McKinsey's Data Governance Framework: 4 Pillars & 5 Steps to Implement it

Updated December 04th, 2024

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McKinsey’s data governance framework offers a comprehensive structure for managing data effectively, focusing on compliance, quality, and scalability.

Built on four core pillars, it emphasizes accountability, policy enforcement, and risk management.
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This framework integrates governance into daily operations, ensuring data quality, compliance, and actionable insights.

By aligning governance strategies with business goals, McKinsey’s model helps organizations unlock the full potential of their data ecosystems.

The McKinsey data governance framework is a set of principles and practices that can help organizations to manage their data effectively. The framework provides a comprehensive and systematic approach to data governance that can help organizations to achieve their business goals.

In this blog, we will understand the four pillars of McKinsey’s data governance framework and more importantly how to implement them for your data teams and data assets.

Let’s dive in!

Table of contents #

  1. What are the 4 pillars of McKinsey’s data governance framework?
  2. The four pillars of McKinsey’s data governance framework: Taking a closer look
  3. The underlying principles of McKinsey’s data governance framework
  4. How to use McKinsey’s data governance framework?
  5. How organizations making the most out of their data using Atlan
  6. Bringing it all together
  7. FAQs about McKinsey Data Governance Framework
  8. McKinsey’s data governance framework: Related reads

What are the 4 pillars of McKinsey’s data governance framework? #

McKinsey defines data governance as the orchestration of processes, policies, and structures to ensure the effective and efficient use of data within an organization.

The framework is based on the following four pillars:

  1. Leadership and sponsorship
  2. Governance policies and procedures
  3. Data stewards
  4. Technology solutions

Let us understand the four pillars in brief:

1. Leadership and sponsorship #


Data governance must be championed by senior leaders within the organization. This will help to ensure that data governance is seen as a strategic priority and that resources are made available to support it.

2. Governance policies and procedures #


The organization should develop a set of policies and procedures that define how data is to be managed. These policies should cover areas such as data ownership, access control, data quality, and data security.

3. Data stewards #


Data stewards are responsible for implementing the organization’s data governance policies and procedures. They work with business users and IT professionals to ensure that data is managed in a consistent and compliant manner.

4. Technology solutions #


Technology solutions can help to automate data governance tasks and make it easier to manage data at scale. These solutions can also help to improve data quality and security.

The McKinsey data governance framework is a comprehensive approach to managing data that can help organizations to achieve their business goals.

The four pillars of McKinsey’s data governance framework: Taking a closer look #

Now that we know the four pillars of McKinsey’s data governance framework, let us look into each of them in detail:

1. Leadership and sponsorship #


Data governance must be championed by senior leaders within the organization. This will help to ensure that data governance is seen as a strategic priority and that resources are made available to support it. Senior leaders should communicate the importance of data governance to the entire organization and provide support for the data governance team.

2. Governance policies and procedures #


The organization should develop a set of policies and procedures that define how data is to be managed. These policies should cover areas such as data ownership, access control, data quality, and data security. The policies should be clear, concise, and easy to understand. They should also be consistent with the organization’s overall business goals.

3. Data stewards #


Data stewards are responsible for implementing the organization’s data governance policies and procedures. They work with business users and IT professionals to ensure that data is managed in a consistent and compliant manner. Data stewards should have a deep understanding of the organization’s data landscape and the challenges and opportunities associated with data management.

4. Technology solutions #


Technology solutions can help to automate data governance tasks and make it easier to manage data at scale. These solutions can also help to improve data quality and security. There are a variety of technology solutions available, and the best solution for an organization will depend on its specific needs.

The underlying principles of McKinsey’s data governance framework #

The framework is based on four key principles, which are:

Principle #1: Alignment with business strategy #


Data governance should support your organization’s overall objectives and strategic goals.

Principle #2: Comprehensiveness #


Data governance should cover all aspects of data management, including but not limited to data quality, data privacy, security, and lifecycle management.

Principle #3: Flexibility and adaptability #


Your data governance framework should be capable of adapting to evolving business needs and changes in the regulatory environment.

Principle #4: Data stewardship culture #


Successful data governance requires a culture of data stewardship, in which every member of the organization understands their role in maintaining the quality and security of the data.

This framework is a valuable resource for any organization looking to enhance its data governance practices. By following a systematic approach, you can effectively leverage data to achieve your business goals.

How to use McKinsey’s data governance framework? #

In this section, we will understand how McKinsey’s data governance framework helps improve data quality, reduce the risk of data breaches, make data-driven decisions, and enhance compliance with regulations.

Its approach to data governance can be summarized in five core steps, which are:

Step #1: Define the scope of data governance #


The first step involves identifying what data needs to be governed. Start with a data inventory and cataloging exercise. Identify the different types of data your organization has, where it is stored, who owns it, who uses it, and what it is used for.

It’s crucial to understand what data you have, where it’s coming from, and how it’s being used. Prioritize the data types that are most critical to your operations and strategic objectives. This could include customer data, financial data, operational data, and so on.

Step #2: Identify key stakeholders #


Once the scope is defined, the next step is to identify the people in your organization who will be involved in data governance. Establish a Data governance committee, which includes representatives from different parts of the organization, to oversee the data governance initiative.

This could include:

  1. Data owners (those who have ultimate responsibility for a particular data set)
  2. Data stewards (those who ensure data quality and compliance)
  3. Data users (those who rely on the data for their work), and
  4. IT support (those who provide the technical infrastructure for data storage and management)

Step #3: Develop data governance policies and procedures #


With your key stakeholders identified, you can begin to develop your data governance policies and procedures. This should cover how data is created, collected, stored, accessed, shared, used, archived, and deleted. It should also cover data quality standards, data security measures, data privacy requirements, and data ethics guidelines.

Also, consider the specific regulations that your organization must comply with, such as GDPR or other Brazilian data protection laws, and make sure your policies and procedures are aligned with these regulations.

Step #4: Implement data governance controls #


With your policies and procedures in place, the next step is to implement controls to ensure that these policies and procedures are followed. This could involve a combination of manual checks (e.g., regular audits or reviews) and automated checks (e.g., data validation rules or access controls in your data management systems).

Also, consider investing in data governance tools or platforms that can help you automate some of these controls and make your data governance processes more efficient.

Step #5: Monitor and improve data governance #


Data governance is not a one-time project but an ongoing process. Establish regular reviews of your data governance framework to ensure it is still effective and relevant. This could involve tracking certain metrics (e.g., data quality scores, number of data breaches, etc.), conducting regular audits, or getting feedback from data users and other stakeholders.

Also, be prepared to update your data governance framework as your organization’s needs change, as new regulations come into effect, or as new best practices emerge in the field of data governance.

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 #

The McKinsey data governance framework is a valuable resource for organizations that are looking to improve their data management practices. By following the principles and practices outlined in the framework, organizations can improve the quality, security, and usability of their data. This can lead to improved decision-making, increased efficiency, and enhanced customer service.

Atlan: Effortless data governaFnce for the modern data stack #


The entire data management space is going through a paradigm shift. The data world is slowly converging around the best of the tools for processing large amounts of data, a.k.a the “Modern data stack”

Data governance for the “Modern data stack” needs a rethinking.

This is where Atlan comes to your support — a data catalog and data governance solution built for agility, trust, and collaboration.

FAQs about McKinsey Data Governance Framework #

1. What is the McKinsey Data Governance Framework? #


The McKinsey Data Governance Framework is a comprehensive set of principles and practices designed to help organizations manage data effectively. It emphasizes structured data management to align with business goals, ensuring data quality, accessibility, and compliance.

2. How does McKinsey approach data governance? #


McKinsey’s approach involves a structured methodology, focusing on aligning data governance strategies with organizational objectives. This includes robust policies for data stewardship, quality control, and leveraging technology for scalable data governance.

3. How can organizations benefit from McKinsey’s Data Governance Framework? #


Implementing McKinsey’s framework enhances decision-making through reliable data insights, improves compliance with data regulations, and optimizes operational efficiency by minimizing data-related risks.

4. How does McKinsey ensure data compliance through its governance framework? #


McKinsey employs strict compliance protocols, including regular audits, real-time monitoring, and adherence to global data standards. These measures ensure organizations meet regulatory requirements while maintaining data integrity.

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