If multimodal AI support is the architectural challenge of 2026, then the convergence of structured and unstructured data is its operational twin – and the organizations that solve it will create AI ...

Getting enterprise data into large language models (LLMs) is a critical task for enabling the success of enterprise AI deployments. That's where retrieval augmented generation (RAG) fits in, which is ...

When leaders think about data, structured data—such as payment amounts, invoice processing dates and customer names—likely crosses their minds first. Because structured data is objective, it’s ...

The enterprise data landscape is undergoing a fundamental shift as the importance of unstructured data grows in parallel with the rise of generative AI and agentic workflows. Data platforms are ...

Like most other sectors operating in the digital economy, healthcare has become hugely reliant on unstructured data. It’s widely acknowledged that this data type, which doesn’t follow a predefined ...

Globally, unstructured data represents 80% to 90% of the world’s digital information. By 2025, that volume is expected to reach 175 zettabytes. Unstructured data is everywhere—medical images, ...

Data scientists today face a perfect storm: an explosion of inconsistent, unstructured, multimodal data scattered across silos – and mounting pressure to turn it into accessible, AI-ready insights.

Anomalo, the company reinventing enterprise data quality, is adding a major innovation, Workflows, to its second product Unstructured Data Monitoring, introducing a hub for managing and monitoring ...

At the center of some of healthcare’s most important conversations about patient access, provider burnout, and interoperability is a single pain point: unstructured data. When the HITECH Act pushed ...

Structured data, such as names and phone numbers, fits neatly into rows and columns. Unstructured data, however, has no fixed scheme, and may have a highly complex format such as audio files or web ...

Large enterprises in regulated industries, especially in data-rich financial services and insurance, have invested significantly in data governance programs. Other businesses have been catching up as ...