Data discovery focuses on finding and scanning unstructured data across systems. Unstructured data cataloging goes further by creating governed data assets, linking content-level intelligence to metadata, ownership, lineage, and policies in a central catalog.
Key takeaways
- Unstructured data now represents the majority of enterprise data and is increasingly used for AI, analytics, and machine learning.
- Without cataloging unstructured data, organizations can’t govern risk, ensure compliance, or build trustworthy AI.
- Storage systems and data lakes are not substitutes for a data catalog – they lack context, lineage, and governance.
- Advanced unstructured data cataloging requires content-level intelligence, not just technical metadata.
- Ohalo extracts meaning, sensitivity, and structure from raw unstructured data, while Collibra provides the enterprise data catalog and governance layer.
- Together, Ohalo and Collibra enable safe AI adoption, end-to-end data lineage, and consistent governance across structured and unstructured data.
Unstructured data flows across more and more systems and is increasingly used for AI and machine learning. But most organizations still lack a reliable way to understand, govern, and safely use this data at scale.
For data leaders, this creates a dangerous gap. AI initiatives depend on high-quality data, but without cataloging unstructured data, you won’t be able to explain where it comes from, what it contains, or whether it’s even safe to use.
Compliance teams face similar risks, as sensitive data exposure often hides in raw, unstructured content that traditional data management approaches were never designed to handle.
Classifying unstructured data is an important step in the process, and we talked about it in our previous article. Let’s now look at what happens when the classified data (or its metadata, to be exact) makes its way to a Collibra Data Catalog.
What is unstructured data cataloging?
Unstructured data cataloging is the process of creating visibility, context, and governance for unstructured datasets across the enterprise. Unlike structured data stored in databases and data warehouses, unstructured data does not follow a predefined data model. Its meaning lives inside the content itself.
Cataloging unstructured data means:
- Creating governed data assets for unstructured datasets in Collibra
- Linking content-level intelligence (sensitivity, entities, semantics) to metadata
- Assigning ownership, stewardship, policies, and access rules
- Enabling data lineage from raw content to downstream analytics and AI use cases
Data catalogs for unstructured data are more than data inventories. They provide data context – ownership, sensitivity, lineage, quality, and usage – so that data leaders can manage unstructured data with the same discipline applied to structured data.
A cataloged unstructured dataset should be:
- Discoverable by business and technical users
- Governed by the same policies as structured data
- Traceable across ingestion, transformation, and AI consumption
What’s the difference between storage and cataloging?
Data storage is just keeping the data somewhere – it might be a cloud drive, more often, it’s multiple places locally and in the cloud (which is why managing unstructured data is so complicated).
And no, it’s not enough to just store unstructured data if you want to prevent compliance risks.
A data catalog ingests the metadata from multiple data sources, giving the unstructured data the structure it lacks and letting it be easily discovered and managed, with clear context, ownership, and access policy
A data lake or content management system might store enormous volumes of unstructured data across multiple platforms. But storage alone does not:
- Reveal sensitive data inside files
- Enable data discovery across enterprise data
- Support governance or data protection
- Provide lineage for data processing and AI use cases
Cataloging unstructured data transforms raw data into usable, governed data assets by connecting content-level intelligence with enterprise metadata.
To learn more about storing and cataloging data, also read:
Data catalog vs. data warehouse
Data catalog vs. data inventory
Why unstructured data management fails without a catalog?
Unstructured data management fails when you try to manage what you can’t see, and that’s the inherent nature of unstructured data in the first place:
- Sensitive data is hidden inside documents, emails, and text data – not in tables or columns
- Technical metadata alone can’t identify sensitive or regulated data – and so data access decisions are made without understanding the risk involved
- AI training data is assembled without provenance or controls
- Data strategy is fragmented across tools, teams, and data sources
And without content-level intelligence:
- Collibra becomes a passive inventory
- Stewardship is manual and error-prone
- Lineage stops at file storage, not AI consumption
When you consider the fact that – depending on the research study – up to 85% of enterprise data is actually unstructured, it’s not a fringe issue (quite the opposite).
What are the challenges in cataloging unstructured data at scale?
Cataloging unstructured data at scale is fundamentally different from working with structured data. Traditional metadata catalogs struggle here because they rely on technical metadata alone.
Advanced unstructured data cataloging requires content understanding, data extraction, and data classification before governance can even begin.
And all that’s because:
- Unstructured data grows faster than structured data and often arrives continuously.
- Identifying personal, confidential, or regulated data inside files requires advanced unstructured data processing.
- Unstructured data across multiple repositories, data sources, and global data environments is difficult to unify, and storage platforms and content systems provide volume, but not governance.
- Using raw unstructured data for AI without proper data governance creates compliance and reputational risk. That’s because AI programs depend on unstructured data, but can’t explain provenance or sensitivity
But when there’s a challenge, there’s inevitably a solution (at least, that’s the rule we live by at Murdio). So, let’s talk about it.
How to build a unified data catalog for unstructured data with Ohalo and Collibra
By integrating Ohalo Data X-Ray with Collibra Data Catalog, you can expect a significant reduction in the time and effort required for unstructured data governance processes.
It’s a tool pairing we talk about in our unstructured data series – and this time, let’s focus on the actual data catalog for unstructured data.
Read the previous parts of the series here:
Unstructured data classification
Ohalo specializes in unstructured data discovery, data extraction, and unstructured data processing. It can extract data from raw unstructured content, identify sensitive data, classify data based on sensitivity, and generate rich semantic metadata at scale.
Collibra acts as the enterprise data catalog and governance layer – the system of record for data assets, policies, lineage, and stewardship.
Together, they enable an end-to-end data approach:
- Ohalo processes raw unstructured data. It scans unstructured data across content repositories, data lakes, and operational systems, extracting entities, classifications, and contextual signals from unstructured text, images, and other types of data.
- The extracted metadata is integrated into Collibra’s metadata catalog, creating governed data assets for unstructured datasets alongside structured data. This way, physical assets are linked to your Collibra Data Catalog. And ongoing governance, discovery, and compliance processes are automated.
- Collibra applies consistent data governance, data privacy, data security, and data quality policies across all data – structured and unstructured. Metadata is proactively monitored and auto-updated for new sensitive information findings.
You can also enhance data sharing and analytics by syncing Collibra attribute and asset tagging with Data X-Ray.
Data leaders gain confidence that AI data, analytics inputs, and machine learning pipelines are built on compliant, well-understood, high-quality data.
How does cataloging enable advanced governance and AI safety?
When you properly catalog unstructured data, you can govern it across its full lifecycle and reduce risk in data and AI initiatives:
- Using Ohalo Data X-Ray, you can connect to data across the enterprise at petabyte scale across multiple data types, hybrid and multi-cloud
- Filter the data for relevance to focus only on the data you need
- Audit LLM activity and understand content provenance and sensitivity
- You can also automatically redact sensitive data at scale.
Can you trace the lineage of your unstructured data?
When you link unstructured data processing outputs to Collibra, you can establish lineage from raw unstructured content through data preparation, data integration, and downstream AI and machine learning use cases.
This enables:
- Auditable end-to-end data flows
- Explainability for AI-driven decisions
- Faster issue resolution when data quality or compliance problems arise
Is your AI training corpus safe and compliant?
It is when you use a tool combo like Data X-Ray and Collibra. Cataloging unstructured data using Collibra Data Catalog lets you:
- Identify sensitive data before it enters AI pipelines
- Apply data anonymization and data protection policies
- Control data access based on sensitivity and purpose
- Keep AI training data compliant and ethical
Sensitive data exposure, data privacy violations, and misuse of proprietary information often originate from poorly governed unstructured datasets.
So, if you’re a Chief Data Officer or Data Governance Manager, a very structured approach to unstructured data treatment is not really optional – it’s a must.
To see how it’s done in real life, read this case study: Discovering, classifying and cataloging unstructured data for a European bank.
Bonus: A decision checklist for data governance teams
To assess the level of insight into and management of unstructured data for AI initiatives across your organization, here are some essential questions to answer:
- Do we understand what unstructured data we use for AI?
- Is sensitive data identified before entering AI pipelines?
- Can we trace lineage from raw content to AI outputs?
- Are unstructured assets governed consistently with structured data?
- Is there a clear owner for Collibra configuration and integration?
From unmanaged content to governed intelligence
Let us make this one thing super clear: If unstructured data powers your AI, it must also be cataloged, governed, and trusted. Period.
With Ohalo providing advanced unstructured intelligence and Collibra serving as the enterprise data catalog, you can manage unstructured data at scale and make sure your AI initiatives use data responsibly and safely.
And if you need support discovering, classifying, and cataloging unstructured data, our data governance experts are here to help.
Yes, but only if the catalog is enriched with advanced unstructured data processing. Without tools like Ohalo to extract meaning and sensitivity, traditional catalogs lack the depth needed to govern unstructured data effectively.
AI and machine learning rely on trusted training data. Without cataloging unstructured data, organizations can’t assess data quality, trace lineage, or ensure compliance, increasing the risk of biased, unsafe, or non-compliant AI models.
Collibra provides the enterprise governance framework – policies, stewardship, lineage, and access controls – while integrating enriched metadata from unstructured data sources into a unified data catalog.
Organizations typically start with high-risk or high-value use cases, such as documents containing sensitive data, AI training corpora, customer communications, or regulatory content where governance and compliance matter most.
