The world in which we live and work today thrives on digital data, most of which doesn’t live in a spreadsheet or database. It is unstructured data, which means text, documents, audio and video ...

The differences between structured and unstructured data mean that traditional database systems and modern AI database systems handle information in different ways.

While raw unstructured data doesn't fit neatly in a spreadsheet or database, it holds within it the potential to glean subtle insights into voter emotions, hidden fears and unspoken desires.

More than 80% of data that is readily available to businesses is unstructured – With the growing popularity of embedded sensors and IoT, Quantzig expects this number to increase further in 2021.

Stored unstructured data could be a black hole full of unknown risk. We look at the key dangers to compliance in unstructured data and some ways of mitigating the risks.

Implementing AI in healthcare isn’t just about choosing the right tools—it’s about making them work in the real world.

Milvus, a Linux Foundation AI and data project, for example, is a well-known vector database of choice among enterprises that’s easy to try out because of its vibrant open source development.

For example, a self-service Einstein Copilot-powered chatbot for customers could pull relevant details out of unstructured sources to answer questions by using information from audio and articles ...

But given the complexity of unstructured data, that’s much easier said than done. That’s where our panel of experts comes in. Register now for this free virtual deep-dive into the ins and outs ...

The Unstructured Data Problem Among the biggest challenges involving CCDs is how to extract and exchange unstructured data such as free-text clinician notes and free-text embedded documents.