The latest generation of cognitive and analytics technology is capable of by far more than taking orders. It will make decisions and learn. By using cognitive computing – a technology that integrates data mining, pattern recognition and natural language processing to mimic the way the human brain works — new applications can process high volumes of information to support decision making. Digital analytics allow these systems to analyze structured as well as unstructured data (e.g. social media and internet data) and apply predictions in order to make the right business decisions.
Computing is coming of age
In the past, systems have relied on conditional programming statements (if/then/else). In 1950, computer pioneer Alan Turing already suggested that building a computer that “thinks” like a child by learning as it operates, would eventually produce technology with the maturity of an adult. In the 90s, technology took a first step in this direction with the introduction of computers that analyze text and mine for keywords and data.
Since the early 2000s, new capabilities for analyzing unstructured data, new data sets (e.g. streaming information) and even more reliable solutions have been in development.
These had been in parts spurred by the advent of the internet and went along with the subsequent need to analyze big data and therefore developing more refined algorithms. New applications and programs utilize natural language processing and machine learning to understand and reason, allowing researchers and businesses to make smarter decisions.
Cognitive computing now enables the enterprise to put the customer at the very center of their strategies. By giving these capabilities into the hands of marketers, a lot of valuable data is no longer hidden from their view. Companies can instantly gain more extensive insights into the most intimate side of the customer universe than ever before. They can discover new patterns, put these in context and get to know each customer with previously inaccessible detail. Subsequently, companies can become agile enough to execute campaigns on the fly.
These campaigns can be combined with data from multiple other sources (e.g. social media and weather), allowing enterprises to reach an entirely new level of interaction by putting customer information in context . Data have evolved from a possession that went unused by many companies into structured insights that are essential to compete, i.e. events and social media have become data in motion (sensor data, IoT, geo special).
How cognitive technology works today
Cognitive technology works in 3 phases:
- Understanding
- Reasoning
- Learning
Cognitive software, combined with in memory technology reads and understands data and documents — structured as well as unstructured — on a huge scale. Through reading and analyzing this data, evidence-based recommendations are made. These are then augmented by leading expert knowledge. The cognitive engine learns and improves over time to enable independent decision making.
Dots are connected and questions that were not thought of before are identified and lead to new inspiration. Intentions can be better fulfilled and questions of customers and stakeholders more effectively answered. Decision making is supported by evaluating a condition and suggesting the right option.
Cognitive technologies are continually in development. IBM, for example, is teaming up with the MIT at their Laboratory for Brain-Inspired Multimedia Machine Comprehension to research and develop a machine’s ability to see and hear more effectively, thus improving machine learning capabilities.
Generating value: the future of healthcare is cognitive
The face of healthcare has already been changed forever on a number of fronts through cognitive technology. By using cognitive computing, researchers can access huge amounts of medical literature, combine it with patient information, and learn over time in order to create diagnoses or recognize patterns in medical information that can be used to improve patient care outcomes. Patients who are likely to develop a chronic health condition can be identified and offered programs to help prevent costly treatments.
As IBM reports, 80% of useful medical information is unstructured and thus cannot be recognized by non-cognitive computers. Once this knowledge has been compiled and machine learning allows computers to more intelligently process and analyze it, data can also be accessed globally at large scale. A doctor who sees a patient anywhere in the world could tap into this information and evaluate symptoms with the help of more data than any single human being could read in a lifetime to come up with the right treatment.
Life sciences also benefit significantly from cognitive solutions. The Watson Discovery Advisor for example, is offering genomics analytics to aid discovery and ensure safety. This area of development is of high value for the medical and life science industry. Real world evidence (RWE) and advanced analytics drive operational and clinical trial effectiveness, allowing for faster drug approval. This enables life science and pharmaceutical companies to develop new drugs faster with reduced costs.
Looking ahead
In the future, applications and increase in digitization will change the face of most industries. Healthcare and life sciences have already been significantly impacted. Information technology will continue to develop into a cognitive partner of industrial, scientific, and commercial organizations, helping to manage and make sense of data to increase knowledge, insight, and productivity.
The ability to learn and recognize patterns will change the way we do business and make decisions. Information can be processed and understood, in partnership with experts, on a scale and timeframe that humans alone cannot accomplish. Companies can use cognitive computing to create value. It has already been embedded in products today (e.g., Netflix and Amazon Prime) in order to deliver a customized and relevant end2end experience.
Amazon has already started to leverage artificial intelligence for the analysis of its individual customer. They know nearly everything about their shoppers: at which weather condition, on which days and at what time people buy certain items. Amazon applies algorithms to adapt prices, aiming at achieving the maximum amount a customer is willing to pay.
In order to compete in the future, companies need to embed cognitive computing and analytics along the value chain and connect data with company objectives, products and solutions. New ways of working and learning need to be developed. IT departments no longer simply support selected work processes, but have to be a key partner in developing and implementing new ways to use cognitive technology across the value chain of the entire enterprise.
As Audi’s CIO Mathias Ulbrich puts it, “We believe that the job of IT is to proactively support the ‘innovative transformation’ of the company as a whole.” Learning their way around cognitive computing, as well as strategizing ways to best apply in order to implement better business practices, should be at the top of the mind of every forward-looking company.
With its potential to change the way we understand and process information at every level, cognitive computing and machine learning are part of the progression towards more automated processes in business and research that began in the 1940s with Alan Turing.
Cognitive computing is the new revolution in the digital world, and businesses will need to harness this technology and use it intelligently to stay ahead of the game.
