Gartner Data Governance Maturity Model: What It Is, How It Works

Updated September 28th, 2024

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A study conducted by Fivetran and Wakefield in 2021 revealed that 85% of enterprises report making poor data management decisions — and losing revenue as a result.

How faulty data management decision making happens — varies from organization to organization, but nearly always comes back to the same reason: weak data governance practices. Improving data governance requires two things: knowing where you’re at right now, and what exactly needs to change.

Gartner has spent decades advising organizations of every kind on their data governance policies, assessing what works and what does not. They turned this expertise into an information management maturity model that any company can use to assess and improve their own practices.
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What is Gartner’s data governance maturity model, exactly? More importantly, how can you use it to evaluate your own company’s current practices — and then use Atlan to help you move the ball forward?

Table of contents #

  1. What is a data governance maturity model?
  2. Gartner data governance maturity levels
  3. Moving up the Gartner data governance maturity model with Atlan
  4. Conclusion
  5. Related reads

What is a data governance maturity model? #

Gartner includes data governance maturity as part of its overall Enterprise Information Management Maturity Model. The model focuses on seven key building blocks for an enterprise information management strategy:

  • Vision
  • Strategy
  • Metrics
  • Information governance
  • Organization and roles
  • Information life cycle
  • Enabling infrastructure

The maturity model itself places organizations on one of five levels:

Level 1 - Aware (<10% of Gartner’s clients) #


At this level, organizations know what their issues and challenges are. They are not, however, currently in a position to enact meaningful change due to reasons like lack of budget or lack of leadership support.

Level 2 - Reactive (Around 30%) #


Reactive organizations aren’t making proactive, planned improvements. Instead, they respond to various fire drills — such as a data breach or a high-profile data quality incident — to correct and strengthen their data governance policies. While better than doing nothing, a reactive approach can still result in lost revenue and lost customer confidence.

Level 3 - Proactive (40%) #


At this stage, an organization is intentionally spearheading at least some initiatives to actively improve its approach to data governance. Typically, though, this effort is not coordinated effectively across various programs and divisions within the org.

Level 4 - Managed (15%) #


Organizations rise to level 4 when they’re managing information across more than two enterprise information management initiatives and coordinating activities across the enterprise.

Level 5 - Optimized (less than 5%) #


A level 5 organization uses top talent and tools to treat information as an enterprise-wide asset. These rare companies have high-functioning organization structures and are generally at the cutting edge of technology.

Gartner data governance maturity levels #

Like Atlan, Gartner has long emphasized that organizations can’t take a one-size-fits-all approach to successful data governance. Successful modern data governance requires:

  • Having multiple styles of governance and being sensitive to context
  • Encouraging innovation
  • Creating a flexible, dynamic strategy that spans the entire enterprise
  • Implementing distributed, formal, and informal decision rights
  • Being actively aware of both the opportunities and risks

What does successful data governance look like in different stages of organizational maturity? Gartner diagnoses the problems, and likely status, that occur at each level of its Enterprise Information Management Maturity model:

Level 1: Aware #


At this stage, organizations are struggling with data quality. They are likely doing the bare minimum required to remain in regulatory compliance.

This doesn’t mean there are no data quality efforts at this stage. Rather, such efforts are usually ad hoc, not well-managed, and not supported by the larger organization.

Generally, the result is a low level of data quality and data trust, which is then reflected in low data usage numbers. Companies at this stage may not even have the data — such as data quality metrics — to assess this objectively.

Level 2: Reactive #


In the Reactive stage, there are some guidelines in place for managing data quality, data classification, security, and other aspects of data governance. These however exist within individual data silos. Individuals and teams feel free to break these guidelines when it suits their needs. There is generally little systematic support to ensure upstream data quality (i.e., fixing data quality issues and data errors at their root in data sources).

Level 3: Proactive #


A proactive company has tasked information owners and data stewards with maintaining and monitoring data quality and adherence to data governance policies.

This is also the stage at which organizations begin successfully employing metadata — i.e., information about the data that enables data consumers to understand its origins, usage, quality, and purpose.

The key difference between level 3 and level 4 organizations is that, at this stage, these efforts are focused on subsets of organizational data. This is where companies have identified and begun tackling their highest-priority data governance issues, targeting them at high-value data sets. At this stage, some larger, cross-team initiatives may begin to emerge.

Level 4: Managed #


This is where a company has an enterprise-wide data governance program that has sway with every aspect of the business. Policies have evolved into principles and procedures that are well-documented, clearly communicated, and mostly automated. Improvements are driven not by technical needs and concerns, but by business objectives.

Level 5: Optimized #


According to Gartner, an Optimized organization has a fully automated information asset management system that catalogs all data (including partner data) that matters to the organization. At this stage, creating high-quality, governed, and secured data is embedded into everyone’s daily workflow processes. Data stewards are able to focus more on finding opportunities to enrich and drive additional value from the company’s data.

Moving up the Gartner data governance maturity model with Atlan #

Looking at Gartner’s data governance maturity model, we see a few themes emerge as companies progress between levels of maturity:

  • Clear data governance policies that everyone understands
  • Automation and active management of data quality and data governance
  • Easy-to-use tooling that enables data self-service and personalization and makes data governance a part of everyone’s day-to-day workflows
  • Cutting-edge technology that helps organizations push the edges of automation and extract more value from their data

Atlan provides a range of features and capabilities that help companies climb the data governance maturity ladder. Using Atlan, you can move from a passive, reactive, and manual approach to an active, always-on, automated approach that eliminates data silos and yields high-quality data products.

With Atlan, you can:

Drive self-service data operations. Democratize data by publishing reusable, trusted data products that data consumers from across your entire organization can easily find and use. Use personas and purposes to deliver personalized data experiences, accelerating time-to-value for new data initiatives.

Embed data governance automation. Use Atlan AI to document your data estate via AI-enriched descriptions. Automate critical data governance with features such as playbooks, data contracts, and tag management to save both time and money.

Generate and enforce data policies. Use Atlan UI to drive policy creation from natural language AI prompts, adding exceptions for important use cases as needed. Generate alerts for potential policy incidents so you can address them instantly, as they occur — instead of discovering and responding to them months (or even years) down the road.

Manage approvals easily. Use Atlan’s visual tools to build a new, no-code approval processing workflow in minutes — no engineering support required. Deploy changes to your processes and manage approvals in the span of days instead of weeks.

Govern at any level of complexity. Use Atlan’s open API architecture to build in support for in-house systems, cutting-edge AI models, and more.

Conclusion #

Improving data governance requires establishing new processes, utilizing the right tools, and incorporating governance as a part of your organizational culture. This doesn’t happen overnight. It’s an ongoing process that requires identifying and addressing challenges in an iterative manner.

Gartner’s data governance maturity model provides a framework for identifying where your company is right now, and where it needs to go next. You can use Gartner’s model to move your organization away from reactive firefighting and into a future where data governance is an active component of your organization’s worklife — and where everyone in the company can access, understand, and use data to drive decisions.

Learn more about how Atlan can push your data governance initiatives forward - contact us for a demo today.

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