Why Unstructured Data Governance is the Key to Scaling AI

With an ever-changing business climate, companies have begun to shift their focus to unstructured data. In the past, unstructured data was challenging to deal with, considering the volume, governance and compliance, so organisations mainly focused on structured datasets. However, with the rise of generative AI and large language models (LLMs), Reece Williams Griffiths, Field CTO of Collibra, says that we can no longer overlook 80 percent of enterprise content—from transcripts and PDFs to emails and images.In this episode of the Don't Panic It's Just Data podcast, host John Santaferraro, CEO and Head Research Analyst at Ferraro Consulting, talks with Griffiths, also Co-Founder and CEO of Deasy Labs (acquired by Collibra). They also talk about the change brought to Collibra after acquiring Deasy Labs. Governing Structured & Unstructured DataFollowing Collibra’s acquisition of Griffiths firm, Deasy Labs, he explains how this merger is making AI truly achievable for businesses. Deasy became renowned for its goal of simplifying data preparation. With Collibra, it’s leading the development of the tools necessary to create order from the chaos and build a unified AI enterprise.Together, they created the first unified governance and catalogue platform for both structured and unstructured data. This single-hub approach is vital for a future where AI agents treat all data types equally.Griffiths tells Santaferraro that, historically, Collibra, like others, focused only on structured data. Now, by combining Deasy’s capabilities, the platform provides a single entry point and a smooth experience for all data assets.One outcome of a unified data strategy is simplified AI use cases. Since AI applications often need to access both tabular data (structured) and documents (unstructured) to give complete answers, unification offers the necessary routing and flexibility, the Field CTO explains.Preparing Unstructured Data for AITo effectively use a huge quantity of unstructured content, it must be prepared. Griffiths describes a four-layer data preparation funnel that goes beyond simple classification to deep semantic embedding, ultimately creating a Knowledge Product.The talk of the moment is the knowledge data product, which the Collibra speaker says is familiar in the structured data scenario; however, not so much on the unstructured data. “We define a knowledge product with four elements – sensitivity, unstructured data quality, metadata for humans, and metadata for AI...

There are no comments yet.

Please log in to write the first comment.