Today, unstructured data accounts for up to 90% of all generated data and is growing at an average rate of 60% each year. This makes it more important than ever for organizations to find innovative ways of managing and analyzing that data so they can utilize it effectively to enrich investigations and make trusted business decisions.
Any type of information that does not have a recognisable, pre-defined data model or is not organized in a pre-defined manner is considered unstructured data, including:
- News stories & articles: Data tends to be very text-heavy and descriptive, containing a wealth of rich information
- Intelligence case files: These reports typically contain lengthy text descriptions and can hold a ton of value
- Emails: While emails include some structured data, the body text fails to follow a recognisable format.
Unstructured data is much harder to use and extract intelligence from, but the deep insights that can be derived from unstructured data makes it a gold mine for forward-thinking organizations.
On the other hand, contextual search helps organizations find the most relevant information from a vast amount of unstructured data, meaning it helps organizations derive the most value from their data.
In today’s highly competitive business landscape, the organizations that will prosper are those that make use of all the data that is available to them. Tapping into unstructured data is central to this, and Contextual Search is making it possible.
Access the full blog and learn more about unstructured data & contextual search, including:
- What is Unstructured Data?
- Challenges in Attempting to Contextualize Unstructured Data
- Applying Context to Unstructured Data
- What is Contextual Search?
- Contextual Search in Action
- Why Contextual Search is the Future
