In this article, we look at structured data, unstructured data, and how semi-structured data brings some order from potential chaos. And brings benefits to organisations that want to gain value ...

Unstructured data is messy, scattered, and multimodal — making it hard to manage and essential to control for successful ...

Companies redefine how unstructured data is governed, integrated and activated for agentic AI, signaling a new era of scalable, trusted AI platforms.

A step above its traditional predecessor is semi-structured data, which arrived in response to the rigidity of table-based formats. Semi-structured data retains some organizational elements of ...

Expanded capabilities include discovering “shadow GenAI,” curating data for GenAI applications developed in-house, and ...

In our data-driven economy, companies generate, collect, and analyze massive volumes of information every day. But managing ...

However, a data warehouse is limited by its inefficiency in handling unstructured and semi-structured data. Additionally, they can get costly as data sources and quantity grow over time.

Imagine AI agents within a company that can independently access and search across all enterprise information to perform complex tasks.

Structured and unstructured data create similar challenges. Put aside the emergence of generative AI for the moment. The foundational challenges of data management that cut across both structured ...

The data that data scientists analyze draws from many sources, including structured, unstructured, or semi-structured data. The more high-quality data available to data scientists, the more ...