Executive summary
Scaling AI starts with building an unstructured data foundation
Enterprise AI adoption is accelerating. But many initiatives still underdeliver. Why? Not because the models are broken, but because the inputs are.
The biggest blind spot? Unstructured data.
This content—contracts, emails, presentations, PDFs, policies, transcripts—is rich with institutional knowledge yet invisible to AI systems. Lacking structure and governance, it introduces risk, reduces model performance and stalls time-to-value. And by failing to harness it, organizations not only limit AI performance but also overlook one of their most valuable assets: the knowledge and intellectual property created by their own experts.
Collibra closes the gap. By automating metadata enrichment, classification, and control, Collibra transforms unstructured content into governed, AI-ready knowledge assets.
The result:
3% → 20% increase in AI-usable data
78% → 92% AI search accuracy
10,000 files tagged in 20 minutes—not 1 month1
This guide explores how Collibra helps you close the data gap, reduce risk and build AI systems you can actually trust.
Read our factsheet to learn more.
Read now
1 Source: Collibra internal research.
What is unstructured data?
Unstructured data refers to information that doesn’t follow a predefined data model or schema. Unlike rows in a database, unstructured content includes documents, emails, PDFs, presentations, chat transcripts, audio files, images and more. It often contains rich business context. But without metadata or classification, it’s difficult to search, govern or use effectively in AI systems.
Poor data quality
Unstructured content chaos
Lack of governance/ compliance
Skills and/or staffing gaps
