Businesses have to make calculated decisions to stay competitive — but it isn't possible without the right data. Data governance can help manage and secure an organization's data assets, which are the lifeblood of valuable insights.
As data volume and complexity grow, organizations face challenges in maintaining data quality and complying with regulations. A shift towards agile data governance helps overcome these issues by increasing collaboration across business units and democratizing data access between teams.
Is your data governance model is mature enough to handle complex data management? In this explainer, we'll discuss the stages of data governance maturity to help you assess your current maturity model.
What is a data governance maturity model?
A data governance maturity model is a structure that helps organizations assess their data governance practices and improve them over time. It provides a step-by-step approach to determine how well your organization manages and uses data.
Here are some of its key benefits:
Helps organizations unify data practices so that data is accurate and consistent across all departments.
Emphasize stronger security measures and better compliance with data protection regulations (like GDPR or HIPAA) to minimize the risks of data breaches.
Promotes data democratization and allows teams with different levels of technical knowledge to access and use data effectively.
Simplifies data management tasks to save time and resources by automating workflows and improving data stewardship.
Provides high-quality and well-governed data to make reliable data-driven decisions.
Key components of data governance maturity models
A data governance maturity model is made with a couple of components that define how each stage will be tackled. The common dimensions that are evaluated in a maturity model include the following:
People & organization
A strong framework for data governance starts with clearly defined roles and responsibilities in the organization. This involves designating governance leaders and data owners to maintain accountability for data security and quality.
Processes & procedures
Standardized data management procedures include processes for handling data-related tasks such as integration and performing quality checks that provide consistent and readily available data. These procedures predict problems with data management through well-established workflows to help data teams find solutions before a problem becomes a bottleneck in the whole data management lifecycle.
Tools & technology
Technology automates data management throughout its lifecycle and imposes strict governance policies. It helps build an infrastructure for metadata management and data quality monitoring. That’s why it’s important to have tools like data catalogs and metadata management systems in your tech stack.
Policies & standards
Standards and policies are the governing bodies of any framework for data governance. They ensure consistency in the data flowing throughout the company. This covers data security and retention guidelines and adherence to industry rules like the CCPA and GDPR. You can use data governance tools to enforce both organizational-specific and law-based policies across data management systems.
Metrics & measurements
A governance program cannot be successful if its effectiveness is not measured. Metrics are used to measure this effectiveness. They give valuable information on data quality and the progress of governance initiatives, using KPIs such as data accuracy rates and incident reaction times.
These components are like chain nodes that hold the whole data management system together. However, you should have a comprehensive strategy to ensure that these nodes work correctly.
Concentrating on one element while ignoring the others creates gaps that compromise data security and integrity. That’s why you should adopt a holistic data management approach that coordinates all aspects and satisfies changing business requirements.
Stages of data governance maturity
A data governance maturity model works in five stages. Let’s understand each:
Initial stage
Initially, data governance is unstructured and informal, and no standardized regulations or processes are in place. At this level, governance practices are more reactive than proactive which means data management is uneven. That’s why organizations may face the following challenges:
Lack of accountability
Uncertain data ownership
Poor data quality
Data silos
Lack of standardized data management strategies
Once an organization recognizes all these inefficiencies and the need for uniform practices, it starts formalizing data governance.
Managed stage
In the next stage, companies develop basic data management processes and define responsibilities such as data protectors or owners. However, these initiatives are limited to specific departments and governance practices are divided with minimal standardization throughout the firm.
Some standards for governing data access and quality exist but they are not usually implemented, so here are some challenges that may arise:
Compartmentalized governance
Inflexible processes
Limited team collaboration
Little use of technology for automation and uniformity
Once you are over the managed state, you move on to the next step, where you can resolve the above challenges.
Defined stage
In this stage, data governance is defined and standardized throughout an organization. Clear roles and duties are defined, with specific governance teams or committees in charge of compliance and data management processes.
Governance efforts are incorporated with larger company goals, and data becomes a strategic asset. However, businesses may still lack advanced measures for improving governance performance and because of this, they can face the following challenges:
Compliance across departments
Controlling data quality consistently
Growing governance as the firm grows
More work is required to incorporate governance tools
When the established governance practices work well, organizations can employ metrics and analytics to achieve governance effectiveness.
Quantitatively managed stage
At the quantitatively managed stage, organizations use metrics and analytics to measure and optimize their data governance processes. They conduct regular data quality assessments using defined KPIs and use these insights to improve governance processes.
In addition, they systematically track data issues and analyze the cost-benefit of data initiatives while maintaining integrated data quality metrics across business processes. However, they may come across the following challenges as well:
Maintaining consistency in measurement across departments
Balancing metrics complexity with practical utility
Getting stakeholder buy-in for metric-based decisions
Resource intensity of continuous measurement
Keeping metrics aligned with evolving business needs
That’s why, first, they solve these challenges so that they can focus on optimization and predictive governance.
Optimized stage
This is the last stage, where data governance is fully incorporated into the organization's culture. Organizations use predictive analytics to identify and avoid possible data governance concerns. This ensures everything continuously improves and complies with regulatory requirements in data management.
However, since this is a very critical stage, you may come across the following challenges, too:
Maintaining alignment between governance principles
Rapid technological improvements
Legislative changes
How to assess your organization’s data governance maturity
To understand where your organization stands, start with a thorough evaluation. Gather key stakeholders from different departments — including IT, legal, operations, and business units — because each department interacts with data uniquely. These team members will help paint a complete picture of how data is currently being handled across your organization.
You can structure your assessment using evaluation tools like maturity models and scorecards. To make things more simple, here is a step-by-step guide to assess your organization's maturity level:
Collect data: Gather details on the data governance procedures that are in place now.
Conduct interviews and surveys: Next, speak with important stakeholders through surveys or interviews. The objectives are to find pain spots and learn how data is handled across departments.
Analyze findings: Check the information acquired and stakeholder input to know the maturity stage of your company.
Check opportunities and gaps: After you've checked the data, note any areas where governance procedures or practices fall short.
Make an action plan: Based on the assessment results, create an action plan to progress your organization to the maturity model.
Best practices for advancing through the maturity model
As your organization moves through the data governance maturity model, you must use targeted agile governance strategies through each stage. Here are some best practices to follow during each phase:
Starting stage (stage 1 and 2)
Document your current data assets and owners.
Create basic data quality standards.
Start tracking data usage patterns.
Establish a simple approval process for data access.
Building momentum (stage 3)
Implement automated data quality checks.
Create clear data ownership roles.
Set up a data catalog to track metadata.
Develop standardized data documentation.
Advanced stage (stage 4 and 5)
Automate governance workflows.
Link business outcomes to data quality metrics.
Enable self-service data access with proper controls.
Build feedback loops between data users and owners.
Key to sustainable growth
To ensure that data governance practices remain effective as your organization grows, make sure to prioritize scalability and adaptability within your organization. In addition to this, create flexible governance frameworks that can evolve alongside data maturity. This approach will make it easier to meet new challenges and find opportunities at every stage of this journey.
To take your organization’s growth to the next level, you can even leverage advanced data platforms and tools like data.world’s data catalog — they will support your organization as it progresses in data maturity.
Learn how data.world accelerated innovation in Snowflake with its agile data governance approach.
Challenges and pitfalls in data governance maturity
A maturity governance model is tricky to implement, especially for organizations with distributed data sources. Here are some common challenges that you may come across in this process with steps to overcome them:
Resistance to change
Organizational resistance to new governance processes is a big barrier. People think that data governance is unimportant, which means a common cause of resistance is a lack of awareness of its benefits.
To overcome this issue, explain the advantages of governance, such as better data security and quality — this will help create a pro-data culture within your organization. Train your teams from the beginning to ensure they appreciate the value of structured data management.
Lack of resources
A data governance maturity model requires huge investment in tools and training. Many companies struggle to get the budget and resources required to implement governance initiatives.
To tackle this issue, start with small and scalable projects that show the value of governance. At the beginning, prioritize important areas where governance can produce immediate outcomes, such as regulatory compliance or data quality enhancements.
Once you have the budget, invest in automation tools to integrate and manage data assets across systems while enabling centralization.
Uncertain leadership and roles
Data governance attempts usually fail without strong leadership and well-defined roles. If no one is held accountable for data governance, it can become fragmented and result in uneven procedures.
To fix this, set clear leadership roles — for example hire a Chief Data Officer (CDO) or establish a governance committee. Next, identify data owners' and stewards' roles and assign their responsibilities so everyone on the team knows what KPIs they have to achieve.
Navigating the maturity model with data.world
Data governance maturity models help organizations assess and improve their data governance procedures for optimal data management. They track progress so teams can set clear goals in each phase. However, effective data governance is a continuous process that requires regular reassessment and adjustments to meet long-term needs.
To support this journey, data.world offers an all-in-one ecosystem with tools for collaboration, data cataloging, data lineage, and governance. These features simplify data management with an agile governance approach, all within a flexible framework that grows with your organization.
With its data mesh approach, data.world unites people and processes to support progress through each maturity stage.
Interested in high-quality mature data governance models? Schedule a demo today and explore how our platform can help your organization excel at every stage.
