Cognitive computing: Difference between revisions

Revision as of 11:59, 31 August 2022

Revision as of 11:59, 31 August 2022

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== Cognitive analytics ==

Cognitive computing-branded technology platforms typically specialize in the processing and analysis of large, [[Unstructured data|unstructured datasets]].<ref>{{Cite web|url=https://public.deloitte.com/media/analytics/pdfs/us_da_3min_guide_cognitive_analytics.pdf|title=Cognitive analytics - The three-minute guide|date=2014|access-date=2017-08-18}}</ref>

Word processing documents, emails, videos, images, audio files, presentations, webpages, social media and many other data formats often need to be manually [[Tag (metadata)|tagged with metadata]] before they can be fed to a computer for analysis and insight generation. The principal benefit of utilizing cognitive analytics over traditional big data analytics is that such datasets do not need to be pre-tagged.

Other characteristics of a cognitive analytics system include:

* '''Adaptability:''' cognitive analytics systems can use machine learning to adapt to different contexts with minimal human supervision

* '''Natural language interaction:''' cognitive analytics systems can be equipped with a chatbot or search assistant that understands queries, explains data insights and interacts with humans in natural language.

== Applications ==