Artificial Intelligence Drives Disruption in the Analytics Space, Finds Frost & Sullivan

The availability of ever-increasing amounts of structured and unstructured data, along with higher, cheaper and faster computing power, has been essential for the development of advanced analytics. The Internet of Things (IoT) has also boosted the development of the streaming analytics market through sensor-driven, real-time insights into consumer behavior.
a year ago

CAPE TOWN, South Africa, March 12, 2018 /PRNewswire

Advancements in analytics technologies and convergence of technologies have led to transformational breakthroughs in the business landscape, enabling companies to gain a competitive advantage and create new ways of generating revenues. The availability of ever-increasing amounts of structured and unstructured data, along with higher, cheaper and faster computing power, has been essential for the development of advanced analytics. The Internet of Things (IoT) has also boosted the development of the streaming analytics market through sensor-driven, real-time insights into consumer behavior.

  • Advanced analytics is impacting several key industries. For example, analytics will be hugely disruptive to the healthcare space, greatly accelerating the drug discovery process.
  • Image analytics will automate and improve the accuracy of diagnosis by reducing error rates.
  • The automotive segment will depend on advancements in analytics for its evolution and adoption. Machine learning will push the self-driving industry, and emotion analytics will eventually be adapted to determine driver behavior.
  • The advertising and media space will also increasingly use advanced analytics technologies to measure consumer sentiment and perceptions, and adapt product mix and marketing strategy. Sentiment analytics and social media analytics will increasingly be adopted by advertisers to determine ROI.

Analytics companies have typically based their decisions on standardized data available in the marketplace. However, this data does not always provide a complete picture to make the best predictive decisions. Therefore, businesses are now leveraging data from across application areas to create much better predictive models. For example, auto insurance companies can use "orthogonal data" from vehicle telematics systems to make better predictive models to calculate premiums.

Source: Frost & Sullivan

Illustration Photo: Self driving Uber (credits: zombieite / Flickr Creative Commons Attribution 2.0 Generic (CC BY 2.0))

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