Unscrambling Big Data

This data deluge phenomenon is notably one of the major platforms of growth in 2012, driving organizations globally to invest in this next “must have” competency. For enterprises, Big Data has become a reality and represents significant opportunities to create business value. For that to happen, enterprises need to manage and make sense of all these data, in order to make better and more real-time business decisions. 

There are three main aspects to data management that enterprises should know. 

First, is the need to modernize IT infrastructure. Organizations need to adopt systems and data centers that are flexible and scalable, responsive to the rapidly changing business environment. Secondly, in building on the foundation of the first phase of data management, is the need for businesses to achieve extreme performance and scale. We are talking about the ability to process hundreds of terabytes, or even petabytes of data, that simply would not have been possible with the servers and computing capabilities ten years ago. This is where the heart of a significant number of innovations is happening in Big Data technologies today. The third piece to the equation is something that organizations are just starting to talk about, which is the formation of an agile analytics team. This is where business users themselves can be much more scientific about how they can use the information they have to understand their business, generate hypothesis or ideas quickly and then validate them with data. 

No doubt there are significant hurdles to cross, but the benefits that can be derived through Big Data span far and wide. Retail bankers, for one, can make use of predictive behaviour to pick out and recommend the right products to their most valuable customers. This can only be done though analyzing huge sets of data, and just like how Amazon make different kinds of recommendations through predictive analytics based on mathematical models, banks must segment their customers based on their lifetime value and correlate these data to their product offerings to deliver the most profitable and relevant recommendations at every point of customer interaction. On a broader basis, banks can also improve the calculation of their risk profile, increasing the frequency from a weekly or monthly basis to one that is real-time. This is especially advantageous in situations of fraud detection where a longer open-window period for fraudulent transactions would translate to greater losses.   

Another vertical which Big Data has great potential to expand into is in improving the state of the healthcare industry. In Singapore especially, with a rapidly greying population and higher demand for accurate and timely health information, there is an impetus to move towards personalized quality healthcare and medical attention while remaining cost-effective. This is exactly the goal of the iN2015 Healthcare and Biomedical Sciences plan  in striving for different healthcare providers to work together in an integrated and coordinated manner to better determine the most appropriate point of care to treat patients, as well as minimize prescription and medical errors. 

From a social media perspective, we are at a point where billions of users across the globe are expressing interests and activities openly through platforms such as Facebook and Twitter. Many retailers are trying to grasp and make sense of information on these platforms to improve their interactions with customers. However, the promise of the ability to reach out to a segment of extremely targeted and focused consumers through Big Data cannot be more real. As part of the Infocomm Development Authority of Singapore’s (IDA) Data & Analytics Programme , IDA and its industry partners will be investing a total of $5.3 million to develop a suite of business analytics shared services for the retail and wholesale sector. 

The industry is moving in the direction of helping companies analyze their data through simpler and more cost effective ways than before. We are looking at the need for an integrated system for Big Data, one which places great importance on the people – the actual users of the systems. Big Data processing needs to embrace collaboration in order for individuals to work together to produce insights. This is where data science comes in, where we observe an intersection of skills from mathematics, computer and behavioral sciences to extract value and insight from data. We are likely to see a dramatic increase in demand, for millions of data scientists in the next ten to 20 years. In fact, we are already observing the emergence of new companies formed specifically around Big Data analytics and data science.

While cloud computing focuses on the bottom line in achieving more efficient use of an organization’s IT infrastructure to gain cost savings, Big Data adopts a more top line approach in helping businesses to generate more revenue and profits.  I look forward to seeing a more holistic approach towards data management and for more industries and organizations to benefit from new operation efficiencies, revenue streams and business models.