Bad data doesn't slow AI down. It scales the wrong answer.

Your AI strategy is only as honest as the data underneath it. And right now, that data is quietly failing. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that aren't supported by AI-ready data. Poor data quality already costs the average organization $12.9 million every year. The models don't fail. They confidently scale wrong answers, eroding trust and inviting the regulator's next question.

Collibra Data Quality & Observability stops the bleeding. It helps you monitor, detect and fix data anomalies while connecting every quality signal to the data products, policies and AI models your business runs on.

How Collibra helps

Most data teams don't have a detection problem. They have a fragmentation problem.

Picture this: Last year, a retailer’s forecasting model quietly over-ordered inventory. Their observability tool caught an upstream data drop and fired an alert, but it lacked contextual information. With no lineage to show which models were affected, no policy flags, and no clear owner, the alert sat ignored. Six weeks later, finance flagged a massive inventory variance. The data team spent three grueling days playing detective to reconstruct the timeline. The actual technical fix? It took a single afternoon.

That is the true cost of data fragmentation. Quality lives in one tool, observability in another, governance in a third so every incident turns into a scavenger hunt before anyone can act. Meanwhile the data keeps moving at a million rows per hour.

Collibra closes those gaps so quality becomes something you operate, not something you chase.

It solves the problems that quietly drain enterprise data teams:

• Issues found too late: After the dashboard broke, the model underperformed, or the auditor asked the question
• Alerts with no contextual information: You know data drifted, but not what it breaks, which policy it violates, which model is now suspect and who owns it
• Manual rule-writing that doesn't scale: And that dies with the engineer who wrote it
• No defensible evidence trail: When the regulator asks, you have anecdotes instead of proof

What's new: Data Quality & Observability Cloud

Collibra unifies quality, observability and governance to make sure your data is AI-ready, deliver trusted data products analysts actually use, and report to audits with evidence on demand. Not three separate vendors stitched together. Every quality issue arrives with the information that makes it actionable: the lineage it sits in, the policies it touches, the business terms it maps to and the data product or model it puts at risk.

Collibra closes the loop, end to end: AI suggests the rules you didn’t know you needed. Templates enforce the standards across domains. Dashboards run the operations at scale. Remediation workflows route issues from technical alert to business-owned resolution. And archived break records keep the full issue history audit-ready.

How Collibra DQ&O Cloud works

Our detection engine pairs autonomous, no-code ML that dynamically flags anomalies with the granular precision of custom rules. And now, by letting AI automatically build and deploy these rules for you, Collibra delivers deep, comprehensive data coverage without the operational bottleneck.

Collibra meets every stakeholder exactly where they work. Engineers get programmatic, API-first access. Stewards get an intuitive, plain-language interface to author and approve rules. Business users and AI agents receive instant trust signals, via the data marketplace and MCP, to consume data with absolute confidence. Finally, rule templates instantly scale a local win in one domain into an organization-wide standard, eliminating isolated silos for good.

The data quality program dashboard unifies every data source into a single view, arming you with the metrics required to scale quality and prove ROI. The new lists provide quick filters to manage jobs and monitors and apply actions in bulk instead of browsing them one by one. Finally, you can archive break records on your preferred data storage to preserve the full issue history for audits and external cleansing.

Then it closes the loop. The issue management workflows (available in July) route each issue through your own resolution path with customizable flows, governed handoffs, and an audit trail that's current at every step.

The reason all of this works is that none of it is bolted on. DQ&O Cloud is native to the Collibra Platform, so every signal inherits the catalog, lineage, policies and business terms that already live there. It runs where your data does, pushdown to compute where the data sits, pull-up when you need a dedicated processing layer, on-prem or cloud-native as your environment requires.

Why you should be excited

Whether you are leading the data strategy or managing the daily technical operations, Collibra DQ&O Cloud streamlines quality, observability and governance into a unified, actionable workflow. Here is how this launch transforms the experience for every member of your team.

For the CDO / CDAO

• One source of truth across quality and governance, not three tools that need translation
• A defensible answer to "can we trust this?" for the board, the auditor and the AI steering committee
• AI initiatives that rest on evidence built on data you can actually stand behind
• No second vendor to govern, secure and renew outside your existing stack

For the Data Engineer

• AI-suggested rules cut the manual rule-writing backlog dramatically
• Programmatic, API-first access that fits how you already build
• Pushdown runs checks on your existing warehouse compute for billions of rows, no data egress

For the Data Steward

• Author rules in plain language, no SQL required
• Remediation workflows route each issue to the right business owner, with the contextual information to fix it
• Rule templates make your standards reusable, so quality scales beyond your own team

For Risk & Compliance

• Audit-ready evidence on demand with archived break records preserve the full history
• Quality tied directly to governed policies, so controls map to DORA, the EU AI Act and BCBS 239
• A documented resolution trail: who resolved what, when and why

Use cases where it earns its place

• AI training data trust: Monitor the data feeding your models. Anomaly and rule checks flag drift at the record level before it reaches training and the diagrams show the affected model and its owner, so a problem gets caught in pre-production instead of in a board meeting.
• Data product certification: Every published data product carries a quality score, built into the catalog. Rule templates enforce the same standards across every domain that feeds it, and consumers see trust signals at the moment they choose data, so they don't have to verify quality themselves.
• Regulatory reporting (BCBS 239 / DORA): Diagrams show all critical data elements and their quality scores from source to consumption. Break records are archived as a defensible evidence trail, and the remediation workflow documents the full chain of resolution so "show me the evidence" has an answer ready.

Key takeaways

Quality, observability and governance finally operate as one sharing the same contextual information, same lineage, same policies, same answer. That's the power of one platform: fewer stitches, fewer contracts, fewer versions of the truth. When leadership asks where things stand, there's a single answer, and it holds up. That's what data quality looks like when the business runs on it and it's what Collibra delivers.

Three things worth remembering:

• Detection isn't enough. Collibra closes the full loop: detect, resolve and prove.
• Contextual information is the differentiator. Every quality signal arrives with the lineage, policy and ownership that make it actionable.
• One platform beats three tools. Quality, observability and governance unified means one source of truth, not three.

To learn more check our product page and documentation.

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  Kate Wendell

  Kate Wendell

  Senior Director, Product Management

  Collibra

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  Geoffery Gettings

  Geoffery Gettings

  Senior Director, Software Engineering

  Collibra

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  Marc Campabadal

  Marc Campabadal

  Senior Product Marketing Manager

  Collibra