McKinsey Data Governance Framework: 5 Steps to Use It

Updated February 26th, 2024
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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.

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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. 4 Pillars of McKinsey’s data governance framework
  2. Taking a closer look
  3. Underlying principles
  4. How to use McKinsey’s data governance framework?
  5. Bringing it all together
  6. 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, 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.

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 governance 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.

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