Executive summary
AI and the challenge of unstructured data
Read any reputable technology or business publication and you’ll find incontrovertible evidence that enterprise investment in AI is booming. In fact, the market is projected to grow from $371 billion in 2025 to more than $2.4 trillion by 2032.1 But too often organizations have the vision yet struggle to execute.
Even more frustrating to executives is that those making progress often leave much of AI’s potential untapped. The reason: Up to 90% of enterprise data is unstructured, and even the latest model can’t deliver insights if essential data is locked away in contracts, emails, policies, research reports, presentations, PDFs, meeting transcripts and much more.2
They are the unstructured files that document how your organization works, makes decisions and serves your customers. They contain valuable knowledge. And yet, most of it is invisible to AI.
If you add it up, unstructured data makes up 80 to 90% of all enterprise data. But less than 12% is ever reused or tapped for insight.2 Without structure or context, this data can’t be governed, discovered or trusted. That leads to poor AI outputs: biased results, hallucinations, compliance risk. The cost is more than technical; without unstructured data, incomplete models slow innovation and weaken team confidence in AI-driven decisions.
This ebook explores the missing link in enterprise AI: unstructured data — and how Collibra Unstructured AI helps close that gap. By enriching files with active metadata, automating classification and unifying governance across structured and unstructured data, enterprises can enjoy the full value of their data and build AI they can trust.
Read our factsheet to learn more.
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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
1 Source: https://www.marketsandmarkets.com/PressReleases/artificial-intelligence.asp
2 Source: https://blog.sphereco.com/blog/unstructured-data-5-stats-2
