Most of the data your AI needs probably isn’t sitting neatly in rows and columns.

In fact, 80% of enterprise data is unstructured. It’s buried in documents, contracts, emails, chats, presentations, call transcripts, PDFs, images, audio files, video files, support tickets and clinical notes. And that’s the problem with unstructured data: it contains the business context AI needs, but engineers and data scientists often can’t tell which information is safe, high quality or relevant for their use case.

The results are familiar: 

- AI projects stall before production

- Data prep consumes engineering time

- RAG accuracy suffers because files lack metadata

- Chatbots miss important context

- Trust erodes before adoption has a chance to grow

Unstructured data holds enormous business value, but most organizations can’t discover it, understand it, govern it or prepare it for AI at scale.

AI raises the stakes. Large language models, retrieval augmented generation (RAG) and AI agents all depend on access to trusted, relevant and well-contextualized information. But when the underlying content lacks metadata, ownership, quality signals, permissions or business meaning, AI systems can retrieve the wrong information, miss the right information or expose data that should stay protected.

That’s why unstructured data has become one of the biggest blockers to AI value.

See how Deasy Labs — a Collibra company — can turn a mass of content into the perfect data slice for AI projects.

What is unstructured data?

Unstructured data is information that doesn’t follow a fixed, predefined data model. Unlike structured data in databases or spreadsheets, unstructured information doesn’t fit cleanly into rows, columns and fields.

Common examples include, documents, PDFs, emails, messages, customer support tickets, images, audio, video, contracts, legal files, research notes, reports, presentations, slide decks, web pages, knowledge based articles, call transcripts and meeting summaries just to name a few.

This doesn’t mean the data has no structure at all. A contract has sections. An email has a sender, recipient and subject line. A transcript has speakers and timestamps. But the meaning is locked inside content rather than organized in a way machines can easily classify, govern and retrieve.

That distinction matters because AI needs more than access; AI needs understanding.

Structured vs unstructured data

The difference between structured vs unstructured data comes down to how the information is organized and how easily systems can process it.

Structured data lives in a defined format. Think customer records, transaction tables, inventory counts or account balances. It’s easier to search, query, classify and govern because each field has a known place and expected meaning.

Unstructured data is more fluid. A customer complaint might appear in an email thread, a call transcript or a PDF attachment. A product requirement might live in a slide deck, a chat message or a shared document. The same topic may appear across hundreds of files with different formats, owners and permission structures.

That messiness is exactly why unstructured data analytics is so valuable. It can reveal patterns across customer feedback, employee knowledge, legal documents, claims, research, operations and product information. But without metadata, governance and enrichment, that same messiness creates noise.

And AI does not need more noise.

Why unstructured data stalls AI

AI teams often run into the same problem. The use case looks promising, but the data foundation isn’t ready.

Maybe the content is scattered across too many systems. Maybe teams don’t know which documents are current. Maybe sensitive information is mixed into files with no clear classification. Maybe permissions don’t follow the content when it moves. Maybe nobody can tell which data is authoritative, duplicate, outdated or approved for AI use. That creates three major problems.

1) First, unstructured data is hard to discover. Teams can’t prepare what they can’t find.

2) Second, it’s hard to trust. Without metadata, lineage, ownership and quality indicators, teams can’t know whether the content is accurate, current or fit for purpose.

3) Third, it’s hard to govern. Sensitive data, regulated information and proprietary knowledge can spread across files, systems and workflows without consistent controls.

For AI, these gaps become production risks. A chatbot can retrieve outdated policy language. An agent can act on the wrong version of a document. A model can expose confidential information. A search experience can return irrelevant results because the content lacks context.

The output looks like an AI problem. The root cause is usually an information management problem.

Learn more about unstructured data in the age of AI.

Why manual data prep can’t keep up

Preparing unstructured data manually is slow, repetitive and expensive. Teams spend more time cleaning, tagging and organizing data than building AI applications. And each new use case can feel like starting over.

AI engineers may spend weeks trying to identify which files are relevant. Data scientists may need to classify thousands of documents before a model can use them. Governance teams may need to review access rights, sensitive content and policy implications by hand.

That approach doesn’t scale.

Deasy’s unstructured data approach is designed to automate content discovery, tagging and enrichment up to 30x faster than manual processes or open-source solutions, with 90%+ classification accuracy across unstructured files using a proprietary mixture of LLM and ML models.

That means teams can spend less time preparing content and more time building AI applications that work.

Metadata is the missing layer

If unstructured data is the raw material, metadata management is what helps make it usable.

Metadata describes the data. It can identify who created a document, when it was updated, what business domain it belongs to, whether it includes sensitive information, which policy applies, who owns it and how it relates to other assets.

For structured data, much of this context can come from schemas, tables and systems. For unstructured information, context has to be extracted, enriched and connected.

That’s why metadata management tools and an effective metadata framework matter for AI. They help transform scattered content into a more meaningful inventory of knowledge assets. With the right metadata, AI systems can retrieve better information, apply the right policies and provide more relevant responses.

This is where active metadata becomes especially important. Metadata can’t sit still while content changes. It needs to update as documents move, policies change, permissions shift and AI use cases evolve.

From unstructured content to governed knowledge

Organizations need a repeatable way to move from unstructured content to governed, AI-ready knowledge.

That journey typically includes four steps.

1. Discover what exists: Teams need visibility across documents, files, messages and other content sources. Discovery should include location, format, ownership, sensitivity and usage context.

2. Enrich the content: Content needs metadata that describes what it means. This includes business terms, classifications, topics, entities, relationships and links to relevant policies or domains.

3. Govern access and usage: Teams need to define who can access content, what it can be used for and whether it’s approved to power analytics, AI search, retrieval or agentic workflows.

4. Deliver AI-ready knowledge: Once enriched and governed, content can support AI use cases with stronger relevance and control. That can improve retrieval, reduce irrelevant results and help teams move from pilots to production with more confidence.

This discovery-to-delivery motion is the key. AI teams don’t need more content dumped into a model. They need content prepared for use.

Turn messy content into AI-ready knowledge

To turn unstructured data into AI-ready knowledge, organizations need capabilities that discover content across disconnected systems, extract and enrich metadata automatically, classify sensitive and regulated information, connect content to business terms, govern access and prepare content for search, retrieval and AI agents.

Deasy Labs and Collibra help organizations transform unstructured data into governed, enriched knowledge assets that AI systems can use with greater confidence. By connecting unstructured information to metadata, ownership, policies and controls, Collibra helps teams make more content discoverable, understandable and ready for AI.

This matters because metadata management is now central to AI success. Models and agents need context to perform well. Organizations need control to reduce risk. Leaders need evidence that the information powering AI can be trusted.

For teams working to unlock the value of documents, messages, transcripts and other complex content, Deasy Labs helps make unstructured data ready for what AI needs next. Learn more about how Deasy helps organizations transform unstructured data for AI.

Discover Deasy Labs.