Paving the way for BIG DATA Analytics in Sri Lanka


By Zahir Fuard

Sri Lanka is a land which is well acquainted with the concept of precognition, and a persistent desire to predict the future. Even the most rational among us cannot help but be tempted by the idea. If you knew what was coming tomorrow, would you act differently? What would you do? How would things change? Naturally, many have tried to step in and fulfil this latent demand for a more certain future. We see it in horoscopes, and in the confident proclamations of astrologers. Yet time and again, these intermediaries fall woefully short of providing any real insight to what lays ahead.

Tools change with the times, and today’s technological innovations are rapidly enabling a brave and exciting new world of possibilities, one in which data becomes a catalyst for an economy, a society, and a nation that is free to unleash its true potential. Across the globe, we see numerous examples of predictive analytics enabled by rapid advances in the ability of a given organization to collate and analyze massive quantities of data – essentially enabling a given macro-situation to be analyzed simultaneously at face value, down the most minute, granular level. This approach is capable of driving entirely unprecedented paradigms of development in nearly every facet of life.

From the way we grow our food, to the way we run our businesses; from the treatment of disease, to the development of breakthrough products and services in the banking and financial sector; from predicting natural disasters to developing smarter financial models, change is coming, and big-data is the key. Our emerging digital economy will ultimately operate according to a drastically different set of rules, and success in this new environment boils down to how we frame an issue, and how intelligently we respond to the insights we are given.

The devil is in the details

While the general approach of how big-data works is fairly consistent, its application and integration into different industries can be a bit more complex. Consequently, specific approaches must be tailored for each sector. Generally speaking, big-data is simply a catch-all term used to describe a process by which predictive mathematical algorithms are applied to very large, complex, rapidly-changing datasets in order to extract precise, reliable insights into what is most likely to happen next, based on a complete analysis of every such situation in the past.

As is often the case, it is the banking, financial services, and insurance (BFSI) that is often the first adopter of cutting-edge technologies and Though many BFSI organizations are beginning to disrupt their analytics landscapes by gathering immense volumes of data assets, these companies are at varying levels of Big Data maturity. As customer volume increases, it dramatically affects the level of services offered by the organization. Existing data analytics practices have simplified the process of monitoring and evaluation of banks and other financial services organizations, including vast amounts of client data such as personal and security information. But with the help of Big Data, banks can now use this information to continually track client behavior in real time, providing the exact type of resources needed at any given moment. This real-time evaluation will in turn boost overall performance and profitability, thus thrusting the organization further into the growth cycle.

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