Quantzig breaks down the distinct differences between structured and unstructured data in its recent article.

The untapped potential intelligence within unstructured content is driving new practices, new technologies and new roles.

We look at alternatives to relational databases that have emerged to help bring some structure to unstructured data and gain valuable insight by making it semi-structured.

Managing structured data is usually handled well. It’s in the management of unstructured data where the problems arise. There are two primary problems with unstructured data: ...

Unstructured data refers to information that does not have a predefined data model or organized format, making it more challenging to store, process, and analyze compared to structured data ...

But there’s another side to the analytics obsession that needs attention because most analytics applications and technologies are focused more on structured than unstructured data.

Although data-centric enterprises have adequate strategies in place for managing their structured data, current tools are not sufficient for managing the recent wave of unstructured data growth, ...

Structured DB services in telecom are well established from an architecture and service model perspective. Telecom structured data sources are many and varied.

Data should allow a business to learn more about their customers overtime. Without that foundation and proven success in leveraging structured data, the addition of unstructured data is guaranteed to ...

Unstructured data exists in huge volumes, but often actually it is semi-structured with metadata. We lift the lid on unstructured data and key approaches to its storage.