Key Takeaways:
- AI and Agentic AI systems need data that is accessible, governed, quality-checked, and certified for specific use cases before they can act on it reliably.
- A modern data catalog goes beyond inventory to create a trust layer connecting business context, lineage, quality scores, and governance policies in one queryable foundation.
- Discovery, traceability, readiness, and trust are the four interconnected capabilities that determine whether your data and AI can be counted on.
Every organization investing in AI eventually hits the same questions. The problem usually isn’t “Does the data exist?” but instead, “Can we actually use this data for the use case? Is it governed? Is it quality-checked? Does it meet our policies? Is it cleared for the specific application we have in mind?”
Sounds simple enough, but getting a reliable answer is not. Most data teams are working with catalogs that were built for discovery — and a data catalog built for AI has to do considerably more than that.
These tools can index tables, surface assets, and help people find what they’re looking for, but when an AI agent or an analyst finds a dataset, what happens next? Can they immediately understand what it means in business terms? Who’s accountable for it? Whether it’s sensitive? How good it is? Whether it’s been certified for use?
If the answer to any of those is “not easily,” then the catalog is doing half the job. What’s needed is something more: a trust layer, where business meaning, quality metrics, governance policies, and lineage are connected on a single catalog foundation, accessible to to people navigating a user interface, to users querying through their AI tool of choice via MCP, and to AI agents programmatically.
That’s exactly what the Data Catalog of the Precisely Data Integrity Suite is designed to provide, and it’s a framework I want to walk through here. Let’s explore the four capabilities that turn the data catalog from an inventory into a governance platform — discovery, traceability, readiness, and trust — and how each one contributes to data your teams and agents can genuinely rely on.
What Does It Mean for Data to Be Discoverable and Trusted?
Discovery is where every data journey starts, but it’s worth being clear about what “discovery” has to mean in the context of AI. Making a dataset findable is the baseline — any modern cataloging tool can index tables. The question is what a user or agent encounters once they’ve found something.
A trust-layer approach to discovery means every asset surfaces with rich, interconnected context:
- A business definition
- Semantic classifications
- Ownership information (who is the domain owner, the data steward, the subject matter expert, the technical owner)
- Related business terms
- Linked governance policies
- Quality scores
- Certification status
When someone asks, “What datasets are used by our customer revenue forecasting AI use case?”, the answer shouldn’t just be a list of tables. It should include the use case as a business asset, the datasets from your data warehouse, the related business terms, organizational objectives, metrics, reports, and governance policies, all linked through a flexible metadata model.
This matters for Agentic AI in a specific way. When an AI agent is connected to the catalog through a Model Context Protocol (MCP) server — a standard that gives agents direct programmatic access to the catalog’s metadata, governance policies, and quality scores — it can traverse these relationships autonomously.
It can search for a use case, discover linked datasets, pull quality and governance scoring for each one, and evaluate those scores against policy thresholds. Without a trust layer underneath, an agent encountering a dataset has no way to know whether it’s fit for purpose. It either guesses, or worse, hallucinates.
It’s also important to remember that not all data is equally important. Only a small percentage of your organization’s data is truly driving C-Suite decisions and core operations.
Identifying and elevating those critical data elements ensures governance efforts are concentrated where they matter most. AI-assisted tooling can help governance teams generate and tag critical data elements directly from catalog context, giving teams a starting point rather than requiring them to build from scratch.
Our data catalog is built for AI, surfacing the existing data, what it means, who owns it, whether it’s trusted, and which assets are critical enough to warrant stricter governance.
How Does Data Lineage Improve Trust and Context for AI?
Once data has been discovered and contextualized, the next question is about impact: where does this data come from, where does it flow, and what does it affect downstream? That’s the domain of traceability, and it’s one of the most practically valuable capabilities a data catalog can provide.
Lineage in this context means a complete, queryable view of how data moves through your organization: from source systems like an S3 bucket or a CSV file, through staging and transformation layers, into your data warehouse, and out to dashboards, reports, or AI models.
Quality indicators travel with the lineage visible at the table level and at the field level, so you can see how data quality scores shift as data moves through pipelines. You can also see which fields are actively used downstream and which are not, which is critical context when deciding where to invest in data improvement.
What makes this more than a visualization exercise is the relationship model underneath. Lineage is one type of relationship in the catalog, but the same framework also captures:
- Use case-to-policy connections
- Dataset-to-quality-rule connections
- Business term-to-technical-asset connections
When an AI agent traces from an AI use case through the related datasets to their quality and governance scores, it’s traversing that same network. Lineage isn’t a separate concern from governance; it’s part of the same fabric.
It’s also worth being honest about the practical limits of automated lineage. Not all lineage can be harvested programmatically. There will be gaps where pipelines or transformations don’t surface through connectors. Our data catalog accounts for this with manual extension capabilities, so teams can fill in what automated harvesting misses without losing the integrity of the overall lineage picture.
Lineage tells you how data moves. But movement alone doesn’t answer whether the data is fit for purpose. That requires a different kind of evidence.
How Do You Quantify Whether Data Is Ready for AI?
Readiness is where a data catalog becomes more than a system of record. It’s the step that moves from “we know where this data comes from” to “we can prove it meets the standards required for this use case.”
There are two components to data readiness scoring: data quality and data governance.
- Data quality scoring evaluates dimensions like completeness, conformity, consistency, and uniqueness, broken down at the rule level so stewards can see exactly which rules are passing, which are failing, and what the scores look like over time.
- Data governance scoring evaluates whether a dataset is well-defined: Has it been described? Are business terms linked? Has personally identifiable information (PII) been tagged? Has ownership been assigned and certified through a workflow?
A score doesn’t mean anything in isolation. It only becomes meaningful when it’s evaluated against a policy.
For example, a governance policy for an AI decision-making use case might require a minimum quality score, a minimum governance score, and a human-in-the-loop review process before an agent is authorized to act on the data. When the scores and the policies live in the same catalog, that evaluation can happen automatically by a human reviewing a scorecard or by an agent checking eligibility programmatically through MCP.
In the demo, this plays out concretely. When an agent evaluates the datasets behind a B2B customer revenue forecasting use case against its governance policies, it doesn’t return a vague “looks fine.” It returns a verdict per dataset: one fully cleared, meeting both the quality and governance thresholds; one blocked, falling short on quality and sitting at partial governance with untagged PII; and one near-miss, passing governance but landing just under the required quality score.
Alongside each, it surfaces the specific steward actions needed to close the gap: remediate the failing quality rules, complete certification, document the PII justification. That’s the difference between an agent that guesses and one that reasons over governed evidence.
When data falls short, readiness becomes a starting point for guided improvement. AI-assisted tooling recommends data quality rules based on catalog context, generates the rule expressions, previews pass and fail records before running rules against the full dataset, and helps stewards standardize values across fields.
Remediation pipelines can then push those improvements directly to the source system, without requiring data to leave its secure environment. The catalog isn’t sitting alongside these pipelines. It’s what they’re built on top of.
Once the data has cleared its quality and governance thresholds, a certification workflow formalizes that readiness — creating a traceable, explainable record that the data has been reviewed, meets policy requirements, and is cleared for use.
Why Data Trust Requires Continuous Monitoring
Certification and scoring represent a point-in-time picture. Data moves, pipelines evolve, schemas shift, new data flows are added, and what was certified last quarter may no longer meet the standards you set. If nothing is watching for that drift, you won’t know until something breaks downstream.
That’s the role of data observability in the trust layer: continuous monitoring that keeps the governance foundation current. Observability rules track volume changes, freshness delays, schema changes, and data drift across critical data elements. It learns what “normal” looks like over time, establishing a baseline from historical data profiles, and surfaces alerts when anomalies occur. This keeps quality and governance scores accurate between formal assessments, so agents and analysts aren’t relying on stale evidence.
The connection back to the Agentic AI context is direct. The quality and governance scores that an AI agent reads through MCP are only as trustworthy as the monitoring behind them. Without observability, a score is a historical artifact. With it, the score reflects the current condition of the data. That’s the difference between a trust layer that agents can genuinely rely on and one that gives the appearance of governance without the substance.
One additional consideration worth noting: the AI capabilities embedded in the catalog, including the AI assistant, rule recommendation engine, and automated metadata generation, follow a Bring Your Own LLM framework. You’re able to use the catalog’s built-in models or inject your own, keeping AI spend under your control rather than locked into a vendor’s infrastructure.
Every layer of the trust layer — discovery, traceability, readiness, and trust — is accessible from within the user interface, via API, or through an AI tool via MCP. The data catalog provides a single, queryable resource that every agent, team, and system can consume consistently.
From Catalog to Trust Layer: The Path to Agentic-Ready Data
The framing I keep coming back to is this: an AI agent connected to a well-built catalog trust layer can answer the question, “Can I use this data?” not by guessing, but by traversing the catalog through policies, datasets, quality scores, and governance posture, and delivering a specific, explainable answer.
- Discovery establishes business context, ownership, classifications, and the semantic layer that makes data understandable to both people and machines.
- Traceability shows where data comes from and where it flows, with trust indicators embedded at every step.
- Readiness quantifies whether data meets the standards required for a specific use case and provides the workflows to close the gaps when it doesn’t.
- Trust, through continuous observability, keeps the entire foundation current as data and systems change.
The Agentic AI Data Integrity Gap — the gap between the data organizations have and the Agentic-Ready Data their AI systems need to operate with confidence — doesn’t close with better models, but with better data infrastructure. The Data Catalog of the Data Integrity Suite, built as a trust layer, is what helps you make that infrastructure real.
Learn more in our eBook: Trusted Data, Powerful AI: Driving Better AI Outcomes through Data Quality and Governance.
