Cognitive analytics is likely to redefine big data events

The proliferation of big data analytics solutions has significantly redefined businesses' data processing over the years. It has already proven a key solution for identifying and deriving meaningful insights from vast datasets. With emerging technologies like artificial intelligence, machine learning and the cloud, data professionals are now leveraging cognitive analytics to drive real-time decision making. It presents much greater potential than big data analytics, unlocking the value of big data by making a system more self-reliant, and information contained more accessible.

Since data is considered the oil of today's digital economy, data analytics is an indispensable economic driver. Over the years, it has evolved exponentially including from descriptive to diagnostic and predictive to prescriptive. Cognitive analytics is now likely to become the next frontier of this data analytics trend. It exploits high-performance computing power by integrating artificial intelligence and machine learning techniques with data analytics approaches.

Creating Competitive Edge with Cognitive Analytics

As the world is floating around voluminous amounts of data, leveraging data analytics tools is crucial for businesses to empower data-driven decision-making. A cognitive analytics system embraces human-like intelligence to certain tasks and brings together multiple intelligent technologies such as semantics, AI algorithms, and deep learning, among others.

Integrating cognitive analytics will extend the capabilities offered by big data tools and lead to more practical decision-making for leaders in diverse industries, including retail, healthcare, consumer goods, financial services, and others. It can also create opportunities for businesses to gain a competitive edge, providing them real-time answers through massive amounts of information and making sense of context. This will considerably improve service consistency and quality, and enhance knowledge sharing.

Cognitive computing-based analytics can also help create new learning opportunities. It assists IT and business professionals to perform their tasks effectively, in addition to staying ahead of emerging data analytics capabilities.

Often, decision-makers refer to cognitive analytics when using big data for business intelligence. As they glean or aggregate large amounts of data from diverse sources, cognitive computing assesses these data profoundly to provide specific outcomes that help enterprises to get an in-depth view of their internal processes. It also enables them to question how their services are received by the market, what are customer preferences, how they can deliver services for effective customer loyalty. Companies also need to build systems for cross-platform data usage and the processing of this data to a particular end.

Industries like healthcare that capitalize on cognitive analytics to foresee and improve patient outcomes. As complex in nature, the healthcare ecosystem collects information that is both structured and unstructured comprised of patient files, claims, medical records, and outbreak statistics. Financial services is another industry using cognitive analytics to envisage market trends, execute trades and accurately predict stock values.

As artificial intelligence and other intelligent technologies are starting to make a substantial impact on business, cognitive computing-enabled analytics is likely to witness an optimistic future. According to a report, the cognitive analytics market is expected to reach from US$1.62 billion in 2016 to US$10.9 billion by 2022, growing at a CAGR of 42.9 percent.