Cognitive analytics: from Big Data to decision-making
Cognitive Analytics removes the barrier between Big data and practical decision making.
We have witnessed a major technological breakthrough. Artificial Intelligence, mechanical learning and processing of natural language are no longer mere concepts – they are a reality. Data scientists have taken the internet, cloud and business processes and combined them to drive real-time decision making. Cognitive analytics allow organizations to respond to consumer trends quickly and accurately.
Prior to Big Data, companies had to manually synthesize customer data to identify trends, behaviors, and target markets. They developed rules to drive data so they could optimize their marketing strategies. Big Data allows them to do this automatically and for a whole lot less money. Traditional methods have progressed to the point where we can see improvements in storage technologies, visualization, business intelligence and statistical modelling.
Make Cognitive Analytics Work
Big Data alone does not solve the problem of cognitive analytics. There still needs to be a human driving business models, analyzing trends and identifying opportunities. Regardless of how advanced technology may become, we still need people to do the work to make the data matter. Big data is simply a tool. The creativity and insight need to come from somewhere else.
Information Discovery and Decision Making – A novel approach
People who work in the field of computer technology have been privileged to witness a world in which machines can learn from empirical data and act accordingly. This is what is referred to as cognitive analytics. Computers are now able to sift through data and recognize trends and patterns. They can then use these patterns to predict future behavior. Of course, it is a person who has to write the software to make this possible. The computer simply mimics the way a human thinks – it imitates how the human brain receives information and processes that information. The decision making is automated without judgment or scrutiny. It only changes when a computer programmer commands it to do so.
Computers are now able to generate hypotheses, make deductions and draw a handful of potential inferences. The results are then presented in the form of a recommendation. The computer can even provide a level of confidence in the recommendations it provides. Decision makers and business managers can then choose which recommendations it wants to follow and which are too risky.
One industry that has already started using cognitive analytics is healthcare. They use cognitive analytics to predict and improve patient outcomes. The information is an amalgam of structured and unstructured information. The structured information is comprised of patient files, claims, medical records, and outbreak statistics. The unstructured data consists of medical journals, textbooks, social media feeds and medical notes. The data can then be incorporated into patient diagnosis and individual histories to provide the best care plan possible. Cognitive analytics also removes barriers such as geographical and economic constraints.
Another area that employs cognitive analytics is the financial sector. They use cognitive analytics to predict market trends, execute trades and accurately forecast stock values. Rather than relying on a trader’s “hunch”, stock brokerages can use precise and empirical data to make recommendations to their clients.
In no way does anyone believe cognitive analytics will replace humans. We will always need people to make judgment calls, devise marketing strategies and provide a customer with an emotional experience. The field of cognitive analytics is still a work in progress. However, it has already had a major impact on most if not all industries.
Regardless of how useful cognitive analytics may be, businesses must always look inward. They have to constantly improve their operations. This will allow them to take full advantage of the information that cognitive analytics offers.