A look into structured and unstructured data, their key differences, definitions, use cases and more.

IBM's new capabilities will enable organizations to ingest, govern and retrieve unstructured (and structured) data—and from there, scale accurate, performant generative AI.

This approach allows organizations to unify unstructured data with structured data. Unifying these two data types unlocks deeper insights that neither type can offer alone.

Ingestion of your unstructured and structured data from a variety of new source systems including Filenet, Box, Google Docs, and more. Semantic enrichment of your data, creating both vectorized …

On the structured side, data engineering is a well-organized discipline, but on the unstructured side, it hasn't really taken off because there's a tremendous amount of data.

Data is often categorized as structured, unstructured or semi-structured based on its format and schema rules. As its name suggests, semi-structured data shares attributes of both structured and …

“Most data being generated every day is unstructured and presents the biggest new opportunity.” We wanted to learn more about what unstructured data has in store for AI.

Nov 25, 2025 · The goal of unstructured data processing is to transform raw, unstructured data into structured and semi-structured datasets that can improve decision-making, data analytics and …

A data lake is a low-cost data storage environment designed to handle massive amounts of raw data in any format, including structured, semi-structured and unstructured data.

Nov 18, 2024 · Along with traditional structured data, big data can include unstructured data, such as free-form text, images and videos. It can also include semi-structured data, such as JSON and XML …