Cognitive Analytics Market Size is Projected to be Around US$ 13 Billion By 2022

The Cognitive Analytics Market is segmented on the lines of its technology, end-user, deployment model, vertical and regional. Based on technology segmentation it covers NLP, machine learning, semantic analysis, and automated reasoning. Based on end-user type segmentation it covers SMBs and large enterprises. Based on the deployment model it covers Cloud and On-Premises. Based on verticals segmentation it covers healthcare, BFSI, consumer goods and retail, aerospace and defense, telecom and IT, energy and power, travel and tourism, media and entertainment and education and research.

The Cognitive Analytics Market is expected to exceed more than US$ 13 Billion by 2022 at a CAGR of 33% in the given forecast period. The scope of the report includes a detailed study of global and regional markets on Cognitive Analytics Market with the reasons given for variations in the growth of the industry in certain regions. Know More

The challenge with machine learning in manufacturing isn’t always the machines; it’s often the people as well. For nearly 30 years, the industry has talked about the coming of one big interconnected network of plants, supply chains, enterprises and technology that creates a digital-lean-manufacturing nirvana. While we’re well on our way to reaching that mountain-top of just-in-time delivery and zero waste, a risk-adverse culture has slowed the implementation of machine learning.

Up until this point, machine learning in the Industrial Internet has focused on optimizing at the machine level. We have access to a ton of data about machine function and productivity that we have used to run our machines at full capacity for as long as possible and predict many maintenance issues. But now it’s time to take the next step and start looking at network-wide efficiency. Know More

AI and Cognitive Computing – spearheading Enterprise Digital Transformation

Artificial Intelligence, robotics and machine learning concepts are changing the parameters of data extraction and analysis. With the onset of digital transformation, there’s a great need to process huge volumes of data into useful insights.

But with traditional data analytic methods, businesses are not able to process the entire data, especially that which is in the form of images, videos, and the human voice, collectively known as ‘dark data’. To process such kind of data, organizations need cognitive computing. Know More

Digital transformation is bringing the world closer and is highly responsible for driving all activities within an enterprise. It is bringing agility into businesses and reducing the overall cost of some kind of ownership also. If we closely look into this phase of transformation, cloud technologies are the highest adopted technology stream in digital enterprises. The very reason for this being is, clouds empower teams to provision new application servers and infrastructure whenever required on the go. Furthermore, with the latest cloud platforms coming up, any digital business can now easily make things work for their IT infrastructure in minutes rather than months.

From the past few years, machine learning is taking over the discussions for digital transformation. Efforts are being made to develop machine learning to a certain stage where without any human help or intervention things go smooth. The data science has been building future on these stepping stones: machine learning  Know More