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

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.

When combined, structured and unstructured data provide a complete view of data across an enterprise. And, especially relevant in this current moment, unstructured data can also help …

Nov 10, 2025 · While high-quality structured data remains essential for reporting and transactional systems, unstructured and semi-structured content make up the majority of business …

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

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 …

But just like with structured data, unstructured data has to be governed—classified, assessed for quality, filtered for PII and objectionable content, and deduplicated—so successful strategies …

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.

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 artificial …

It is powered with capabilities to unlock insights from both structured and unstructured data from diverse sources. It supports a variety of workloads and integrates seamlessly with IBM’s AI …