Collibra Data Governance: features, overview, and case studies

Data governance still doesn’t get the credit it deserves. So, here’s an article where we’ll talk specifically about Collibra Data Governance and the tools the Collibra platform provides to help enterprise companies manage their data more efficiently and in compliance with relevant regulations.

Key takeaways

• Collibra Data Governance is the core framework of the Collibra platform, defining roles, ownership, policies, and processes for how data is managed across an organization. It underpins and powers the entire platform, enabling effective cataloging, lineage, marketplaces, and dashboards.
• It embeds governance into everyday data work through automation, improving efficiency, compliance, and trust.
• The data operating model (including roles, domains, communities, and the data dictionary) provides a clear structure for accountability and collaboration.
• Automated workflows – approvals, certifications, issue management – turn governance from manual tasks into streamlined, auditable processes, while centralized policy management connects rules directly to data assets.
• Organizations worldwide use Collibra to operationalize governance, improve data quality, and scale their data ecosystems.

What is Collibra Data Governance?

Collibra Data Governance is the foundational layer of the Collibra platform – the piece that defines how data should be owned, managed, and used across your organization, built into the software. It’s not a separate module like, say, Collibra Data Quality and Observability, but rather, it’s the essence of Collibra. 

At its core, Collibra Data Governance is focused on structure and accountability. Collibra provides a ready-made framework that defines who owns the data, who manages it, what it means, and how it should be used, all in one central platform.

And it doesn’t just focus on compliance, as many traditional data governance tools do. Instead, it embeds governance into daily data activities, so teams can easily find, trust, and use data confidently.

According to one of our data governance experts, data governance is already part of the everyday work of anyone who creates, manages, or uses data. Collibra helps by systematizing and automating key governance activities, making them more consistent, transparent, and easier to manage.

“The difference Collibra makes is in how governance is scaled and operationalized. For example, when you need to demonstrate regulatory compliance, doing this manually often means spending significant time compiling information from spreadsheets, documents, and emails. With Collibra, governance information is captured in one place and maintained on an ongoing basis, making reporting, traceability, and audits more efficient and reliable.”

Let’s take a look at the key elements of data governance and how they’re automated using Collibra Data Governance.

The data operating model

The data operating model is the structural foundation of Collibra Data Governance. It defines how data is organized, who’s responsible for it, and how governance activities are managed across different domains and business units.

When implemented well, it becomes the living blueprint of your data organization.

Roles and responsibilities (data owners, data stewards, technical product owner)

The clarity of ownership is a foundation of every data governance initiative. Collibra makes it explicit through roles and responsibilities that are part of its data operating model.

• Data owners are accountable for the quality, compliance, and lifecycle of data assets within their domain. They make the strategic decisions – what’s approved, what’s shared, and what’s restricted.
• Data stewards are the day-to-day guardians. They make sure data definitions, metadata, and quality metrics are maintained accurately, acting as the link between technical and business users.
• Technical product owners or system custodians keep track of the integration between Collibra and source systems, automating data feeds and keeping data flows consistent.

We work with both clients who already have roles created within their teams and those who start from scratch and need to allocate them and create their data governance framework. In short, companies at different levels of data governance maturity.

Collibra makes things easier in both cases by allowing you to map roles directly to assets, domains, and workflows, so ownership is always visible and traceable. And this accountability is what turns governance from theory into action.

Domains and communities

Collibra’s domain and community model mirrors the structure of your organization. (And if you’re starting from scratch, it helps define a relevant structure around data naturally.)

• Domains group related data assets – for example, “Customer Data,” “Finance Data,” or “Product Data.”
• Communities represent groups of people who manage or consume that data – such as the Finance team, Marketing, or Data Engineering.

This hierarchy makes data governance collaborative, which is really important for both adoption and effectiveness. Different teams (e.g., local teams within a global org) can work within their own context while still adhering to enterprise-wide governance standards.

To give you another example, a Marketing Data Community can manage its own glossary terms and approval workflows while aligning with the enterprise’s central data policies.

An example of a business glossary in Collibra. Source: Collibra documentation

This federated model is one of Collibra’s biggest advantages – it scales governance without slowing teams down.

Collibra data dictionary

A crucial part of this operating model is Collibra’s data dictionary, which stores definitions, metadata, and technical details of every data element. It’s where you document what each data field means, its source, relationships, and quality metrics.

Having a shared language is essential to avoid the classic “data means something different to everyone” problem.

We’ve covered this in detail in our Collibra Data Dictionary guide – but in short, the dictionary ensures consistency, transparency, and trust across your data ecosystem.

You’ll read more about the Data Dictionary here: Data Catalog vs Data Dictionary: Understanding Their Roles in Data Governance.

The automated workflow engine

Once your data operating model is in place, governance activities need to be consistent and repeatable. Which is exactly why you need automated data governance workflows.

Built on BPMN (Business Process Model and Notation), the automated workflow engine lets organizations automate governance processes – from approvals to certifications to data issue resolution.

In our implementations, this is often where clients start to see the tangible ROI of Collibra. Manual steps disappear, accountability becomes clear, and governance processes actually get done.

Managing access approvals

Whether it’s approving a new data asset, reviewing a policy, or certifying a glossary term, Collibra workflows handle the process automatically.

Each step can trigger notifications, assign tasks, and record decisions, so that governance actions are documented, auditable, and efficient.

For example, a “New Asset Approval” workflow can route a data set from a data steward to the data owner for approval, then automatically publish it to the Data Catalog once approved.

Resolving data issues (Collibra DQ & Observability)

Collibra’s governance workflows integrate with Collibra Data Quality & Observability, allowing data issues to be tracked, assigned, and resolved in context.

When a data quality rule fails or a threshold is breached, an automated workflow can create a data issue record in Collibra. The issue can then be routed to the appropriate owner or steward, complete with details from Collibra DQ – like rule name, dataset, and failed records.

This really helps with transparency and getting issues out of silos they’re so often in, while other teams or departments are completely unaware. 

Asset certification

Collibra’s asset certification workflows are designed to make sure that data assets meet defined quality and compliance standards before they’re shared or consumed.

Certification can include checks for:

• Data completeness and accuracy
• Up-to-date metadata
• Compliance with internal or regulatory policies

Once an asset is certified, it can be labeled as “trusted” in the Data Catalog or Data Marketplace, giving users confidence that they’re using reliable data.

From our experience, automated certification is a game-changer for organizations struggling with data trust issues. It replaces manual validation with a transparent, repeatable process.

Centralizing policy management

Collibra Data Governance goes beyond data management software. It helps actually enforce the rules that define how data should be handled.

Collibra’s Policy Management centralizes these rules, letting companies document, approve, and monitor policies across business units and regulatory frameworks.

This includes:

• Data privacy and security policies (GDPR, CCPA, etc.)
• Data retention and lifecycle rules
• Access control and usage guidelines

Each policy can be linked to relevant data assets, processes, and responsibilities within the Collibra environment.

For example, a GDPR data retention policy can be linked directly to datasets containing personal data. If the policy changes, every related asset and owner will automatically be notified.

This takes policies from static documentation to active governance. Policies don’t just exist in isolation – they guide behavior directly within the data ecosystem. 

(We also talk about it in relation to AI Governance in this article: 5 Steps to Operationalize AI Governance With Collibra).

Policy management diagram example. Source: Collibra documentation

How Collibra Data Governance tool powers the rest of the Collibra Platform 

Let’s say that again – Collibra Data Governance is not a separate tool per se. It’s part of the entire Collibra platform, which is a one-stop shop for data management. And the governance tools are designed and built to connect with the rest of the Collibra Platform from the very beginning.

• Collibra Data Catalog – Governance defines ownership and metadata, and the catalog makes that information discoverable for business users.
• Collibra Data Lineage – Governance defines relationships and impact, and lineage visualizes them across systems. (Think of directions on Google Maps, letting you track a route to a destination, i.e., a report or dashboard.)
• Collibra Data Marketplace – Governance certifies trusted assets, and the marketplace delivers them for reuse across the organization.
• Collibra Dashboards – Governance defines KPIs and policies, and dashboards measure and display adoption and compliance.

They’re all intertwined pieces of the same data governance puzzle, and we help our clients put those pieces together as efficiently as possible. You can read more about how that happened in the following case studies:

• Building a Collibra data marketplace for a global life sciences company – where we show how we helped create a data marketplace for HR, starting with business-aligned metamodel design, access request workflow, and stakeholder collaboration
• Management and cataloging sensitive critical data elements in a Swiss bank – where we discuss how we helped our client comply with FINMA Circular 2023/01, starting by establishing a centralized data catalog and automating data governance workflows.
• Collibra Implementation Team for an International Retail Chain – where we show how we custom-developed data lineage, enabled data quality and data governance workflows, and tailored reporting and dashboards as part of a comprehensive Collibra implementation project. 

Want to know how your organization could benefit from Collibra Data Governance tools?

We’d love to chat about your needs and how our Collibra experts can help.

Frequently Asked Questions 

What type of tool is Collibra?

Collibra is an enterprise data intelligence platform that combines governance, cataloging, lineage, and quality capabilities in one environment.

Specifically, Collibra Data Governance is a set of data governance tools and workflows, helping organizations manage roles, responsibilities, policies, and processes related to data management.

Its focus is on people and processes – so that data is not just technically correct but also understood, trusted, and used responsibly across the organization.

Who competes with Collibra?

Collibra’s main competitors include:

• Alation
• Informatica Axon
• Atlan
• IBM Watson Knowledge Catalog
• DataGalaxy

While each platform offers governance features, Collibra stands out for its deep process automation, scalable operating model, and integration across the entire data ecosystem.

From our experience, organizations often choose Collibra when they want governance that’s not just centralized but collaborative and operational.

What are the 4 pillars of data governance?

Depending on the tools and framework, the four pillars typically include:

1. Data ownership and stewardship – defining who’s responsible for data.
2. Data quality – ensuring accuracy, consistency, and completeness.
3. Data policies and compliance – managing rules and standards.
4. Data access and usage (or data management in general) – ensuring data is used ethically and securely.

Collibra supports all four pillars through its integrated capabilities — from operating model design to automated workflows and policy management.

Is Collibra an MDM tool?

No – Collibra is not a Master Data Management tool, if that’s what you mean by MDM.

While both deal with data governance principles, MDM systems focus on maintaining consistent master data records (like customer or product data) across systems.

Collibra, on the other hand, provides the governance framework – defining ownership, policies, and workflows around all types of data, including master data.

In many organizations, Collibra and MDM tools work together:

• The MDM tool maintains the golden record.
• Collibra governs how that record is defined, owned, and shared.