What is a Data Governance Framework? Examples & Models

Learn why you should create a single rulebook for the collection, storage, and usage of data. Unlock regulatory compliance and department-wide collaboration.


What is a data governance framework?

A data governance framework is a set of rules, processes, and responsibilities that dictate how an organization collects, organizes, stores, and uses its data. The goal of a data governance framework is to set a standard on how data is managed (to ensure its integrity), leveraged by internal teams, and protected from security risks.

The importance of data governance frameworks

Without a data governance framework in place, companies can’t guarantee data quality  or compliance with privacy regulations. This opens the door to mismanaging customer data, which could land you in hot water legally (resulting in hefty fines and reputational damage).

In the absence of a data governance framework, individual departments follow their own standards and processes, creating data silos that quickly snowball into inefficiencies. Instead of consulting a single source of truth within an organization, employees only have insight into the data collected in their owned tools. This creates blindspots in understanding, and sometimes mismatched reporting between databases – resulting in a distrust of data altogether.

The benefits of a data governance framework

Data democratization

A data governance framework allows you to establish data democratization, giving employees of all technical skill sets the ability to access and act on data. This autonomy and confidence in data allows teams to accurately set goals, measure performance, strategize, and discover new opportunities. 

Data democratization has been notoriously difficult for businesses to achieve in the past few years. (83% of companies admit they’re unable to turn fragmented data points into comprehensive user records.) It’s one of the reasons behind the growing adoption of customer data platforms (CDP) to help manage and centralize customer data so everyone can benefit from it. 

Take Landbot as a prime example. The no-code chatbot platform experienced rapid growth and was struggling to govern its data amid an evolving tech stack. The business implemented Twilio Segment’s CDP to help unify and standardize data at scale, quickly integrate new tools, and give every team access to real-time insights. The result was: 

  • Eliminating the 8 hours of engineering time spent each day on product integration

  • Increasing the accessibility of their data by 80% across all teams. 

  • Easily integrating 12 new tools in a 6-month time span.

Standardized and trustworthy data

An important aspect of good data governance is clear guidelines on how to label and categorize data. Guidelines allow you to standardize data that the entire organization can trust. Efforts to standardize data may include creating a shared data dictionary to ensure consistency across teams in what is being tracked, and their naming conventions. 

A tracking plan is a living document that creates internal alignment on what is being tracked to avoid inconsistencies or duplicate entries.

Data Tracking Plan Template

A data tracking plan helps businesses clarify what events they’re tracking, how they’re tracking them, and why. Use this template to help create your own tracking plan.

Download asset image for Data Tracking Plan Template

Compliance with regulatory requirements

The global rise of customer data privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR), has made it necessary for organizations to know exactly how they collect, store, and use data. Now, certain privacy regulations dictate that a user has a right to request their personal data be deleted by an organization, or that data needs to be stored and processed locally (i.e., data sovereignty laws). 

A data governance framework ensures that a business is adhering to these larger privacy and security regulations. One example of ensuring compliance at scale is with the media publisher Quartz. They used Twilio Segment to consolidate their customer data and prioritize first-party data to get ahead of the deprecation of third-party cookies which signals a massive shift in how digital advertising will be done. With Segment, Quartz was able to automate consent management, enforce privacy policies, and streamline regulatory compliance, while also honing their personalization strategies. Learn more about Quartz’s strategy here.

Improved business performance

Data governance sets clear processes for the collection, storage, and use of data. When employees know how to collect and where to find important data, the results are improved efficiency and data accuracy. 

The cumulative effect of these practices is better decision-making and performance. With precise customer data, for instance, the marketing team can optimize their campaigns to result in a higher ROI. The leadership team is also able to make better strategic decisions on which products or use cases to prioritize.

How do data governance frameworks work?

Data governance frameworks will vary depending on the business. However, the Data Governance Institute (DGI), which listed 10 essential components that you’ll often find some combination of in any framework. We cover a few of the overarching themes below.


First order of business is to understand who will be responsible for establishing the rules and processes within your data governance framework. (When we say “rules,” we’re referring to all policies, definitions, and standards that you use for your data.) 

Who is controlling this data decision-making process? Who is responding to issues that stem from non-compliance within that framework? 

Some businesses may create a Data Governance Office (DGO) to lead this initiative, maintain documentation, communicate policies, track metrics, and more. This DGO could be a team of people or stakeholders, or an individual person (usually a data architect). 

Data stewards may then be assigned throughout the organization to ensure internal alignment on standards and make recommendations. Larger organizations might have multiple councils to address different data issues, such as data storage, quality, and securing sensitive data.


Along with establishing a data governance framework, you’ll want to define the specific goals and metrics that will be used to measure the success of your initiative. The Data Governance Institute recommends considered the impact to the “4 Ps”:

  • Programs

  • Projects

  • Professional disciplines

  • People

For example, you should evaluate how your data governance initiative impacted revenue, costs, or the risk of regulatory violations.

Performance Monitoring

Accountabilities refer to all of the tasks that must be done in order to comply with your data governance framework. These processes should be repeatable, documented, and target different aspects of data governance, such as:

  • Assigning decision rights

  • Managing change

  • Resolving issues

  • Defining data quality 

Approved Tech

In a data governance framework, stakeholders should approve the tech that’s being used to process, store, and use data, along with ensuring specific controls are in place to prevent data breaches.

Collaboration Standards

Data stakeholders are all the employees who create, use, and regulate data across the organization. Leaders of the data governance initiative must decide which stakeholders to include or consult with during the decision-making process and which ones should just be informed of the final decisions.

Data governance framework models and examples

Before we dive into specific examples of data governance frameworks, we should first touch on the five main data governance models. The models are based on how data governance decisions will flow through your organization.

  • Top-down: Company leadership implements data governance policies that are then passed down to individual business units and shared with the rest of the company.

  • Bottom-up: Employees at the lower levels implement data governance practices, such as standardizing naming conventions, which spread to the higher levels of the organization.

  • Center-out: The team or individual responsible for data governance sets data standards that the entire organization follows.

  • Silo-in: Various departments come together to align on data governance while keeping in mind the needs of each group.

  • Hybrid: Data governance decisions involve different levels of the organization. For example, a company uses a center-out model to suggest a course of action but employs a top-down model to make the final decision.

DGI data governance framework

The DGI framework is most appropriate for larger organizations and enterprises with complex data systems. It addresses rules, processes, and the people and organizational bodies that are needed for effective data governance.

DGI framework


McKinsey’s data governance model

McKinsey sees an effective data governance framework as consisting of a central data management office (DMO), a data council, and data leadership by domain.



The data management office (DMO) consists of leaders who set data governance standards, while the data council resolves issues and ensures compliance with previously set standards. Domain data leadership is responsible for data quality in their domain (such as transactional or product data) and takes part in the data council.

PwC’s enterprise data governance framework

PwC’s framework consists of four components that span strategy, data governance stewardship, data governance enablers, and data management.

PwC Data Governance Framework


Other leading data governance frameworks to consider include the Data Management Body of Knowledge (DAMADMBoK) and the Data Management Capability Assessment Model (DCAM).

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Frequently asked questions

Data governance frameworks will vary between businesses, but a few overarching components include:

  • Establishing ownership: perhaps through a data governance council or appointing data stewards across cross-functional teams.

  • Goal-Setting: setting measurable goals and metrics for your data governance framework. The Data Governance Institute recommends thinking about the impact your framework will have on the “Four P’s,” which are Programs, Projects, Professional Disciplines, and People.

  • Approved tech & security: Identifying the proper tools and technology to be used throughout the data lifecycle, and ensuring controls are in place to protect against security threats and data breaches.

  • Collaboration: Choosing stakeholders across teams to consult with during the decision-making process and provide feedback.

To create a data governance framework, an organization needs to:

  1. Use a data maturity model to evaluate its current state of data.

  2. Set up a dedicated data governance team.

  3. Align on the issue(s) that the framework must address, such as data security or data integrity.

  4. Decide on data rules and processes you must implement.

  5. Set key performance indicators (KPIs) to measure the impact of the data governance program.

There are five data governance framework models:

  1. Top-down

  2. Bottom-up

  3. Center-out

  4. Silo-in

  5. Hybrid.

The models are organized around how data governance decisions will flow through the organization.

Data governance refers to all of the rules, responsibilities, and procedures that govern data collection, storage, and usage. Data management includes all of the business processes and software tools an organization implements to achieve data governance.

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