Cognitive search, powered by artificial intelligence (AI), delivers contextually aware information that is highly relevant to the user’s information quest by understanding the user’s intent and the patterns and relationships that exist within the data corpus.
Big Data’s three V’s – volume, velocity and variety – present significant opportunities for rich insights and appreciable barriers to getting such insights. Organizations suffer from slow and ineffective decisions resulting from anemic insights due to massive dark data of diverse formats trapped in silos.
Cognitive search enables knowledge discovery that is highly relevant to users’ intent by deriving contextual insights from conceptual data. It does this by recognizing the patterns and relationships that exist within virtually any type of information – structured or unstructured, written or spoken. Similar to natural language processing (NLP), this ability to understand data makes it possible to automate manual operations by extracting meaning and performing appropriate actions in real time. Unlike NLP, which focuses solely on linguistics, cognitive search follows a language-independent, statistical approach to understanding human information that is fine-tuned by the use of linguistics.
In the age of big data, cognitive search must be able to access diverse data across different formats (text, video, image and audio) and sources (outside and inside the firewall). The underlying content analytics technology is based upon machine learning, continuously learning and adapting as more data becomes available, to achieve the best possible accuracy.
OpenText™ IDOL is a unified search and analytics platform powered by AI that enables better and faster decisions by accelerating comprehensive insights across text, video, image, and audio data while providing enterprise-class data access security and scalability.Learn more