Cuelogic Blog Icon
Cuelogic Career Icon
Home > Blog > Data And Machine Learning > Big Data Analytics > Cognitive Computing: A Logical Extension of Analytics Work

Cognitive Computing: A Logical Extension of Analytics Work

Cognitive Computing: A Logical Extension of Analytics Work

Cognitive Computing is the 3rd era of computing. Cognitive systems are complex information processing ones, capable of acquiring information, putting it into action and transmitting knowledge. Cognitive science brings together perception, intelligence, calculation, reasoning, and, in the end, conscience. Cognitive systems are like a man with a machine than simple machine. And a man with a machine always beats a machine.

cognitive computing-01

These extraordinary characteristics of Cognitive methods have created an illusion that this method is a distinct concept from analytics.

Though every big organization is advancing its way towards analytics for better results, but cognitive computing is thought as being an exotic science project.

But if we look closely the realization is no far, that it is a rational and plausible extension of analytics work. It is the smart step for the budding organization as well as fully bloomed ones to apply this method or to realize it and expand it for future benefits.

So, what is Cognitive Computing?

Cognition, is the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.

Computation, the use of computers, especially as a subject of research or study or the procedure of calculating; determining something by mathematical or logical methods.

Therefore, Cognitive Computing means comprehension of data through thought, experience and senses and its evaluation through any of the systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.


Cognitive computing makes a new class of problems computable. It addresses complex situations that are characterized by ambiguity and uncertainty; in other words it handles human kinds of problems. In these dynamic, information-rich, and shifting situations, data tends to change frequently, and it is often conflicting. The goals of users evolve as they learn more and redefine their objectives. To respond to the fluid nature of user's’ understanding, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is “best” rather than “right”.

What are Cognitive computing systems?

Cognitive computing systems are machine with (nearly) man’s brain. Therefore, they are more adaptive, interactive, iterative and contextual. They differ from current computing applications, they move beyond tabulating and calculating based on pre-configured rules and programs.

Beyond these principles, cognitive computing systems can be extended to include additional tools and technologies. They may integrate or leverage existing information systems and add domain or task-specific interfaces and tools as required.

Cognitive systems will coexist with legacy systems into the indefinite future. Many cognitive systems will build upon today’s IT resources. But the ambition and reach of cognitive computing is fundamentally different. Leaving the model of computer-as-appliance behind, it seeks to bring computing into a closer, fundamental partnership in human endeavors.


We are in a new area of computing, where budding organization in the industries are already using its potential to add value to their businesses and help solve some of society’s greatest challenges. Following the programmable and tabulating systems eras, cognitive computing represents a huge leap forward.

The fundamental difference between the old and new era is the way systems are built and their interaction with humans. In the traditional programmable systems era, the results were based on human direction and processing. The cognitive era on the other hand is about thinking itself – how we gather information, access it and make decisions.

There are many big names believing and leveraging its potential. According to the research of IBM, multiple innovative opportunities across industries, creating chances for early adopters to achieve a substantial first-mover advantage. WinterGreen Research estimates the global healthcare decision support market alone will increase to more than $200 billion by 2019 as a result of new cognitive computing technologies.

Are You A Subconscious User of Cognitive Method for Analytics?

All the companies use cognitive computing in one form or another, even without realizing it. Perhaps, you are using “machine learning” which attempts to automatically improve the fit of models and “learns” its way to a better set of explanations or predictions. The method uses logistic regression, that is used for analytics since 1930s. Neural networks are also the formulation of same step, which are the basis of “deep learning” approaches used by many cognitive applications today. So whatever method you follow it has its roots in statistical approaches that are very familiar to analytical folks.

Why to Use Cognitive Computing for Analytics?

The main benefit of Cognitive technology is that it breaks through the limitations of traditional analytics. As we know, data can be collected much faster from sensors, online applications and social media than humans analyzing or acting on it. It is nearly impossible to analyze or process the huge data without the technologies like machine learning.

It also addresses the main problem faced in human-centric analytics, i.e, wastage of collected information. Yes! Researchers Martha Feldman and James March published an article as far back as 1981 arguing that managers often ask for information that they don’t use. They seem to be deciding through the process of analytics, but end up relying on their intuition at the end. Therefore, it’s important to bypass human decision-makers with automated decisions when we know that data and analysis are critical to decision outcomes.

For the vendors like SAS and TIBCO, “cognitive” and “analytics” are increasingly intertwined. We can look forward towards these vendors as an inspiration and a reason to use Cognitive Computing, SAS offers a machine learning capability as well as event streaming for automated analytics. And TIBCO is increasingly focused on “streaming analytics” for real-time automated decision-making – what it calls “fast data.”

Example A: 

Cognitive method is not a far away concept, but a real time approach. IBM is using “cognitive science” to capacitate automakers and their partners (eg.Panasonic) to deliver the insights to the drivers they may not even have thought about.

 David J. Taylor, director of connected services at Panasonic Automotive Systems Company of America,said the firm is working with IBM in ways that weren’t possible, even a short time ago, specifically in processing data that will ultimately be served up to consumers through an in-dash interface.

According to David J. Taylor, Companies like Panasonic would be able to deliver much tailored solutions to the automakers, that would understand their needs and cater their wants individually. “It is also going to be reflected in the actual ownership experience, even at what point someone will own a car. They might start out with using things like Uber and come into the space of wanting to own a car later. But we think what’s key is that we can leverage the data with cognitive solutions to provide a consistent consumer experience.”

Example B:

Microsoft has entered into the field of cognitive computing with an innovative project. Makers are putting their faith in this growing field of Home Security Systems to improve the customer satisfaction. Through this self built project, company is saving many pound and also it allows total control and customization to suit your needs.

With the introduction of Microsoft's Cognitive Services, facial recognition applications are now more accessible to makers than ever before. This project utilizes a Raspberry Pi, basic Webcam, and an internet connection to create a door that unlocks itself via facial recognition. If the visitor at the door is recognized, the door will unlock!

Example C:

Election Campaigning arguably is the most ambiguous and opinion filled process. Parties using their core beliefs as strength and hyping the party’s polar opposite side as negative.

The media’s biasm when recounting debates, speeches, and so called scandals prove it difficult to obtain objective facts on political issues and candidates, which is quintessential when deciding who should lead the country.

A sentiment analysis API is thought of as an option to track public opinion on elections. The line of action is to channelize the chaos of information on Twitter and Facebook regarding the opinions of the public about each candidate in real-time.

The tweets of the people can be sentimentally analyzed and mapped from 0-100 (0 to 49 is negative, 50 is neutral, and 51 to 100 is positive) and factored into a running average for the day and also into a smoothed, instantaneous sentiment.

The total tweets can be divided into the above mentioned sentiments and thus, candidates can be compared according to the public reaction/ metrics.

All of this information is then saved and added to a Haven OnDemand index, allowing search of pertinent, past public perception of particular candidates. Each candidate also has their bio, link to their Wikipedia page, and the most recent news articles about them, obtained using our Query Text Index API for public news sites.


Being in any business either you are using cognitive computing in one or another way or lagging behind. In this speedily growing market cognitive approach is required to process your data “smartly”. The companies using Cognitive methods have man with a machine and those who are not lack the man. And a man with a machine always wins the game!