How can cognitive analytics help business in real-time?

Most of the data dealt with today are unstructured and scattered in nature. This data comes from various IoT devices; satellite feeds, social networking sites, purchase transactions and more. Thanks to Big Data, higher computational speeds and the increased availability of analytics tools, we can carry run analytics of the data in real-time, anywhere, anytime. While these analytics provides a competitive advantage for businesses, they lack knowledge-based insights to help in decision making. In other words they are mathematical in nature. Therefore to enable this, we now have cognitive analytics that exploit the massive advances in High-Performance Computing using a combined force of AI and data analytics practices.

In technical parlance, it is a field of analytics that mirrors human brain intelligence by drawing inferences from existing data and patterns to perform certain tasks. Here, it draws conclusions based on existing knowledge bases to locate real-time data, and then inserts it into the knowledge base for future inferences, thus creating a self-learning feedback loop. Businesses rely on cognitive analytics when using big data for business intelligence. It blends a number of intelligent technologies to accomplish this, including semantics, artificial intelligence algorithms and a number of learning techniques such as deep learning and machine learning. So leveraging cognitive analytics can drive a cognitive application into becoming smarter and more effective over time by learning from its interactions with data and humans. It can interpret the context of the written material. It allows companies to expand the business into new markets, mine untapped data sources and enable accelerated innovation of new products and services. Also, at the enterprise level, it can establish links between large volumes of information and the need to make decisions in real-time. Further, it also helps unlock the value of big data by making the whole system more self-sufficient and information contained more accessible.

Cognitive analytics has potential across every industry vertical. For instance, in the Fintech sector, it is employed to predict market trends, execute trades and accurately forecast stock values, instead of relying on a trader's "hunch." In e-commerce sector, it can help businesses improve customer service, enable personalized customer experience, enhance customer engagement and enable faster response to market needs. In healthcare, doctors can scan through medical journals, textbooks, social media feeds and medical notes belonging to the patients to provide the best care plan possible and diagnosis outcomes. This can help in removing geographical and economic constraints that restrict people from seeking timely medical aid. Most of these cognitive analytics potentials are possible due to its adaptive, interactive, iterative, contextual capabilities. Moreover, it is compatible to deal with "dark data" like social media postings, EMR notes, fitness device readings, unstructured images, videos and the digital or non-digital documents generated by users in day to day life.

While the technology of cognitive analytics is relatively a work in progress, it is already transforming several industries. In fact, it is already viewed as a successor to big data analytics as companies are harnessing its power to solve problems in their business premises. Further, it is redefining the relation between man and machines as latter gets smarter by self learning.