The journey of data analytics began with simple descriptive (review of past events) and diagnostic (analysis of past events) exercises and moved to the more sophisticated predictive and prescriptive genres,where advanced data models have enabled accurate future forecasts and actionable intelligence. And now cognitive computing has further strengthened the actionable predictive power of the machines by making the machine act like the human brain. For example, in addition to deciphering “words” in a piece of text, a cognitive analytics system can also interpret the context of the written material.

According to Ulster University research, 90 percent of the data floating around in the digital world today “has been created in the last two years.”This data is the “oil of the 21st Century Digital Economy.” Thus, it is easy to understand why data analytics is becoming an increasingly important driver of global businesses and soon many spheres of human activity will be governed by data-driven decision-making.

Over the years, data analytics has played a key role in corporate decision-support systems. Data-driven information, insights, and predictions — all have contributed to the competitive edge of global business operations, making data the most prized asset of enterprises in the digital age.

Cognitive Computing: The Third Era of Computing

What is Cognitive Computing? describes cognitive computing as:

“A subfield of artificial intelligence, [which] simulates human thought processes in machines using self-learning algorithms through data mining, pattern recognition, and Natural Language Processing.”

According to some experts, cognitive computing follows the tabular systems of the 1900s and the programmable systems of the 1950s. Cognitive computing ends up as a result of years of research in cognitive science and computer science, bringing the human brain closer to the computer brain. What Everyone Should Know About Cognitive Computing mentions that cognitive computing is a direct byproduct of cognitive science, the study of the “human brain and its functions,” blended with computer science to enable the machine to act like the human brain.

Cognitive Computing Demystified: The What, Why, and How provides a helpful roundup of opinions on cognitive computing from industry thought leaders. This article particularly talks about enabling a “system of best practices” to keep advanced computing aligned with “social and ethical values,” while remaining compliant with all applicable laws and policies.

Cognitive computing extends the capabilities of traditional analytics to a level where very complex BI or statistical tasks become relatively easy. For example, Watson Analytics uses cognitive computing to make complex analytics procedures simple.

Cognitive Analytics — Combining Artificial Intelligence (AI) and Data Analytics claims that cognitive analytics enables the analytic tools to think like humans. According to this article, cognitive analytics exemplifies the best possible blend of artificial intelligence (AI), machine learning (ML), deep learning (DL), and semantics. 

The general expectation from cognitive analytics tools is that over time, they will continuously learn from data and human-machine interactions and become smarter. The goal of cognitive analytics is to blend traditional analytics techniques with AI and ML features for advanced analytics outcomes.

The webinar Understanding the New World of Cognitive Computing, published a few years ago, is useful for understanding the fundamentals of this technology and its applications.

The Role of Ulster University in Cognitive Research

Ulster University is one notable university with a unique track record of fifteen years of research excellence in cognitive analytics. The Intelligent Systems Research Centre in that campus is globally renowned for frontier research in cognitive computing. Here, esoteric research techniques are employed to train cognitive applications to become smarter. In technical terms, the process of “applications becoming smarter” involves allowing the computerized models to simulate the human brain and act like a human entity.

The outcome? PDAs on mobile phones such as Siri is one such example, yet it does not truly represent a cognitive application with self-thinking capabilities. These applications are pre-programmed with set responses. Let’s take another example: Hilton Hotel’s Connie, a robot concierge, who can guide the future hotel guests about the hotel facilities and local sightseeing opportunities in a natural language. In the future, more polished, self-thinking computer models will more accurately simulate the human brain by exploiting techniques of data mining, natural language processing (NLP), and pattern recognition.

The Current Status of Cognitive Technologies

According to the published results of the Deloitte 2019 Global Human Capital Trends survey, 80 percent of the respondents have predicted growth in cognitive technologies, while 81percent have predicted growth in AI. Twenty-five percent of the business entities participating in the survey have already implemented cognitive technologies like AI or ML.

7 Indicators of the State-of-Artificial Intelligence (AI) mentions that the global AI market is projected “to grow at a compound annual growth rate (CAGR) of 40 percent until 2023, reaching $26.4 billion.” This same article also indicates that AI is likely to replace about half of the world’s unskilled labor within the next five to seven years. By 2022, “one in every five workers” may be using AI daily. Increasingly, intelligent machines are acting as “mediators” in our “political, economic, social, and cultural” exchanges.

The webinar Want a Fast AI Implementation? Cognitive Search is Your Ticket to Ride highlights the findings of a “24-criterion evaluation” of 12 noted cognitive search-engine providers. Mike Gualtieri, Vice President and Principal Analyst at Forrester, initiates a discussion of specific use cases and the selection strategy (Forrester Wave) for locating the best cognitive search tool.

360 Quadrants, a technology-assessment site, has compared 49 cognitive analytics solution vendors across 133 criteria. In this comparative quadrant, the race is between Dynamic Differentiators (known players), Visionary Leaders (strong portfolio), and Emerging Vendors (new entrants with limited exposure.) It is interesting to note how the growth strategies vary in each group.

How Does Cognitive Analytics Work?

According to Quick Guide to Cognitive Analytics Tools and Architecture from Xenonstack Insights, the cognitive analytics system follows this sequence of procedures:

• It searches the entire available “knowledge base” to locate real-time data.
• It collects and makes real-time data sources such as text, images, audio, and video available to advanced analytics tools for decision-making or business intelligence (BI).
• It mimics the human brain to study and learn from the available data to extract actionable insights, hidden behind “data patterns.”
• While following this process, the system often combines techniques of AI, ML, DL, neural network, and semantics.

Rita Sallam, research vice president at Gartner, said:

“Data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do. The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers. It’s critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value.”

Will Cognitive Analytics Take Over Big Data Analytics?

For years, big data has ruled the corporate roost, delivering operational efficiency, insight-driven products and services, and path-breaking customer service. And now, suddenly, cognitive analytics seems to be threatening that unrivaled position of supremacy. In fact, cognitive computing is already showing hidden potentials for overthrowing stand-alone big data analytics through its power to make an analytics system more self-reliant and data-ready. Cognitive Analytics is Bigger than Big Data Analytics describes how cognitive computing will further strengthen Big Data analytics.

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