Data Governance Maturity Model: A Roadmap to Optimizing Your Data Initiatives and Driving Business Value

Updated September 28th, 2024

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Data governance maturity is a cornerstone of successful, data-driven companies. How mature is your company’s data governance? A data governance maturity model can help you answer that question.

In this article, we review what a data governance maturity model is, its value, some standard templates, and how your organization can best use one.
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Table of contents #

  1. What is a data governance maturity model?
  2. Data governance maturity model: Templates and examples
  3. Data governance maturity model: Assessment
  4. Takeaways
  5. Related reads

What is a data governance maturity model? #

A data governance maturity model is a tool that helps organizations assess the current state of their data governance program. Companies use them to evaluate their data capabilities, train employees on data best practices, and benchmark their data governance programs against those of similar organizations.

Data governance maturity models are especially useful when starting a data governance program. An important step in establishing data governance is defining a vision and creating a roadmap with clear milestones to achieve it.

With a data governance maturity model, you can determine where your company is on the way to reaching your data governance goals, which can include

Your company can reach these goals by creating or improving a data governance program that implements

  • Privacy and security standards
  • Governance for data stewardship, technical management, and access
  • Lineage and retention
  • Infrastructure
  • Documentation

Using a data governance maturity model: templates and examples #

A data governance maturity model template outlines a series of stages or levels. The template includes specific criteria for each stage of maturity. With a template, your company can determine your data governance level and create a plan with objective milestones to work toward mastery.

Here is one example of levels outlined in a data governance maturity model template, based on Atlan’s guidelines for starting a data governance program:

  1. Unaware: Data governance is an unknown concept.
  2. Aware: The company now knows what data governance is, and leadership is aligned on the data governance vision.
  3. 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.
  4. 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.
  5. 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.
  6. Governance mastery: The entire company uses the data governance program, with easy access to data assets that serve as a single source of truth.

data governance maturity model template

data governance maturity model template - Image by Atlan.

Other data governance maturity model templates #


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.

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.

None of these templates provide a one-size-fits-all, prescriptive model. Instead, each must be adapted to your organization’s unique needs. After choosing a maturity model, modify the template to accommodate your company’s stage, technology stack, regulatory requirements, and existing processes.

Getting started with a data governance maturity model: assessment #

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

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.

Conducting a data governance maturity model assessment with a questionnaire #


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:

  1. Do you have a formally defined data governance strategy?
  2. Does a process exist to address data quality issues?
  3. Do you have standardized data definitions and glossaries?
  4. Does a process exist to manage who can access which data assets?
  5. 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.

Takeaways #

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, third-gen data catalog like Atlan, it’s easier than ever to deploy a best-in-class data governance program.

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