization stands today and define what success looks like over the next 1–3 years.
- Scope and complexity: Tailor the model to your environment. Global enterprises with complex data landscapes will need different milestones than smaller, regional teams.
- People and ownership: Assess who will lead and support governance efforts. A maturity model should account for roles, responsibilities, and team readiness.
- Tooling and metadata infrastructure: Without reliable metadata, governance remains reactive and fragmented. Evaluate whether your current data stack provides deep, accessible metadata, such as lineage, classifications, usage stats, etc.
- Metadata foundation to support AI and large language models (LLMs): As AI agents and LLMs increasingly interact with enterprise data, your model should account for metadata readiness, access control, and context delivery for safe, effective AI use.
- Change management readiness: Maturity requires behavior change. Evaluate your organization’s appetite for change and the resources needed to support it.
- Compliance landscape: Models should reflect evolving regulatory requirements, especially in sectors like finance, healthcare, and public services.
Key considerations for choosing the right data governance maturity model - Image by Atlan.
How to Build Your Data Governance Maturity Model in 5 Steps #
Follow these five steps to get started with your data governance maturity model:
- Assess your current state
- Use a structured questionnaire for assessment
- Align on a shared vision
- Choose and tailor your model
- Build a phased roadmap
Assess your current state #
Your first step should be to conduct a data governance maturity model assessment.
This assessment should include an audit of:
- Any existing data processes
- Infrastructure
- Documentation
- Tools
- Owners
Your assessment should have predefined criteria around:
- Data quality
- Security
- Lineage life cycle management
- Reliability
- Uptime
- Roles and responsibilities
Additionally, you should gather information from key stakeholders across the organization–everyone from data engineers to data consumers like product managers and data analysts.
Learning how data teams and consumers interact with data will tell you volumes about your data governance maturity.
Use a structured questionnaire for assessment #
Questionnaires are a useful tool when conducting a data governance maturity assessment. They provide a standardized way to gather data from stakeholders and establish a baseline for future evaluations. They also provide an easy way to identify gaps and areas for improvement.
All stakeholders should receive a questionnaire, which should ask questions about how and where they consume data, how much they trust the data, and so on. The questionnaire can be structured as open-ended questions, statements to be rated on a Likert scale, or a combination of question types.
Here are a few questions you might include:
- Do you have a formally defined data governance strategy?
- Does a process exist to address data quality issues?
- Do you have standardized data definitions and glossaries?
- Does a process exist to manage who can access which data assets?
- What data quality or security problems have you encountered?
Ask as many questions as you need to get a full picture of your company’s current data state.
Align on a shared vision #
Once your assessment is complete, bring stakeholders together to define a shared governance vision. Agree on:
- Target maturity level in the next 6–12 months
- Organizational goals that governance should support (e.g., compliance, analytics speed)
- Roles, metrics, and funding required to reach the next stage
Choose and tailor your model #
No model is plug-and-play. Success comes from tailoring the framework to your context.
So, select a data governance maturity model (e.g., Atlan, Gartner, CMMI, DAMA-DMBOK) that fits your industry, size, and regulatory context. Adapt the model to reflect:
- Your cloud and metadata tooling
- Existing governance processes
- Data domains and priorities
- Emerging needs like AI/LLM readiness
Build a phased roadmap #
With a baseline and vision in place, define a phased improvement plan:
- Prioritize quick wins (e.g., centralized glossary, access controls)
- Outline medium-term goals (e.g., lineage, contract enforcement)
- Track maturity milestones using KPIs tied to business outcomes
Embed governance into ongoing projects rather than as a standalone exercise. As you evolve, repeat the assessment every 6–12 months to track progress and realign.
Getting started with your data governance maturity model - Image by Atlan.
How do modern data teams improve their data governance maturity? #
Modern data teams evolve their data governance maturity by moving from manual, siloed practices to integrated, collaborative, and automated workflows.
As data ecosystems grow in complexity—with more tools, teams, and AI-driven use cases—governance must scale with them. This shift demands platforms that operationalize governance across discovery, access, quality, and trust.
That’s where Atlan comes in. As a unified metadata control plane, Atlan helps modern data teams centralize governance without slowing down innovation. It brings active metadata, embedded collaboration, and automated context-sharing into everyday workflows, enabling faster decisions, fewer risks, and stronger compliance at every maturity stage.
Real stories from real customers #
Implemented data governance strategy and roadmap using Atlan
“Our objective was to improve Aliaxis’ maturity around Data Governance, and we wanted to ensure that whatever tool we picked not only fit nicely with our tech stack, but that users didn’t feel intimidated, or felt that it was a huge organizational shift. Atlan’s user-friendliness seemed so simple and its capabilities gave us room to grow and mature as an organization without too many restrictions.”
Nestor Jarquin, Global Data & Analytics Lead
Aliaxis
🎧 Listen to podcast: Aliaxis’s global data journey with Atlan
Discover how a modern data governance platform drives real results
Book a Personalized Demo →
Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes
“Instead of spending 50 days manually identifying and then tagging personally identifiable information, Tide used Atlan Playbooks (rule-based bulk automations) to identify, tag, and then classify the data in a single, automated workflow.”
Michal Szymanski, Tide’s Data Governance Manager
Tide
🎧 Listen to podcast: How Tide automated GDPR compliance with Atlan
Implemented data governance processes using Atlan
“We worked with a consulting firm that helped map out a data maturity model journey. As part of that it was agreed that setting up a Data Governance program should be one of the first steps. We picked Atlan because of its UI. Within two clicks, people were exactly where they wanted to be.”
Kelsey Coffin, Senior Data Governance Manager
Commonwealth Financial Network
🎧 Listen to podcast: From spreadsheets to Commonwealth’s active metadata strategy
Ready to advance your data governance maturity? #
A data governance maturity model is a great way to assess your company’s data governance practices and chart a course for your data-driven journey. A data governance maturity model template can provide an objective benchmark to both see where your organization is now and what to work on next.
With a leading, metadata-driven unified control plane like Atlan, it’s easier than ever to deploy a best-in-class data governance program and improve your data governance maturity.
Discover how a modern data governance platform drives real results
Book a Personalized Demo →FAQs about data governance maturity model #
What is the data governance maturity model? #
The data governance maturity model is a framework that helps organizations assess their current data governance practices. It outlines various maturity levels, enabling companies to identify strengths and weaknesses in their data management strategies.
How can the data governance maturity model improve my organization’s data management? #
By using the data governance maturity model, organizations can evaluate their data capabilities, set clear goals for improvement, and implement best practices. This leads to enhanced data quality, compliance, and alignment with business objectives.
What are the key stages of the data governance maturity model? #
The key stages of the data governance maturity model typically include: Unaware, Aware, Small Scale and Foundations, Expand and Adapt, Scale Up and Optimize, and Governance Mastery.
Each stage represents a level of maturity in data governance practices.
How do I assess my organization’s current data governance maturity level? #
To assess your organization’s data governance maturity level, conduct an audit of existing data processes, infrastructure, and documentation. Use predefined criteria to evaluate aspects like data quality, security, and roles and responsibilities.
What best practices can I implement to advance my data governance maturity? #
Best practices to advance data governance maturity include establishing a clear data governance strategy, training employees on data best practices, implementing privacy and security standards, and regularly assessing data quality.
How do data governance maturity models support AI and LLM initiatives? #
Maturity models help assess whether your metadata, access controls, and governance processes are ready to support safe, accurate, and compliant use of AI and LLMs. As AI agents rely heavily on trustworthy metadata, a higher maturity level ensures better context delivery, reducing hallucinations and risks in AI outputs.
How often should I revisit or update my data governance maturity assessment? #
You should reassess your maturity every 6–12 months. Regular evaluations help track progress, adjust to changes in business priorities or tech stacks, and continuously improve governance practices as your data landscape evolves.