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How to Implement Data Governance: A 10-Step Guide for 2025

Updated December 20th, 2024

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Data governance is like a playbook for managing data. It tells us how to use it, access it, and keep it safe. By following these rules and implementing a data governance plan, we can make sure our data is reliable and secure.

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As organizations increasingly rely on data for decision-making, data governance has become a critical practice to ensure data accuracy, consistency, and security. Data breaches, privacy concerns, and regulatory requirements have also heightened the importance of data governance in maintaining compliance and mitigating risks.

So, let’s go through the steps of implementing a robust data governance plan that’s beneficial to your organization.

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

Table of contents #

  1. 10 steps in a data governance plan
  2. Data governance plan tooling: 5 essential factors to consider
  3. Key steps to consider while making a data governance implementation plan
  4. How organizations making the most out of their data using Atlan
  5. Bringing it all together
  6. FAQs about how to implement data governance
  7. How to implement data governance: Related reads

10 steps in a data governance plan #

Implementing a data governance plan in your organization can be crucial in achieving growth and industry leadership. So, let’s take a look at a step-by-step guide on how to implement data governance:

  1. Define goals and objectives
  2. Obtain executive support
  3. Establish a Data Governance Council
  4. Define data governance roles and responsibilities
  5. Develop a data governance framework
  6. Implement data governance tools
  7. Develop a data governance training program
  8. Monitor and measure data governance effectiveness
  9. Foster a data-driven culture
  10. Continuously improve your data governance program

Now, let’s understand each one of them in detail.

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1 Define goals and objectives #


Clearly outline the goals and objectives of your data governance plan. Make sure to focus on how it can position your organization as a leader in shaping data models and leveraging data for monetization within the industry.

2 Obtain executive support #


Gain buy-in from senior leadership to ensure the success of your data governance initiative. Also, present a well-prepared business case outlining the benefits and ROI of implementing data governance.

3 Establish a Data Governance Council #


You’ll need to form a cross-functional team with representatives from various departments, including IT, legal, finance, and operations. This team will be responsible for establishing policies, standards, and processes related to data management.

4 Define data governance roles and responsibilities #


Then, clearly outline the roles and responsibilities of data stewards, data owners, and data custodians within your organization. Ensure that each team member understands their role and is accountable for data management activities.

5 Develop a data governance framework #


Once you’re done outlining the roles and responsibilities, you need to develop a data governance framework. It should include policies, procedures, standards, and guidelines for data management. This framework should also account for data quality, data privacy and security, data lineage, metadata management, and data sharing.

6 Implement data governance tools #


The next step in your data governance plan is to invest in data governance tools and technologies that will automate and streamline data management processes. These tools can include data cataloging, data lineage tracking, data quality monitoring, and data privacy enforcement solutions.

7 Develop a data governance training program #


Provide ongoing training and education to your team members on data governance best practices, tools, and technologies. This will ensure that they understand the importance of data governance and are equipped to execute their roles effectively.

8 Monitor and measure data governance effectiveness #


In this step, you need to track the effectiveness of your data governance program by establishing key performance indicators (KPIs). You should regularly review these metrics and adjust your approach as needed.

9 Foster a data-driven culture #


It’s time to encourage a data-driven culture across your organization by promoting the value of data, sharing data insights, and rewarding employees who contribute to data governance initiatives.

10 Continuously improve your data governance program #


As your organization grows and evolves, so should your data governance program. That’s why you need to continuously evaluate and refine your processes, policies, and technologies to keep pace with industry best practices and the changing needs of your business.

By following these steps, you can implement a robust data governance program that will help your organization monetize data while ensuring compliance and maintaining the highest standards of data quality.

Data governance plan tooling: 5 essential factors to consider #

The above steps give you a roadmap for your data governance plan. Now, let’s look at how data governance tools come into play.

Here are the five essential factors to consider before choosing a data governance tool for your organization:

  1. Functionality
  2. Scalability
  3. Ease of use
  4. Customization and flexibility
  5. Vendor support and community

Let’s take a closer look at each factor.

  1. Functionality: Assess whether the tool provides the necessary features and capabilities to support your organization’s data governance objectives, such as data cataloging, data lineage tracking, data quality management, and data privacy enforcement.
  2. Scalability: Evaluate whether the tool can scale to accommodate your organization’s growth and handle increasing data volumes and complexity.
  3. Ease of use: Choose a tool that’s user-friendly and intuitive, allowing your team members to quickly learn and adopt the platform.
  4. Customization and flexibility: Determine whether you can customize the tool to fit your organization’s unique data governance needs and processes.
  5. Vendor support and community: Consider the level of support provided by the vendor, including implementation assistance, documentation, training resources, and technical support. Additionally, assess the size and activity of the tool’s user community, as this can be a valuable source of knowledge and best practices.

By carefully considering these prerequisites and evaluation criteria, you can select the best-in-class data governance tool that aligns with your organization’s needs and helps you effectively implement your data governance plan.

Key steps to consider while making a data governance implementation plan #

So how do you convince your management to invest resources in implementing data governance?

Creating a compelling business case for data governance is essential to securing management buy-in and obtaining the necessary resources for implementation.

Here are some key steps and points to consider when making a business case for your data governance plan:

  1. Start with a clear problem statement
  2. Align with business objectives
  3. Quantify the benefits
  4. Outline the risks of inaction
  5. Present a phased approach
  6. Estimate costs and resource requirements
  7. Highlight competitive advantage
  8. Showcase success stories
  9. Address potential objections
  10. Offer ongoing support

Let’s look at each one of these in detail.

  1. Start with a clear problem statement: Clearly articulate the data-related challenges your organization is facing, such as data quality issues, inconsistent data definitions, regulatory compliance concerns, or missed opportunities for data-driven decision-making.
  2. Align with business objectives: Demonstrate how data governance can support your organization’s broader business goals and objectives, such as increasing efficiency, reducing costs, enabling innovation, improving customer satisfaction, or driving revenue growth.
  3. Quantify the benefits: Whenever possible, quantify the potential benefits of data governance in terms of cost savings, revenue generation, risk mitigation, or efficiency gains. Use concrete examples and case studies from similar organizations to illustrate the potential ROI.
  4. Outline the risks of inaction: Emphasize the risks and potential consequences of not implementing data governance, such as regulatory fines, reputational damage, loss of competitive advantage, or missed opportunities for data-driven insights.
  5. Present a phased approach: Propose a phased implementation plan that allows your organization to start small, demonstrate quick wins, and build momentum for broader data governance initiatives. This approach can help management feel more confident in their investment and reduce the perceived risk.
  6. Estimate costs and resource requirements: Provide a detailed estimate of the costs associated with implementing data governance, including technology investments, personnel costs, training, and ongoing maintenance. Be transparent about the required resources and the expected timeline for implementation.
  7. Highlight competitive advantage: Explain how effective data governance can give your organization a competitive edge in the market, especially in terms of data-driven decision-making, innovation, and customer satisfaction.
  8. Showcase success stories: Share examples of organizations that have successfully implemented data governance and achieved tangible benefits, particularly within your industry or among your competitors.
  9. Address potential objections: Anticipate potential objections or concerns from management and be prepared to address them with well-researched facts and arguments.
  10. Offer ongoing support: Commit to providing ongoing support and guidance throughout the implementation process, including training, communication, and change management, to ensure the success of your data governance initiative.

By presenting a well-reasoned and comprehensive business case that addresses management’s concerns and demonstrates the value of data governance, you increase the likelihood of securing buy-in and obtaining the necessary resources for a successful implementation.

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

Bringing it all together #

By focusing on the points above, you can create a strong foundation for your data governance program, producing tangible improvements in data quality, compliance, and overall decision-making in your organization.

Implementing a solid data governance plan requires a toolset that improves security and compliance without compromising data democratization. Looking for such a tool? Try giving Atlan a spin.

FAQs about how to implement data governance #

1. How can you implement data governance? #


Implementing data governance involves defining clear goals, obtaining executive support, establishing a governance council, and developing a framework. It also includes selecting appropriate tools and fostering a data-driven culture within the organization.

2. What are the key components of a successful data governance strategy? #


A successful data governance strategy includes defined roles and responsibilities, a comprehensive framework for data management, effective tools for monitoring data quality, and ongoing training for staff to ensure compliance and best practices.

3. How does data governance impact data quality and compliance? #


Data governance directly enhances data quality by establishing standards and processes for data management. It ensures compliance with regulations by implementing policies that protect data privacy and security, reducing the risk of breaches and penalties.

4. What are the common challenges faced when establishing data governance? #


Common challenges include resistance to change, lack of executive support, unclear roles and responsibilities, and difficulties in integrating governance tools with existing systems. Addressing these challenges requires strong leadership and a clear communication strategy.

5. How can data governance support data privacy regulations like GDPR or CCPA? #


Data governance supports privacy regulations by establishing policies for data handling, access control, and compliance monitoring. It ensures that organizations manage personal data responsibly, maintain transparency, and uphold individuals’ rights regarding their data.

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