6 Commonly Referenced Data Governance Frameworks in 2022

April 21st, 2022

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A robust data governance framework helps ensure the quality, integrity, security, discoverability, and usability of all data an enterprise collects.

The framework must span across the entire lifecycle of a data asset — from data acquisition, storage, transformation, archival, and deletion.

Finally, the framework must also ensure that data consumers must readily access data(and its metadata) with a robust privacy and security layer around data assets.

Here is a list of the most commonly referenced data governance frameworks:

  1. DAMA DMBOK — Data management body of knowledge functional framework
  2. The DGI data governance framework
  3. McKinsey — Designing data governance that delivers value
  4. Eckerson — Path to modern data governance
  5. The SAS data governance framework
  6. PwC enterprise data governance framework

Before we dive into learning each of the frameworks in detail, let's understand:

  1. What is a data governance framework?
  2. What is the purpose of a data governance framework and why does your organization need one?

What is data governance?

According to Gartner:

Data governance is the specification of decision rights and an accountability framework to ensure the appropriate behaviour in the valuation, creation, consumption, and control of data and analytics.”

Let’s look at one more official source. The DAMA Dictionary of Data Management defines data governance as:

“The exercise of authority, control and shared decision making (planning, monitoring, and enforcement) over the management of data assets.”

It goes without saying that data is the key to successful transformations in a post-pandemic world where digital initiatives have become the norm. However, simply capturing good data isn’t enough.

For data to be of any use in making reliable decisions, it must be:

  • Relevant
  • Of high quality and accuracy
  • Trustworthy
  • Easy-to-understand and use

And for data to comply with regulatory standards, it must:

  • Facilitate lineage tracking back to the source
  • Record metadata along with its context in a data dictionary or a catalog
  • Ensure data quality monitoring and reporting
  • Create, enforce and document access policies

Good data governance ensures that your data meets the prerequisites mentioned above so that it is accurate, actionable, and auditable. For more on data governance and its role in deriving value from data, check out this blog.

Why is data governance so important?

The management consultancy McKinsey puts it this way:

“For data to fuel digital initiatives, it must be readily available, of high quality, and relevant. Good data governance ensures data has these attributes, which enable it to create value.”

Data governance helps you set up standardized data systems, policies, procedures, and measures throughout the organization. As a result, you increase the value and credibility of your data to drive decision-making and improve efficiency.

In that process, you reduce the risk of major disruptions exposing your data or rendering it useless.

For instance, the threat of cyberattacks is rising and affecting everyone, from governments and major corporations to small businesses. Incidents like malware attacks, security breaches, or data leaks can wreak irreversible damage to an organization's operations, finances, and reputation. (Remember SolarWinds, NotPetya or WannaCry?)

That's where standards and policies governing access to data or making changes to existing data can make a difference. Such measures also help organizations avoid hefty fines and colossal losses (like NASA's lost Orbiter) from inconsistencies in data.

Now you might think — if data governance is so important, why doesn't everyone already have it? Short answer: it’s not that easy to set up.

What makes incorporating solid data governance a challenge?

Gartner states that only 20% of organizations investing in governance will scale their initiatives for the entire business through 2022.

The biggest barrier to governance across the organization is a lack of a standardized approach — the goals aren’t well-defined. The “why” of governance, be it compliance, efficiency, or revenue, isn’t clear. In several cases, neither is the ROI.

Barriers to good governance

The ten common barriers to achieving data governance

Even when a standardized approach or a framework is being developed, it isn't planned meticulously. For instance, if the use case doesn't tie into the organization's overall business goals, then it's tough to quantify its ROI and get buy-in from the C-suite.

Here’s how SAS puts it: “Companies get more traction if the governance initiative links to a specific strategic initiative or business challenge. Not only does this allow governance activity and investment to follow corporate objectives (and make clear the ROI for such activity), but it also eliminates the “academic exercise” label that is sometimes applied to data activity.”

That brings us to another related challenge — often, senior leadership doesn’t see the value in data governance. As a result, they don’t support governance initiatives with adequate resources — funding, talent, technology, and autonomy.

One last hurdle — accountability. Who decides how you decide? Who defines data standards or monitoring and ensuring data quality? Setting up a team of data professionals — data stewards, data owners, chief data officers — with adequate resources and autonomy who are answerable to the C-suite and regulatory bodies is essential for it to work. Failure to do so results in chaos.

Overcoming these challenges means rethinking the approach to data governance and restructuring business and IT teams to work together. Organizations must embrace the three driving principles of a good governance program:

  1. Governance requires business and IT to collaborate to achieve shared outcomes that impact the business.
  2. Implementing governance means setting up dedicated teams and giving them the autonomy and resources to implement governance programs.
  3. Governance is a journey, not an event, and that’s why the efforts to enforce governance must be continuous.

Having a comprehensive framework — a “how-to” guide — can tackle the barriers to governance.

What is a data governance framework?

Here’s how the Data Governance Institute (DGI) defines a data governance framework:

“The data governance framework is a logical structure for classifying, organizing, and communicating complex activities involved in making decisions about and taking action on enterprise data.”

As we mentioned earlier, a data governance framework gives you the “how” for data governance, and that’s how we at Atlan define the concept:

“A data governance framework is the how — a blueprint for enforcing governance.”

Why do we need a data governance framework?

According to the Data Governance Institute (DGI), every organization must have:

  • A set of rules (policies, requirements, standards, accountabilities, controls) and the rules of engagement describing how different groups work together to make and enforce these rules
  • The people and organizational bodies making and enforcing those rules
  • The processes that will govern data while creating value, managing cost and complexity, and ensuring compliance

That’s because everyone within the organization must agree on deciding how to decide. Otherwise, it’s impossible to standardize the implementation of your data governance program and get the support of all the stakeholders in realizing the program’s goals.

That’s when it’s common to hear frustrated questions such as:

  1. What do certain fields in this data set mean? Who can help me figure this out?
  2. There are two sales reports for my region showing different projections. Why is that? How do I verify the accuracy of each report?
  3. There are some changes in this data set that didn’t exist before. Who made these changes?
  4. If I change some fields within this data set, which reports will it affect? And how?
  5. Can I share this data? If yes, how do I do that?

Having a solid framework in place gets rid of these frustrations, saving time and effort that data teams can use to analyze and draw valuable insights from data.

What is the purpose of a data governance framework?

Data governance is all about deciding how to decide. And like everything else, adopting a structured approach that dictates such decisions is essential for them to work. That’s where a data governance framework comes in handy — it is the foundation for ensuring effective data governance across an organization.

If you’re still contemplating the need for data governance or a framework to implement it, here’s a cautionary tale from NASA to make that decision for you.

How a math problem cost NASA $125 million

Way back in 1999, NASA lost its Mars Climate Orbiter because of a translation problem. Unfortunately, spacecraft engineers didn’t make the switch from the Imperial to the metric system for their measurements.

“It is very difficult for me to imagine how such a fundamental, basic discrepancy could have remained in the system for so long. I can’t think of another example of this kind of large loss due to English-versus-metric confusion. It is going to be the cautionary tale until the end of time.”

John Pike, space policy director at the Federation of American Scientist

What if they had a central data repository — complete with a glossary that provided adequate context and standardized processes — that governed the recording and storage of all data? The Orbiter might still have disappeared, but discrepancies in data wouldn’t be the cause.

That’s why data governance, along with a solid framework, is so important. However, before we move on to the nitty-gritty of a data governance framework, let’s quickly recap the concept of data governance.

Data governance frameworks in detail

There’s no need to reinvent the wheel. There are several established, tried, and tested frameworks already in use.

Let us briefly deep dive into each of these models to understand the differences and similarities.


DAMA DMBOK is one of the most popular data governance frameworks.


The DAMA DMBOK functional framework. Source

It pictures data management as a wheel with data governance at the center (the hub) surrounded by nine knowledge areas. Data governance is considered to be the high-level planning required for effective data management.

Each knowledge area explores an avenue of data governance. For instance:

  • Data architecture management represents the overall structure of data and how it connects with each application within the data ecosystem.
  • Data development is all about data modeling, requirement analysis, design, implementation and maintenance of data storage elements, like databases.
  • Metadata management involves collecting, categorizing, integrating and maintaining high-quality metadata.

The framework further defines environmental elements that provide structure to each knowledge area. They define the underlying processes, roles, technologies and deliverables that guide the planning and execution of each area.

They also cover how an organization’s culture must evolve for data governance initiatives to work.

DAMA DMBOK Environmental elements

The seven environmental elements guiding each knowledge area in DAMA DMBOK. Source


The DGI framework comes with ten universal components that address the why-what-who-how of data governance.


The DGI data governance framework. Source

Let’s look at some of these components:

  • Goals, metrics and funding are all about elaborating how the data governance program would increase revenue, optimize costs and ensure business resilience despite risks or disruptions.
  • Controls are for risk management and can be preventive or corrective. They can be applied at various levels of the framework to support the goals of the data governance program.
  • The DGO oversees the entire governance program, collaborates and liaises with other stakeholders, aligns data-related policies and standards, and maintains detailed records on the program.

DGI divides each of its components into core areas: rules, people, and processes to simplify the concepts.

DGI Components

The ten components of the DGI framework. Source


McKinsey believes that rethinking the entire organizational design is the starting point for ensuring success with data governance. Their approach includes three core components:

  • A data management office (DMO) defines policies and standards, trains and guides data leaders and ensures that data governance is connected with every other function within the organization.
  • Domain-based roles manage the day-to-day execution of the data governance program.
  • A datacouncil heads the overall strategic direction of the data governance program. It brings the DMO and domain leaders together to review progress, authorize funding, and resolve issues and roadblocks to effective governance.


A sound data governance model, according to McKinsey. Source


The Eckerson Group’s framework has six layers and 39 components. Let’s look at some of the layers:

  • Goals and standards address the why and how of implementing a data governance program.
  • Processes ensure that the data governance initiatives meet their end goal. This could be anything from ensuring quality and accuracy to cataloging metadata.
  • Culture fosters an environment of collaboration, data democratization and transparency without any conflict.

The USP of this framework is that it puts people at the heart of data governance by defining roles such as data owners, stewards, curators and stakeholders to outline their roles and responsibilities when accessing, using and changing data.

That’s because:

“The reality is that we don’t govern the data. We govern what people do when working with data.”


Eckerson Group’s modern data governance framework. Source


“The SAS Data Governance framework illustrates a comprehensive framework for data governance that includes all the components needed to achieve a holistic, pragmatic data governance approach.”

The framework focuses on showing how organizations value their data with effective data management and governance practices.

The data governance and methods modules represent the data governance framework — drafting policies, monitoring their implementation and ensuring that they drive data quality, architecture and security.

While the corporate drivers oversee the strategic aspects of data governance, the data management, solutions and stewardship modules handle the tactical or operational aspects.


The SAS data governance framework. Source


The PwC enterprise data framework takes conventional models such as DAMA DMBOK and DGI a step further to account for next-gen data landscapes.


The PwC enterprise data governance framework. Source

PwC includes five components in its framework starting with a data governance strategy, followed by a management layer encompassing all the aspects of a data ecosystem.

The lifecycle management layer covers all the policies required to ensure streamlined flow of data throughout its lifecycle. The stewardship layer focuses on enforcing governance and the governance enablers account for the people, processes and technologies involved in ensuring effective governance.

Phew, that’s a lot of information to process!

We get it. That’s why we’ve pulled together the elements that most of these frameworks have in common to give you our take.

Regardless of which data governance framework you choose, make sure that it addresses these vital aspects:

  1. The goal of the data governance program and its KPIs
  2. The key definitions for the game plan — data standards, rules, policies and controls
  3. The people with the authority and responsibility to create, execute, manage and monitor the data governance program
  4. The processes that ensure the governance program meets its goals
  5. The technology that enables effective data governance — tools for data cataloging, discovery, profiling, exploration, lineage and more

How can you build a data governance framework for your organization?

Here’s a 7-step process you can use to build a comprehensive data governance framework for your organization:

1. Identify the “why” of governance.

Pick one that resonates the most with your organization and business goals to get started.

2. Describe how you will measure the “why”.

Once your objectives are clear, the next step is to figure out how to measure the outcomes. The key is to find the right metrics that can translate into a significant business outcome in terms of revenue, costs or efficiency.

3. Define the scope, outline compliance requirements and clear any misinterpretations.

This is the stage where you should standardize the rules, standards and definitions around all data assets. Standardizing avoids math errors like that of the NASA orbiter and gets rid of questions like:

  • What does this field mean?
  • Why are there two fields with the same value?
  • Should I record our international sales in USD or Euros?

While it may be too complex for the entire organization to remember all the rules and follow them to the T, the folks responsible and accountable for ensuring governance should be well-versed.

4. Get buy-in from all stakeholders, including the C-suite.

A major hurdle in governance is navigating the organization’s culture — every team has its values, priorities, styles and preferences. The best way to unite the entire organization behind shared outcomes is to get all the stakeholders on board.

5. Define who is leading the governance efforts.

Accountability and autonomy are the two factors crucial to the success of any data governance initiative. That’s why it’s prudent to set up a data governance council or a steering committee responsible for strategic decisions and ensuring that the operational aspects align with the strategy.

6. Outline processes.

Here’s where the data governance council defines processes for each data function. These include:

  1. Data storage and modeling
  2. Data definitions
  3. Cataloging and mapping lineage
  4. Data security and access policies
  5. Data quality, integrity and interoperability
  6. Reporting

The outline should include quality rules, common conventions, best practices, recommendations and alternate approaches to help data governance teams weave governance into each function.

7. Leverage technology to automate what you can.

Data governance for any enterprise is a complex and arduous endeavor.

That’s why using documents or spreadsheets to track these efforts isn’t practical or reliable. Instead, organizations should opt for technologies that automate data discovery, quality checks, profiling, cataloging, lineage or building business glossaries.

For instance, a modern data catalog:

  • Gathers and consolidates data from diverse sources and applications under one roof
  • Enriches data assets with metadata and contextual descriptions
  • Makes data assets instantly discoverable
  • Provides details such as data asset owner, origins, latest modifications and a history of changes made
  • Auto-generates  data quality profiles

We'll elaborate a bit more on the best practices to build a data governance framework in an article following this. Stay tuned!

Learn how to build a robust data governance framework with best practices from data leaders.


Data governance is central to the success of digital transformation initiatives and data-driven decision-making. Additionally, as digitization becomes the norm, the regulations governing the use of data will continue to rise and become more stringent.

That’s why, having a solid data governance framework in place, allocating adequate resources (funding and technology) and appointing people who make data governance a top priority of their job roles isn’t optional anymore. It’s the key to effective governance.

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.

Free Guide: Find the Right Data Catalog in 5 Simple Steps.

This step-by-step guide shows how to navigate existing data cataloging solutions in the market. Compare features and capabilities, create customized evaluation criteria, and execute hands-on Proof of Concepts (POCs) that help your business see value. Download now!