Cognitive Computing Market Forecast To Be Worth $31Billion In 3 Years
In a new report, IDC forecasts global spending on cognitive systems will reach nearly $31.3 billion in 2019, with a five-year compound annual growth rate (CAGR) of 55%.
Cognitive computing is one of six “innovative accelerators” that will drive digital transformation by opening new revenue streams, creating information-based organizations, and changing the way work is performed, according to International Data Corp. (IDC).
More than 40% of all cognitive systems spending throughout the forecast will go to software, which includes both cognitive applications (text and rich media analytics, tagging, searching, machine learning, categorization, clustering, hypothesis generation, question answering, visualization, filtering, alerting, and navigation) and cognitive software platforms, which facilitate the development of intelligent, advisory, and cognitively enabled solutions.
As both the largest and fastest-growing category of cognitive systems, cognitive applications spending, is expected to approach $13.4 billion in 2019. Cognitive-related services (for example, business services and IT consulting) represent the second largest spending category, while hardware spending will is expected to grow nearly as fast as software spending.
"Unstructured and semi-structured data is fueling a renaissance in the handling and analysis of information, resulting in a new generation of tools and capabilities that promise to offer intelligent assistance, advice, and recommendations to consumers and knowledge workers around the world," David Schubmehl, research director, cognitive systems and content analytics at IDC, said in a statement.
"These cognitively enabled solutions are being developed and implemented on cognitive software platforms that offer the tools and capabilities to extract and build knowledge bases and knowledge graphs from unstructured and semi-structured information as well as provide predictions, recommendations and intelligent assistance through the use of machine learning, artificial intelligence, and deep learning,” Schubmehl said.
