Cognitive computing is based on self-learning systems that use machine-learning techniques to perform specific, human-like tasks intelligently.

The purpose of cognitive computing is to simulate human thought processes using a computer model. So, with self-learning algorithms that use data mining, pattern recognition, and natural language processing, a computer can mimic the way the human brain works.

According to a recent report by Allied Market Research, the cognitive computing market is expected to generate revenues of $13.7 billion by 2020. It drives the digital transformation of companies of all sizes and market segments to a new level.

In this article, in addition to having an overview of cognitive computing, you will understand what its relationship with Business Intelligence (BI) is. Check it out!

What is cognitive computing?

Cognitive computing uses technology and algorithms to automatically extract data concepts and relationships, understand their meaning and generate learning, regardless of data patterns and previous experiences, expanding what people or machines could do alone.

It builds on top of Artificial Intelligence and involves many of the same underlying technologies to drive cognitive applications, including expert systems, neural networks, robotics, and virtual reality.

In practice, cognitive computing solutions can synthesize data from various sources of information while weighing context and conflicting evidence to suggest the best possible responses.

Basically, there are three ways in which cognitive computing can be applied today:

  • robotic and cognitive automation to automate repetitive tasks to improve efficiency, quality, and accuracy;
  • cognitive insights to discover hidden patterns and relationships in order to identify new opportunities for innovation;
  • cognitive engagement to drive customer action, offering personalization in scale.

It should also be said that the use of computer systems to solve types of problems for which humans are normally required needs large amounts of data — structured and unstructured — fed by machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and process data to become capable of anticipating new problems and modeling possible solutions.

What benefits does cognitive computing offer to businesses?

Gartner classifies cognitive computing as a platform that will bring a digital divide unlike any other tech did in the last 20 years. Here are the top two benefits it offers organizations.

1. Improved data analysis

Take the health sector as an example. Cognitive computing systems can collect information, reports, and data from different sources, such as medical journals, personal patient records, diagnostic tools, and documentation of similar treatment lines used in different hospitals and health care centers in the past.

This provides the physician with evidence-based data and recommendations that can improve the level of care provided to the patient. So here, cognitive computing will not replace the doctor, but will simply take on the tedious work of sifting through various data sources and processing them logically.

2. Efficiency gain in processes and customer interaction

The use of cognitive computing helps companies to identify and act on emerging standards. For a faster and more effective response, it also helps identify opportunities and discover problems in real time.

Cognitive systems can provide surprisingly relevant, contextual, and accurate information on broad customer issues. For example, in a hotel chain, they can report on local tourist attractions, provide information on hotel amenities, and provide great restaurant recommendations by crossing data from a variety of sources.

How can cognitive computing be used in your company?

In general, in the corporate environment, cognitive computing is used to assist humans in their decision-making process. Some examples of application include the support to doctors in the treatment of diseases.

In financial services, a cognitive sales agent uses machine intelligence to initiate contact with promising sales leads and then qualify, track, and sustain leads. It can analyze natural language to understand customer conversational questions, dealing with hundreds and even thousands of conversations simultaneously and in dozens of languages.

The most common uses of cognitive computing are to perform advanced classification, such as routing of people and needs for the best employees to meet requirements, and for predictive analytics, how to know the best way to promote a product to a buyer.

Predictive cognitive analyses are part of a data routing and interpretation approach that begins and ends with what the information conveys. This unique way of approaching all information (of all types and on any scale) reveals connections, patterns, and settings that enable unprecedented and even unexpected insight.

Predictive analytics can also help you get meaningful business information using structured, sensor-based data, as well as unstructured data such as text and video without labels for customer sentiment mining. In recent years, a shift toward “cognitive cloud” analysis has also increased access to data, allowing for real-time learning advancements and reduced business costs. This recent change has made more accessible a series of advanced analytics and business intelligence services based on cognitive computing.

What is the relationship between Cognitive Computing and Business Intelligence?

With all this, it is undeniable that cognitive computing makes the concept of Business Intelligence even deeper for organizations.

Generally, cognitive technology excels in cases where there is too much data for humans to classify, where making automated and fast decisions in a mass of data is critical to the business and where the rules of the game are well defined. Machine learning, for example, is being used to detect and deal with fraud more proactively, reduce customer turnover, or boost sales through personalization.

There is still a lot of territory to be covered before most companies are in a strong position to take advantage of cognitive solutions and their benefits. In the process of finding the platform and the right people and then paying for it all, cognitive computing can help differentiate future-thinking companies.

So, understood what cognitive computing is and what its relation to BI is? Got any questions? Leave a comment!