KRC Murty, Vice President, Chief Technology Officer, Deutsche Bank Ag

We have been hearing of many new technologies which may take front stage in 2016 and years to come. One such new wave or buzzword according to me may be Cognitive Analytics. We have seen how analytics changed the entire gamut of approaching predictions and requirements of clients and users and behavioral patterns. Many organizations have used business intelligence (BI) and analytics to take their business to greater heights with the use of this tool. Cognitive analytics is also a way to bridge the gap between big data and the reality of practical decision making.

Cognitive analytics can be denoted to array of diverse analytical strategies that are used to learn about certain types of business-related functions, such as customer outreach. Certain types of cognitive analytics also may be known as predictive analytics, where data mining and other cognitive uses of data can lead to predictions for BI.

Currently, analytical systems that enable better data-driven decisions are at a crossroads with respect to where the work gets done. However, while technology is leveraged for data-handling and number-crunching, the hard work of forming and testing hypotheses, tuning models, and tweaking data structures is still reliant on people and is much of a manual activity in many areas. Much of the grunt work is carried out by computers, while much of the thinking is dependent on specific human beings with specific skills and experience that are hard to replace and hard to scale.

Generally, technology professionals refer to cognitive analytics when talking about various uses of big data for business intelligence. The idea here is that organizations collect or aggregate huge amounts of data from very diverse sources. Some designated software programs or other technologies analyze these in a depth to provide specific results that help a business to get a view of its internal processes, how the market receives its products and services, customer preferences, how customer loyalty is generated, or other key questions where accurate answers are used to provide a business with a competitive edge. Cognitive computing is also used in several artificial intelligence (AI) applications, including expert systems, natural language programming, neural networks, robotics, and virtual reality.

The implementation and use of analytical programs will drive better decision making in today's most agile organizations. Big data is considered to be most valuable when analyzed in conjunction with traditional data sets, such as sales or customer data. Various predictions and studies say that future computing will become increasingly microservice-enabled, where everything - including analytics - will be connected via an API.

Similarly, the predictions are also that by 2020, 50 percent of all business analytics applications will include prescriptive analytics built on cognitive computing functionality and that cognitive services will be embedded in new apps. More interestingly, the predictions are also that automated data curation and management will free up analysts and data scientists to do more of the work they want to do. The robotic data dissemination will help address the issue of short supply of analysts and data scientists.

Most organizations are not competent enough at using the data they have and putting it to the right use for their business development. Going ahead, cognitive analytics will become the follow-on to automated analytics. Many organizations will see relationship between cognitive computing and analytics, which may harden in much the same way that businesses now see similarities between analytics and big data. The prediction may also be that data science and predictive/prescriptive analytics will become one and the same.

However there may be a challenge in sourcing the right skill sets in this area. As a result, the market for big data professional services will spur out largely. There may be a big gap in addressing the demand which may not be able to be met in short term. However, it is inevitable that this is a new wave and everyone needs to catch up with this soon.



 1 feb


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