Data Governance Examples: Real-World Case Studies for 2025

Updated December 13th, 2024

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Data governance examples provide valuable insights into effective data management.
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Organizations leverage governance frameworks to improve data quality and compliance.

For instance, Airbnb promotes data literacy, while GE Aviation centralizes data access.

These case studies reveal the critical components of a successful governance strategy. They also emphasize the role of data stewardship in enhancing decision-making processes.

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

Data governance goes beyond policies. It elevates trust in your data and empowers you to make better decisions, improve operational efficiency and save costs.

But what does successful data governance look like in action?

Let’s dive into five real-world data governance examples from top organizations, such as Airbnb, GE Aviation, Wells Fargo, CSE Insurance, and Uber.

Table of contents #

  1. Data governance examples: 5 case studies
  2. Data governance examples: 5 key takeaways
  3. How organizations making the most out of their data using Atlan
  4. Bottomline
  5. FAQs about data governance examples
  6. Data governance examples: Related reads

Data governance examples: 5 case studies to visualize the impact of data governance #

Let’s take a closer look at five data governance examples, each showcasing how data governance can drive success and contribute toward your business outcomes:

  1. How Airbnb used data literacy to promote data-driven decision-making
  2. How GE Aviation balanced data governance with data enablement
  3. How Wells Fargo built a single source of truth to ensure proper governance
  4. How CSE Insurance transformed its data culture for better data management
  5. How Uber set up a flexible approach to data governance

Let’s explore each case study further.

How Airbnb used data literacy to promote data-driven decision-making #


At Airbnb, effective data governance encompasses data management and enhancing data literacy.

How are data literacy and data governance connected?

Data literacy is a crucial component of data governance as it supports the ability to read, understand, create, and communicate data.

Organizations that do not fully understand their data, lose control of it throughout its lifecycle. Moreover, when people are fluent in the generation, use, and application of data, teams can make data-informed decisions.

Data literacy also makes them use data responsibly, which is essential for the success of any data governance program.

How did Airbnb promote data literacy?

To ensure data literacy, Airbnb has launched an in-house educational initiative called “Data University”.

The program is designed to boost data literacy across all departments, equipping employees to understand, interpret, and use data effectively in their roles.

Data University is Airbnb’s dynamic data education program, with the vision to empower every employee to make data-informed decisions.

Data literacy at Airbnb helped democratize data.

Data literacy at Airbnb helped democratize data. - Source: Medium.

As a result, Airbnb could democratize data and scale decision-making, while ensuring responsible use of their data.

Also, read → How to measure data literacy

How GE Aviation balanced data governance with data enablement #


GE Aviation embarked on a mission to centralize its scattered data sources, making it more accessible and reliable for all users within the organization. They called the initiative Self-Service Data (SSD).

“Self-service initiatives often fail in large enterprises for a variety of reasons. The issues boil down to a larger problem: self-service gets treated as a one-time project that gets launched, then forgotten.”

That’s why GE Aviation got started by setting up the SSD data team responsible for user enablement, tooling, data product (i.e., dashboards) deployment, and exploring new opportunities to improve processes.

Simultaneously, GE Aviation also set up a Database Admin team responsible for data governance, proper use of data, and supporting users whenever required. They ensured that all data products are well-documented, have project owners, and describe the workflow of approvers in detail.

What is the process for deploying data products at GE Aviation?

There’s a 4-step process to deploying data products (i.e., dashboards):

  1. Any user can look up and use the datasets for which they have access permissions. The datasets being used for setting up the data product must be tagged appropriately.
  2. After adhering to the compliance rules, users can create projects in a design environment to explore that data and test various data pipelines.
  3. Once the user pushes their project to production, a series of checks occur — verifying naming conventions, checking the schemas being used, ensuring proper data distribution, verifying dataset size, etc. The Database Admin and Self-Service Data teams run these checks manually.
  4. If the data product passes all checks, it is automatically pushed to the production environment.

According to Jonathan Tudor, the former Director of Data Platforms and Governance at GE Aviation:

“It’s incredibly important to create and communicate common definitions and processes so that you and your user base are speaking the same language and to ensure that you use automation to enforce those definitions and processes. Otherwise, you’ll have chaos instead of outcomes.”

The approach accelerated decision-making processes and instilled a sense of data ownership among employees. This has led to improvements in safety and operational efficiency.

Read more → Data governance framework examples, templates, standards, and best practices

How Wells Fargo built a single source of truth to ensure proper governance #


Wells Fargo’s data governance strategy highlights the importance of creating a single source of truth to enhance data accuracy and reliability.

Their approach was to centralize data from multiple sources to create a unified, trustworthy source that reduced discrepancies and improved consistency.

This streamlined data management and allowed for more accurate reporting and analysis, thereby enhancing decision-making across the organization.

Here’s how Prahalad Thota, the former Senior Vice President, Head of Enterprise Analytics & Data Science at Wells Fargo, describes their efforts:

Wells Fargo’s data governance strategy also emphasized the importance of data visualization (using Tableau) to make data more accessible to non-technical stakeholders and improve overall data literacy.

As a result, Wells Fargo was able to ensure data consistency, accuracy, and visualization to reduce risks and foster data-driven decision-making.

Read more → How do large organizations avoid data silos

How CSE Insurance transformed its data culture for better data management #


CSE Insurance offers property and casualty insurance across the United States. Like many other insurance companies, they faced challenges in governing and managing their data.

The data was siloed across various sources, making it difficult for teams to find, access, and use the information they needed.

Moreover, basic definitions and metrics weren’t consistent, and migrating their documentation required starting everything from scratch.

To remedy these issues, CSE Insurance set up a single source of truth powered by data governance policies and procedures.

They also set up a small group of data champions to kick off their documentation initiative using a single platform to document data. The platform also helped all users get complete context with shareable data profiles and collaborate effectively.

As a result, CSE Insurance was able to transform its data culture and manage data more efficiently.

Also, read → Modern data culture

How Uber set up a flexible approach to data governance #


Uber operates in 70+ countries, each with its own data governance framework to comply with local and regional laws.

To ensure compliance across all regions, Uber uses a core platform to take care of data privacy and security centrally. Uber collects data globally but adapts its governance policies depending on the origins of each dataset by using customizations and plugins.

Here’s how Manikandan Thangarathnam, the Senior Director of Mobility and Platforms at Uber, puts it:

The company has invested in training its people and honing their skills to ensure the success of its data governance programs across all regions.

Data governance examples: 5 key takeaways #

Having explored real-world data governance examples, let’s highlight some of the vital lessons from these stories:

  1. Establish a clear data governance structure with well-defined roles and responsibilities, as demonstrated by GE Aviation’s SSD and Data Admin teams
  2. Set up a single source of truth, as CSE Insurance and Wells Fargo did, to eliminate data silos and make your data governance efforts more streamlined
  3. Treat data as a product and assign owners responsible for ensuring its adherence to your rules, policies, and standards
  4. Adopt a flexible, rather than a ‘one-size-fits-all’, approach to data governance
  5. Invest in enhancing data literacy so that your employees can understand, interpret, and use data effectively and responsibly, just like Airbnb did with its in-house educational initiative

Read more → Data governance best practices

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.

Bottomline #

It’s crucial to remember that effective data governance goes beyond compliance and risk management — it serves as a strategic catalyst for cultivating trust and generating value from data.

Drawing insights from the successful data governance practices of established organizations helps you learn how to balance compliance with data enablement and democratization.

Such insights help you overcome common obstacles and develop a data governance program that drives business growth, besides helping you navigate the regulatory landscape.

FAQs about data governance examples #

1. What is a real-life example of data governance? #


A real-life example of data governance is Airbnb’s Data University initiative. This program enhances data literacy across the organization, empowering employees to make data-informed decisions. By promoting understanding and responsible data use, Airbnb demonstrates effective governance in action.

2. What are the four pillars of data governance? #


The four pillars of data governance include data quality, data management, data privacy, and data compliance. These components work together to ensure that data is accurate, accessible, secure, and used responsibly within an organization.

3. What is data governance in simple terms? #


Data governance refers to the management of data availability, usability, integrity, and security in an organization. It establishes policies and standards to ensure that data is handled properly and used effectively to support decision-making.

4. What is a data governance framework example? #


A data governance framework example is the approach taken by Wells Fargo. They created a single source of truth by centralizing data from multiple sources. This framework enhances data accuracy and reliability, supporting better decision-making across the organization.

5. How can effective data governance improve data quality and compliance? #


Effective data governance improves data quality by establishing clear standards and processes for data management. It ensures that data is accurate, consistent, and accessible. Additionally, it enhances compliance by enforcing regulations and policies related to data privacy and security.

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