Meanwhile, platforms like Azure AI Foundry are accelerating development, empowering data scientists to build and deploy AI agents at scale. But with this speed comes heightened responsibility. Enterprises face mounting pressure to align rapid AI development with strict requirements for data reliability, privacy and traceability.

That’s why Collibra is partnering with Microsoft to integrate Azure AI Foundry into its AI Governance capabilities, with a particular focus on AI agents. This integration brings enterprise-grade governance — reliability, traceability and compliance — into the AI development lifecycle. From ideation to deployment, organizations gain the clarity, accountability and confidence they need to scale AI responsibly from day one.

“Scaling AI responsibly means finding the right balance between innovation and oversight. That’s why we’re excited to welcome Collibra’s integration with Azure AI Foundry — bringing enterprise-grade governance capabilities such as reliability, traceability and compliance that empower our customers to drive strong business outcomes with confidence,” said Erik Kerkhofs, Microsoft ISV Western Europe Director. 

What makes the Collibra AI Governance integration with Azure AI Foundry unique

The Collibra integration with Microsoft Azure AI Foundry is designed to close a critical gap: the disconnect between fast-moving AI development and the slower, fragmented enterprise governance processes. It brings Collibra’s enterprise-grade data and AI Governance platform, already trusted for data policy, lineage and privacy, directly into the workflows of Azure AI Foundry.

Unlike traditional post-development controls, this integration embeds reliability, traceability and compliance into the AI lifecycle from the start. The result is a seamless bridge between innovation and oversight, enabling governance leaders, data scientists and privacy teams to operate in lockstep across even the most complex AI initiatives. 

Challenge 1: Ensuring AI reliability

AI systems can only be as trustworthy as the data, context and policies behind them. Yet in fast-moving development environments, governance is often seen as a bottleneck, skipped or addressed too late. The result: models that are difficult to validate, explain or trust.

This creates significant risk for governance leaders, who are ultimately accountable for ensuring the organization’s AI meets internal standards and external obligations. But they can’t do it alone. Achieving AI reliability requires close collaboration between governance, privacy, risk, business and data science teams—each bringing a critical piece of the puzzle.

  How the Collibra integration with Microsoft Azure AI Foundry can help: The integration ensures that AI models built and deployed via Azure AI Foundry are connected to enterprise governance from the start. Collibra enables governance teams to define policies, curate approved datasets and provide business context, all of which can be referenced during model development in Azure AI Foundry.  Governance leaders can define rules and standards in Collibra, while data scientists working in Azure AI Foundry can access trusted data and comply with enterprise requirements. Business and risk stakeholders benefit from greater transparency into how models are developed and how decisions are made, ensuring governance and innovation go hand in hand.

Example: A financial services governance leader utilizes Collibra to establish data usage policies that align with regulatory standards. As data scientists in Azure AI Foundry build credit risk models, flagging the use of sensitive attributes and requiring documented approvals. Business leaders can review and validate the model’s scope, and privacy teams are notified of any changes to data classification. Everyone operates from a single source of truth, and the model moves to production with transparency, alignment and trust built in.

Challenge 2: Maintaining AI traceability

As organizations scale AI, they face a growing challenge: understanding what’s been built, how it works and who owns it. AI models often evolve quickly, with iterations spread across teams and environments. When those models become critical to operations, a lack of traceability becomes a major liability.

This is especially true for data scientists, who often build high-value models only to see them disconnected from governance, undocumented or misused without context. It’s not just a governance issue—it’s a productivity loss.

How the Collibra integration with Microsoft Azure AI Foundry can help

Every model or agent developed in Azure AI Foundry is  now registered and ready to be governed in Collibra. For each model, Collibra captures key metadata including model type, performance metrics, data inputs and ownership. For agents, we also register the agent type and its operational instructions.

Most importantly, we automatically link each agent to its underlying model, providing governance, risk, and business stakeholders with a clear view of how AI is being developed and deployed. This unified view includes lineage, input/output traceability, and ownership, ensuring that data scientists get credit for their work while providing the transparency needed to assess usage, manage changes and investigate downstream impacts.

By surfacing this information in one place, Collibra breaks down silos and fosters collaboration across teams—from technical to business—enabling trustworthy and scalable AI.

Example: A supply chain data scientist develops a forecasting model using Azure AI Foundry. As the model and its corresponding agent are deployed, Collibra automatically captures and links key metadata—including model type, performance metrics, data inputs, agent type and agent instructions—into a centralized AI governance registry. Lineage, ownership and dependencies are also recorded.

Six months later, when the agent’s output is flagged for bias, the team can instantly trace the issue back to the underlying model, review its inputs, understand the agent logic, and make targeted adjustments—without guesswork or delay. This end-to-end visibility ensures accountability, accelerates resolution, and supports responsible AI practices.

Challenge 3: Meeting growing and often complex privacy and compliance requirements

Global AI regulation is becoming more demanding—and more specific. From the EU AI Act to industry-specific mandates, organizations must comply and prove compliance across their AI portfolio. The challenge is that privacy controls are often separated from model development workflows, leaving gaps in enforcement and auditability.

For privacy leaders, this creates blind spots that can quickly lead to exposure, especially when sensitive data is used in your AI Use Cases.

How the Collibra integration with Microsoft Azure AI Foundry can help: With Collibra AI governance coupled with Azure AI Foundry, privacy and legal controls are integrated into the AI lifecycle. Sensitive data is flagged, usage is monitored and policies are applied. Using assessments and signoff, privacy teams can define and enforce guardrails that shape how models are built, without needing to manually monitor every project.

Example: A healthcare company’s privacy lead uses Collibra to apply patient data classification rules across all projects in Azure AI Foundry. If a model developer attempts to use a dataset containing identifiable health information, the platform blocks the action and alerts the data owner and privacy team. This prevents policy violations before they occur, protecting the organization from regulatory and reputational risk.

Helping Azure AI Foundry-powered AI use cases to grow with confidence 

The integration between Collibra and Microsoft Azure AI Foundry allows organizations to take a unified, proactive approach to AI governance:

• Governance leaders ensure that all AI initiatives meet internal standards and regulatory obligations.
• Data scientists retain visibility, credit, and traceability for the models they build, without slowing down innovation.
• Privacy teams embed protections into AI workflows and manage sensitive data risk with precision.

Together, this approach transforms governance from a series of reactive controls into a foundational accountability system, aligned with how AI is built and deployed today.

Conclusion: AI use cases powered with Collibra and Azure are now ready to scale

As AI becomes a core driver of decisions, outcomes, and customer experiences, the cost of poor governance will only grow. Collibra’s integration with Microsoft Azure AI Foundry provides organizations with the tools to keep pace—not just with AI innovation, but also with the demands of reliability, traceability and compliance. For leaders tasked with enabling AI across the business, this integration represents a clear path forward: from complexity to clarity, from acceleration to accountability.

The post From innovation to accountability: Collibra Azure AI Foundry Integration helps enterprises govern AI agents and models at scale—without slowing delivery appeared first on Collibra.

As a Product Data Analyst (and proud Data Citizen), I sip that champagne daily — and it’s genuinely good.

Two reasons:

1. It actually helps me do my job.
2. I’m basically our target customer — so using it gives us honest, practical feedback we can build on.

What does “drinking our own champagne” look like?

One of the tools I use the most is Collibra Data Notebook — think SQL playground on steroids.

It lets you:

• Write SQL
• Visualize your results (with beautiful charts)
• Add documentation and context
• Share everything with your team

The magic part? It’s built on top of the Collibra platform, so you can quickly view useful details like schemas, column descriptions and ownership information without leaving your notebook. 

That means I don’t have to go hunting for metadata or open three tabs just to write a join. It makes writing SQL on the go a lot easier — and it dramatically decreases time to insight.

We use Data Notebook every single day. To explore data, answer product questions and create insights that help us make better decisions.

Each notebook is like a mini story:

You start with a question or a hypothesis — sometimes it’s yours, or sometimes a stakeholder kicks things off by dropping their questions right at the top of the notebook. That becomes the business goal. From there, you write a few queries, add some commentary and plot a few charts. Ideally, you answer the original questions. Sometimes you end up with better ones. That happens too.

Once published, a notebook becomes an asset that anyone at Collibra can access, share and collaborate on (as long as they have the right permissions) — just like any document.

How we use Data Notebook at Collibra to make product decisions

Here are two concrete examples of how we use Data Notebook internally at Collibra. We’ll show how we quickly explore data, validate assumptions, reduce time to insights and drive product decisions. 

Example 1: Understanding Collibra AI Copilot usage

We recently announced Collibra AI Copilot, an intuitive chat experience with Collibra that helps users answer data questions like: “What dataset can I use to report on ARR?” or “How is ARR defined?”

User interacting with Collibra AI Copilot from the platform’s homepage interface

Before releasing it to a wider audience, we ran a private preview with customers — and tested it internally, too.

Naturally, we had some questions including are people actually using it? And do they like it?

So, we opened a Data Notebook and started digging.

We tracked usage over time with SQL, visualized the trends directly in the notebook, and gathered insights from early adopters.

A data notebook displaying the business goals and notebook table of contents for ad-hoc analysis around the usage of Collibra AI Copilot, alongside the query history

A chart showing the breakdown of internal and external users of Collibra AI Copilot during the private preview, with a summary provided below

If you look closely at the question_over_time query, as shown below, you’ll see it pulls from a table called fact — a base query we reused multiple times across the notebook. 

That’s one of the things that makes Data Notebook so powerful: you can reference queries inside other queries, like dbt models, but without all the YAML and overhead. Under the hood, it just chains everything together using Common Table Expressions (CTEs) that you can reuse anywhere else. 

An additional chart showing the number of prompts from internal and external users of Collibra AI Copilot during the private preview below the detailed query

A base query referenced as the fact table that has been reused multiple times across the notebook

Sometimes a notebook outgrows itself 

Data Notebook is amazing for ad hoc analysis — for exploring ideas, testing assumptions and answering one-off questions.

But sometimes, an insight gets enough traction that it shifts from analytics to reporting. At that point, it deserves a more robust home.

That’s when we scale.

We usually take one of three paths:

• Move to dbt, and turn our logic into a production pipeline
• Send it to Tableau, and build a dashboard around it
• Or both — dbt first, Tableau after (in most cases)

If we’re going the dbt route, we wrap it into a transformation pipeline, often copy-pasting the queries directly.

If we need a quick Tableau proof of concept, we just export the full SQL from a notebook block — it’s already cleaned and structured with CTEs. Just plug and go.

Complete CTE of questions_over_time, ready to be imported into dbt or Tableau.

What we learned from the AI Copilot analysis

Here are three takeaways from the notebook we built around AI Copilot usage:

1. Users are embracing the chat experience. Engagement with the chat interface (along with feedback) was high from day one.
2. Model A didn’t deliver. Based on early feedback, we moved from Model A to Model B — and saw significantly better results.
3. There was demand across teams across the organization. Other teams wanted a clean dataset and dashboard, so we moved it into dbt and Tableau: we made a clean dataset so that anyone can query it and also published a Tableau dashboard. 

The notebook allowed us to move quickly, surface early insights and adapt in real time.

When it was time to scale, everything we needed was already written.

Example 2: Helping the UX Research team make data-driven decisions

Asset diagrams in the Collibra platform just got a fresh look and feeI, allowing users to have a seamless and consistent user experience across the platform. Functionally, the diagrams let users visualize the flow of their data assets, which is super useful. 

Example asset diagram in Collibra

The UX team is continuing to research how users interact with the diagrams UI to further improve the experience. For instance, they recently wanted to know which buttons in the UI get used most frequently.

To figure that out, they needed data. So, once again, we opened a notebook.

To kick things off, within the notebook we:

• Set the business goal
• Added clear naming conventions (so my colleagues in UX could follow)
• Included context, table of content sources and logic at the top of the notebook

A data notebook displaying the business goals and table of contents for ad-hoc analysis around business diagram buttons

Once the data was organized and transformed, the actual analysis was light — we just queried button usage and sorted by volume.

We added a chart — not because SQL wasn’t enough, but because visuals make it easier to make decisions fast.

A query and chart showing the most used diagram buttons by internal and external users

The results

Top-used buttons:

• Zoom in / Zoom out / Full screen (navigation)
• Label / Preview / Redraw (core diagram actions)

This gave UX Research the data they needed to understand real usage patterns — to shape design priorities based on evidence, not assumptions.

And if anyone wants to dig deeper, they can. Everything’s there in the notebook: source tables, logic, definitions, queries. No mystery math.

And because the notebook is a published asset, it’s searchable in the data catalog. Meaning anyone interested in this topic can request access or reuse some or all of the code. 

Takeaways 

We use Data Notebook every day to explore data, validate product ideas, and move fast — without waiting on dashboards or specs.

It helps us stay transparent, share logic across teams, and scale insights when the time is right.

If you want to learn more:

• View the Data Notebook demo video
• Take Data Catalog product tour featuring Data Notebook

The post Why we use Collibra Data Notebook every day — and how it helps us build better products appeared first on Collibra.

A significant 44% of data professionals report not having access to the right data needed for their jobs, while 41% of executives consider their data to be “substandard”.1,2 Furthermore, a third of executives (33%) identify timely data delivery as a top priority for data operations improvement.2 These challenges are only set to intensify as data complexity continues to grow exponentially.

To make data more accessible, reusable and valuable across your organization, treat data as a product, not just an asset. This approach helps organizations better understand the value and utility of their data, fostering stronger connections between data producers and consumers through internal data marketplaces. 

The benefits of data products

A data product is a reusable and consumable data artifact combined with its business purpose and meaning.  It also provides clear information on how to access the data. This approach to combining data, business context and meaning, and access instructions offers several key benefits:

• Drive strategic alignment: Data products deliver data assets with a defined purpose, owner and measurable impact, making it easy to ensure alignment with organizational goals and for others to understand their value. 
• Strengthen AI-readiness: Data products are essential for advancing AI capabilities by providing high-quality, trusted data with built-in semantic and business context to power AI models and enable autonomous AI systems. They serve as reusable building blocks for AI use cases, offering clear, repeatable inputs that can be rigorously monitored and observed, with data contracts ensuring quality.
• Enable faster insights: Data products empower users with quick, governed self-service access to trusted data, which significantly accelerates data-driven decision-making and promotes data democratization. This enhances self-service capabilities, fosters innovation and improves data literacy. 
• Ensure accountability and compliance: Implementing data products involves assigning clear ownership, enforcing governance policies and maintaining transparency to meet regulatory compliance requirements. This framework ensures the appropriate stakeholders are accountable for data product ownership and compliance. 
• Boost productivity and reduce costs: Data products enhance efficiency and reduce costs by minimizing data duplication, breaking down data silos, reducing reliance on IT and promoting asset reuse. Their well-defined and reusable structure also simplifies the creation of new offerings, which further improves productivity and reduces expenditures. Data products have been shown to deliver data-intensive applications 90% faster and with a 30% reduction in costs, while also lowering risk and data governance burden. 3

Data products are key for scaling trust, insights and innovation across your organization. By treating data as a product, you can align your organization’s data strategy with your business goals—and make your data AI-ready so it can be used to train a model, launch a campaign or report to regulators.

Laying the foundation

But how do you get started? 

Successfully deploying data products requires a structured approach involving the right people, well-defined processes and essential tools.

People: defining roles and responsibilities

Let’s start with identifying the right people and responsibilities – these can be broken down into two key layers of stakeholders:

• Sample roles at the overall data product initiative level include: 
  • Executive sponsor: Provides overarching support and momentum for the data product journey
  • Program manager: Manages the initiative from end-to-end
  • Data catalog team: A centralized team responsible for ingesting metadata into a data catalog tool that enables the creation, management and discovery of data products

• At the data product-level, typical roles include:

• • Data product owner: Curates and refines data product requirements, defines business value, searches for relevant assets and manages the data product lifecycle
  • Technical steward/data product build team: Design, assemble and operationalize the underlying data structures, transformations and integrations needed to deliver a functional and reliable data product
  • Business steward/SME: Provides business context, collaborates on processes and ensures compliance with policies and standards

These roles ensure your data products are grounded in business context, technically sound and actually adopted across your organization.

Processes: Establishing a scalable framework

Once you have the right people, you need to establish a structured and user-focused framework for building and managing valuable data products. One that we recommend consists of three main components:

• Data products governance model: Define the blueprint for managing data products across your organization. This includes best practices, asset management, assigned roles and a clear lifecycle for every data product.

• Governed data product processes: Implement standardized workflows for developing, certifying and publishing data products. These ensure consistency, compliance and alignment with your governance model.
• User-centric design: Put your users first by designing intuitive, role-based experiences. Map out the end-to-end user journey – from data product request to consumption – so that adoption is seamless and sustained.

Together, these components empower organizations to scale data products with clarity, consistency and impact. Next, let’s explore how the right tools can bring this framework to life.

Tools: Equipping teams for success

To effectively implement these processes and the overall framework, certain critical capabilities are essential:

• Flexible data product framework: An adaptable out-of-the-box (OOTB) model that provides valuable guidance, allowing for the ability to extend based on an organization’s specific needs.
• Data marketplace: A centralized portal that simplifies the discovery and request process for data products, acting as the primary interface for consumers.
• Collaboration and feedback mechanisms: Enables users to rate, review, comment, and provide feedback on data products, fostering continuous improvement.
• Data access considerations: Includes robust data sharing agreements, access management protocols, and flexible request fulfillment to ensure timely and appropriate access to data.
• Usage tracking and reporting: Provides useful insights into data product adoption and value, allowing organizations to measure the impact on the business.
• Data contracts: Clear agreements on data availability, update frequency, and support, crucial for scaling and ensuring data quality.

With these critical capabilities in place, organizations are well-equipped to build and scale effective data products.

How Collibra empowers your data product journey

Collibra delivers a complete platform for data and AI governance, giving teams the visibility, control and confidence to turn data into a trusted asset.

For organizations building a data product strategy, Collibra’s unified approach supports three critical areas:

1.Building and scaling trusted, reusable data assets: An extendable out-of-the-box framework and flexible workflows provide guidance and bring consistency to how data products are defined and managed across teams. Integrated data quality and observability and data contracts help ensure data products are reliable and fit for use, while glossaries, KPIs and lineage add the business context and traceability needed to drive understanding and trust. A federated model allows domain teams to manage their products independently while adhering to global governance standards.

2. Maintaining control and compliance compliance: Collibra supports consistent, compliant data use through policy management, data sharing agreements and automated classification and masking that protect sensitive information – all helping teams align with both internal and external regulatory requirements across the organization.

3. Accelerating discovery, access and consumption: A centralized data marketplace, integrated access workflows and built-in feedback mechanisms make it easier for users to find, request  and engage with data products. Collaborative querying and documentation capabilities support teamwork and knowledge sharing, while usage analytics offer valuable insights into adoption and performance.

With these integrated capabilities, Collibra enables teams to deliver governed, high-quality data products that are easy to discover, understand and use – supporting confident decisions and readiness for AI at scale.

New innovations powering data products at Collibra

Collibra continues to enhance the data product experience with new innovations that simplify deployment and lifecycle management, as highlighted in our recent Product Premiere: 

• The acquisition of Raito redefines data access governance for the AI era, providing a unified command center for confidently using data and AI. By unifying Collibra’s semantic graph with Raito’s security graph, organizations can define access policies once and enforce them across various multi-cloud platforms, enhancing visibility, traceability and control while providing faster, frictionless and compliant access to data products.
• A lifecycle progress tracker for data products accelerates the management and publishing of data products, providing clear visibility into each stage, with adaptable phases designed to align with your organization’s requirements.
• Visual widgets on data product asset pages enhance data product consumption and engagement through intuitive displays, such as relation diagrams to increase visibility of dependencies and output viewers for clear inspection and verification of output data.
• Data usage capabilities allow you to identify and analyze how often Snowflake data is queried, track total users and queries and assess data relevance with a “popularity score,” driving prioritization efforts.
• Data Quality & Observability (DQ&O) unified with the Collibra Platform accelerates enforcement of governance by automating technical rule creation, simplifies collaboration through unified interfaces and workflows, and streamlines deployment and maintenance with centralized connections and user management.
• Collibra AI Copilot optimizes asset discoverability using generative AI, allowing data consumers to more easily discover data assets (including data products), look up business glossary definitions and search Collibra product documentation using simple, natural language.

Build trusted data products using Collibra

Instead of serving every user every piece of data, Collibra enables you to curate reusable data products and make them available through a governed, self-service marketplace – bridging the gap between data producers and consumers.

Leading organizations like Toyota Motor Europe use Collibra to scale access to trusted data products across their data mesh. Likewise, SAP relies on Collibra to register, govern, and secure their data products at enterprise scale.

Now you can accelerate innovation and growth by giving teams easy access to high-quality, compliant data products – each with clear ownership, purpose and usage guidance. This helps your organization move faster, stay aligned and scale data and AI initiatives with confidence.

Want to learn more?

• Watch our on-demand webinar to learn what it takes to operationalize data products: Getting started with data products: The people, processes and tools you need
• Take a tour of the Collibra Platform
• Schedule a Collibra demo to accelerate your Collibra journey

The post Getting started with data products: A practical introduction appeared first on Collibra.

Consider a simple workflow: a business term needs clarification. The data steward first asks the subject matter expert who then needs to know when it has been approved. Instead of the delays associated with a chain of emails and platform-specific comments, imagine if you could get a single Slack notification with the right people tagged, the right discussion happening in a thread, and decisions made in minutes.

That’s why we’re introducing a two-way Slack Integration and a new Notification Center.

Now you can bring the power of Collibra right into your daily communication flow, making it easy to stay on top of your governance responsibilities.

Slack Integration: Governance, right where you work

Integrate Collibra directly within your Slack workspace to boost efficiency and collaboration by:

• Receiving real-time notifications in Slack: Stay up to date on mission-critical tasks that require your attention
• Commenting directly: Immediately reply to resource and assessment comments
• Finding Data Assets easily: Search the data catalog and share assets easily

Notification Center: Your new in-app inbox

To provide a complete notification solution, we’re also launching a central Notification Center to complement the two-way Slack Integration. Now you can manage when and how you’re notified from one centralized location, allowing you to:

• Mark notifications as Important: Quickly review relevant notifications by starring them
• Avoid communication fatigue: Manage the frequency you would like to be notified for a given notification type
• Personalize notification preferences: Choose your preferred channel – email, Slack, or Microsoft Teams (coming soon!)

Using a centralized notification hub like this, now you can get all your important alerts in one place—customized for your preferred delivery channel and delivered how and when you want.

Meeting users where they work

With Collibra inside of Slack and the new centralized Notification Center, teams never miss a task or get buried in inbox clutter. 

And coming soon: Collibra will integrate into other collaboration tools you use every day, such as Microsoft Teams.

To get started with our new notification features, watch our demo video for a quick overview or dive into the product documentation for detailed guidance.

The post Enhance collaboration with our new Slack Integration and Notification Center appeared first on Collibra.

Together, they help data leaders focus ongoing data stewardship efforts and drive greater adoption.

Introducing Data Usage for Snowflake

Data Usage for Snowflake pinpoints the most popular data assets to help data teams prioritize governance efforts.  Using real-time insights into which  Snowflake data is being used, you can now find your most popular and frequently used data assets, empowering your team to:

• Prioritize with precision: Immediately identify your most critical datasets based on actual usage
• Maximize your impact: Apply data quality rules, enrich business metadata and build governance workflows for the data that provides the most value
• Increase efficiency: Stop wasting valuable time on low-use or irrelevant data and focus your resources where they will make a difference

Data Usage answers the questions about your data assets: Which data is most valuable? Where should we focus our efforts? Which datasets are critical for business operations? 

The power of Usage Analytics

Knowing what data is important is one side of the coin, but knowing who is using the platform and how can also help drive greater adoption. 

With Usage Analytics, now you can get real-time insights into platform and asset usage to help understand user behavior, drive adoption, and continuously improve your data culture. See which users are engaged and what assets they are interacting with, so you can ensure your data citizens are getting the most out of your data intelligence investment.

Usage Analytics answers the fundamental questions about your data governance program: Is it being adopted? Is it providing value to our users? Which departments or teams are most and least engaged?

Conclusion 

Collibra Usage Analytics and Data Usage for Snowflake together empowers data leaders to understand their users and drive greater adoption. By linking platform engagement with data asset popularity, you can now understand how people, process, and data all work together. This lets you optimize resources, build a more resilient data culture, and prove the ROI of their governance program by proactively aligning their efforts directly with business activity.

The post From insight to impact: The powerhouse duo of Usage Analytics and Data Usage in Collibra appeared first on Collibra.

Based on your feedback, we’ve made some big improvements

Our goal was simple: create a Community experience that’s easier to navigate, more enjoyable to use, and even better at helping you connect, collaborate and find the answers you need. It’s your community, reimagined. Here’s what’s new:

A modern, updated design

We’ve refreshed the look and feel of community.collibra.com to make navigation more intuitive and the overall experience cleaner and faster. Whether you’re new to the Community or a longtime member, finding what you need is now easier than ever.

A better forum experience

The discussion forum is the heart of our Community, so we focused on making it easier to browse, post and get responses. You’ll notice improved layout, faster loading times and clearer categories to help you jump into the conversations that matter most. To get started, follow these directions to create a discussion post in the Community. 

More robust customer user groups

We’ve strengthened our customer user group experience with clearer group pages and a better experience for sharing resources and ideas. These groups are a great way to connect more deeply with peers who share your role, region or product focus. Read more about our user groups in this blog, From networking to knowledge: The impact of Collibra User Groups.

Improved email communications

You’ll now receive more relevant email updates and notifications, so you won’t miss a reply, event or announcement. These updates are designed to keep you informed without overwhelming your inbox.

A new Adoption Corner

We’ve added a new space called the Collibra Adoption Corner—your go-to resource for driving Collibra adoption across your organization. It’s full of helpful tips and resources to set you up for success.

A quick note on your points and badges

We’re currently working behind the scenes to update our gamification platform. As a result, your points and badges won’t be visible right away—but don’t worry, we’re not starting from scratch. All of your information will be restored once the upgrade is complete in late 2025.

I hope you’re enjoying the new Community as much as I am. We’re always looking for ways to make your experience even better—and your feedback plays a big part in that. Please take a few minutes to complete our Bi-Annual Community Survey and help us continue to improve.

If you have any questions or need help navigating the new site, don’t hesitate to reach out to collibra@community.com—we’re always here to support you.

See you in the forums!

The post Your feedback, our improvements: A better Community experience appeared first on Collibra.

Risk can be managed in a multitude of ways. Fortunately, NIST, in conjunction with both private and public organizations, has helped take the guesswork out of where to start. The NIST AI Risk Management Framework (RMF) and associated playbook lay out a comprehensive methodology to reduce AI risks. In the AI RMF core (arguably the most important aspect of the framework) sit four functions that should work in a continuous cycle of advancement and improvement: Govern, Map, Measure and Manage.

Govern, the center piece of the RMF, encourages agencies to build a culture of risk management and ensure that it is truly a cross agency effort. Map helps bring context to AI risks and opens lines of communication between stakeholder groups to understand and identify potential risks. Measure has the agency actively assessing and analyzing risks and their impact to ensure the proper data is collected for a complete picture. Finally, Manage takes the output of the three prior functions to effectively act upon risks that will or have become too great and should be eliminated.

Collibra AI Governance and the NIST RMF

Collibra AI Governance has been deployed by a variety of agencies to help quickly and systematically catalog and govern both existing and new AI use cases, helping to ensure safe and reliable AI. And to get agencies up and running even faster, Collibra now offers a complete NIST RMF assessment out-of-the-box. This is an exact 1:1 of the NIST RMF that agencies can start using immediately, or if desired, can customize to their specific needs. The NIST RMF is a valuable resource for any organization that is developing, deploying, or using AI systems.

However, AI doesn’t start and stop with a risk assessment – it starts with data. Data feeds AI, and no matter how good of an AI framework you have, without great, high-quality data feeding that AI, you’ll never be able to deliver innovative or reliable AI use cases. AI governance should be a natural extension of your data governance efforts. The same rigorous standards and controls put in place for data should be applied for AI. Collibra AI Governance was built with that in mind and works seamlessly with the functions of the NIST RMF to help ensure reliable and trustworthy AI. 

• Govern: Collibra AI Governance was built with sole purpose to provide organizations a systematic approach to not only mitigate the risks of AI, but use governance as an enabler for innovation. Not only does Collibra help you govern your AI, but also data from across your organization
• Map: AI requires a broad set of stakeholder input for success. Collibra AI Governance was developed with both technical and business users in mind, helping both sets of users easily provide context to use cases and their known or potential risks. Collibra AI Governance also comes with full lineage capabilities to visualize data being fed into AI as well as the AI outputs
• Measure: Measurements of critical metrics, including risks, should be a continuous and ongoing process. Even after AI models are deemed safe to deploy, it is imperative that models are measured as data may have changed over time, leading to increased risk of unexpected model behavior which could lead to negative outcomes. With Collibra AI Governance, agencies can create scheduled AI use case reviews to ensure everything from outputs to legal requirements are being regularly checked
• Manage: Collibra AI Governance provides a single location to document and review all aspects of the AI use case for agencies to make informed decisions as to whether internally developed and third-party AI risks are too great or do not meet agency standards. Remediation plans can also be crafted and documented if the need to address risks if needed

Collibra AI Governance combined with the NIST AI RMF provides agencies with confidence needed to innovate safely with AI. To learn more about Collibra AI Governance, read this factsheet. Want to get hands-on? Take a product tour here. 

The post Deliver AI with confidence: Collibra AI Governance and the NIST AI RMF appeared first on Collibra.

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As AI adoption accelerates, organizations face mounting challenges in managing compliance, traceability and lifecycle efficiency. According to Gartner’s 2023 AI in the Enterprise Survey, it takes an average of 8.2 months to move an AI prototype into production. And only 7% of companies can do it in under three months.

AI model governance: Introducing new capabilities for greater control and accountability

As AI development accelerates, many organizations struggle with visibility and accountability. Models are created across teams, tools and environments, with inconsistent documentation and unclear ownership. That makes it difficult to meet regulatory requirements, align with business objectives or ensure responsible outcomes.

To address this challenge, the Collibra AI Model Registry provides customers with a unified platform to manage all AI models while linking each one to its underlying data, policy context and business stakeholders. Additionally, our expanded integration across the AI ecosystem—with new support for Azure AI Foundry and MLflow—builds on existing connections to AWS Bedrock, AWS SageMaker, Databricks Unity Catalog, Google Vertex AI, Microsoft Azure ML and SAP AI Core. 

The result: Data and AI professionals get transparency from day one and support for proactive risk and compliance management throughout the model lifecycle.

In parallel, we’ve introduced lifecycle sign-offs, allowing teams to embed formal checkpoints across the development process. These structured reviews capture ownership, rationale and evaluation outcomes, ensuring models are reviewed and approved before moving forward. And the benefit is twofold: A more predictable path from experimentation to production, without compromising on trust.

With the introduction of the AI Model Registry,  we’re strengthening the power of AI governance already embedded in the Collibra platform.

Looking ahead: AI agent oversight in preview

AI agents are increasingly used to automate service interactions, business processes and content creation. But unlike traditional models, agents are autonomous and act autonomously on behalf of the enterprise, often making decisions without human intervention. Whether developed in-house or purchased from third parties, the use of agentic AI introduces a new level of risk, demanding new forms of governance and oversight.

Get a preview of the Collibra AI Agent Registry 

To support the rise of autonomous systems, the Collibra AI Agent Registry is now available in preview. A tool-agnostic capability, the AI Agent Registry enables organizations to associate each AI agent with its specific business context, designated owner, lifecycle stage and policy alignment, eliminating blind spots and laying the groundwork for a reliable, traceable and compliant use of AI agents within enterprise environments. 

The preview launches with an initial integration with Azure AI Foundry—our first supported environment for agent oversight—and additional integrations will be added over time based on customer feedback to expand coverage and ensure alignment with enterprise needs.

The post Extending traceability across the AI model lifecycle with Collibra AI Model Governance appeared first on Collibra.

Traditional approaches often force teams to choose between robust governance capabilities and deployment simplicity, creating friction that slows strategic data and AI initiatives. The challenge, however, extends beyond mere technical implementation. From a business perspective, the capacity for data teams to focus on value creation, and ensure complete visibility into data flows across hybrid and multi-cloud environments, is no longer a nice-to-have.

Today, we’re addressing this strategic imperative through two complementary advances that strengthen the unified governance foundation of the Collibra Platform by introducing Collibra Cloud Sites and OpenLineage integration. 

These enhancements support our core mission of accelerating every data and AI use case through unified governance, removing traditional barriers between infrastructure complexity and strategic capability deployment. 

Discover the Collibra Platform. Take a tour

Collibra Cloud Sites: Infrastructure-free governance

Collibra recognizes many of our customers might not have the extensive infrastructure or platform engineering expertise needed to manage an on-prem application connecting to their diverse data sources. 

Collibra Cloud Sites represent a fundamental shift in how organizations deploy enterprise data governance. As a fully-managed SaaS offering, Collibra Cloud Sites eliminate the traditional infrastructure burden that often delays governance initiatives, offering these key benefits:

• Onboard faster: Get the full advantages of the Collibra Platform with a significantly reduced time-to-value, helping you achieve your data goals quicker
• Reduce infrastructure overhead: Avoid the complexities of managing servers and software. Enjoy a fully managed Collibra Platform deployment, with infrastructure upkeep handled by Collibra
• Simplify IT operations: Leverage our deep platform engineering expertise. We handle the security, reliability, and performance, so your IT team can focus on strategic initiatives

Collibra Cloud Sites simplify deployments, ensuring you can harness the power of the Collibra Platform with unparalleled ease and efficiency.

OpenLineage integration: Comprehensive data traceability

Data lineage is a sometimes underappreciated but integral part of data governance. In fact, if you want to make smarter decisions, ensure compliance and resolve data issues at lightning speed, understanding your data’s full journey is key. 

At Collibra, we want to accelerate and strengthen every data and AI use case so everyone in your organization can trust, comply and consume data. That’s precisely why we’re thrilled to announce a significant enhancement to Collibra’s capabilities: a powerful new integration with OpenLineage. 

OpenLineage: A leader in data lineage  

OpenLineage is “an open framework for data lineage collection and analysis.” A new generation of powerful, context-aware data tools and best practices, OpenLineage enables consistent collection of lineage metadata, creating a deeper understanding of how data is produced and used.

Our integration with OpenLineage means Collibra customers will be able to capture and expose technical lineage from even more data sources. 

As part of the launch we will use OpenLineage to bring technical lineage from jobs orchestrated in Apache Airflow and AWS Glue—and more in the future. 

What does it mean for data and AI professionals? Well, here are four big benefits:

• Quickly trace your data: Get self-serve answers to “where did this data come from?” in minutes
• Boost compliance & auditing: Easily prove data handling and meet regulations like GDPR and the EU AI Act
• Simplify impact analysis: Understand what’s affected by changes, fast
• Streamline onboarding: New team members can visualize data flows immediately

By integrating with OpenLineage, Collibra continues to empower organizations with comprehensive data traceability. This new capability simplifies complex data environments, ensuring a more complete and accurate picture of your entire data ecosystem.

Take advantage of Collibra Cloud Sites and OpenLineage

While Collibra Cloud Sites eliminate deployment friction through fully-managed SaaS delivery, our OpenLineage integration expands technical lineage capture across modern data orchestration platforms. Together, they create a more comprehensive governance foundation that can scale with your organization’s ambition.

How to get started

Existing Edge users can seamlessly add Collibra Cloud Sites through platform settings, while new deployments can leverage the fully-managed option immediately. You’ll be able to request a Collibra Cloud Site directly from your settings with just one click, and the provisioning process will take up to 2 business days. Check out the Collibra Cloud Sites documentation.

Ready to integrate OpenLineage into your Collibra Platform instance? See our documentation on how to bring metadata from Airflow and from AWS Glue into Collibra.

Discover the Collibra Platform. Take a tour today.

The post Enhancing unified governance: Collibra Cloud Sites and OpenLineage integration appeared first on Collibra.

Let’s take a step back and look at the bigger picture. This is where we are:

• The decoupling between storage and compute is here to stay: Open table formats like Iceberg let data platforms run data workloads wherever the data is stored. It’s more cost effective, provides more flexibility, and solves the age-old data silo problem. With Databricks’ launch of LakeBase, this architecture is now available for Postgres as well. With this, the need to move data across platforms is quickly going away. The ETL market is shrinking, data movement is becoming more natively built-in into data platforms (eg Snowflake Openflow and Databricks Lakeflow) and zero-copy is the new norm. Is this why Informatica sold?
• The age of multi-structured data has arrived: Structured and unstructured data used to be managed separately — those times are over. AI applications and AI agents now combine structured and unstructured data. Data platforms are quickly expanding their capability to process this multi-structured data. But governing unstructured data is an unsolved problem. Organizations need to discover, catalog, curate, monitor, govern access, and ensure the quality and trust of unstructured data, and do it at scale. Everything that has been built for structured data over the last 10 years now has to be built for unstructured data. 
• The modern data stack is dead: Massive venture capital has flowed to the modern data stack. Every micro-category, from catalog to transformation, orchestration, observability, access and lineage… now has a long list of small point-products. Very few of these vendors have any scale. More importantly, it’s not what organizations are looking for. The result is a fragmented, expensive data stack that’s hard to manage and doesn’t deliver enough business value. Some of these vendors will continue to consolidate (we’ve seen 4 catalog companies be acquired in the last few months), and many will go away. Organizations need simpler data stacks based on fewer, but broader platforms that maximize business value for the lowest TCO.
• The value of data is moving up the stack to the semantic and governance layer: At Snowflake’s conference, Sam Altman said “the perfect AI is a very tiny model with superhuman reasoning and 1 trillion tokens of context.”At Databricks’, Ali Ghodsi said that whoever owns governance in the enterprise owns the future, because of AI. We agree! For AI and agents to work in production, context is king. For data, that means business context must be codified through a semantic layer. The semantic layer combines rich business context with active, executable governance. We are seeing it with customers — data products are quickly becoming the best-practice to deliver trusted data, and knowledge graphs are seeing a resurgence. The major business application platforms also understand the value of their business semantics: SAP is going all-in on data products with Business Data Cloud and Salesforce is betting on Data Cloud to help deliver on its agentic vision. 

 The big question is – where does this take us?

1.The fight for data and AI workloads intensifies. 

As data platforms continue to decouple storage from compute, the fight for that compute is only intensifying, especially as compute workloads move from data to AI. The recent announcements at Databricks’ and Snowflake’s user conferences made that very clear. It is highly unlikely that Microsoft, AWS and Google will just give those data and AI workloads away.

In this fight, governance is quickly becoming a key differentiator. AI is driving an exponential increase in data consumption while fueling new applications that are often autonomous. This requires having confidence in that data to run these workloads at scale in production. It’s no surprise that Ali Ghodsi started his keynote with governance. We fully agree with his message that “governance needs to go across all data, and needs to be unified across every use-case”. All of this is a good thing for organizations as competition drives innovation and cost efficiencies.

2. The risk and cost of governance fragmentation.

History is repeating itself. The same years of pain we lived through dealing with data silos and data fragmentation will now move up the value chain to governance silos and governance fragmentation. As data and AI workloads are deployed across multiple platforms, each with their own governance features of variable maturity, fragmentation becomes almost a certainty. The need for Unified governance across all data, every use-case, made usable for all users can quickly become a nightmare. The stakes with respect to quality, security, privacy, visibility, and efficiency are too high for governance to be an add-on or an after-thought. 

Luckily, we know the solution. We’ll deal with governance fragmentation in the same way we dealt with data fragmentation – by decoupling governance from compute. Databricks has led the industry by using open standards to deliver an architecture that is more scalable, more cost-effective and more flexible. Their approach won! At Collibra – we’re doing the same: we’re building a tightly integrated, but decoupled unified governance layer for data and AI, to deliver a more scalable, more cost-effective and more flexible data estate. We’re also equally leaning into open standards such as OpenLineage and Open Data Contracts to make this a reality.

At Collibra, we continue to innovate with intention — to help our customers confidently use data and AI, anywhere, at any scale. It’s never been a more exciting time to be in data!

The post Data, AI, market consolidation, platform wars and the cost of governance silos appeared first on Collibra.