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The Three V’s of Big Data

Srinivasan Govidanrajalu (2)In an exclusive Srinivasan Govindarajulu, Senior Director and Practice Head- Enterprise Information Management, Virtusa Corporation discusses the challenges in Big Data adoption and the three v’s.

1.      What are the big challenges for banks to incorporate big data?

Big Data has the capability to handle both data volume and real-time data management to move the practice of data analytics far beyond the merely transactional. This however can be done only up to a certain point. It requires skills from social sciences and psychology to name a few. These analytical skills help identify and articulate insights in the pattern delivered by technology. Hence, talent acquisition in the desired skill sets poses a big problem in realizing the potential of big data. Banks and other enterprises will have to fill this gap with the right data professionals.

Also most of the banks and financial institutions still employ legacy technologies to manage data analysis, with a very small chunk looking at adopting alternate technologies to monetize the huge volume and variety of data available. On an average, a bank uses more than 5 different technologies to analyze data today, which reflects a lack of awareness towards technological cohesion in an enterprise.

2.      How can organizations get the best benefits of big data solutions?

Virtusa is focused on harnessing the power of Big Data technology to deliver business results for its customers. Given the business benefit that can be harnessed, we are working with clients, across the spectrum – from strategy consulting to technology implementation. For example, in the Banking and Financial Services sector we are working with customers to bring in solutions catering to faster analytics of sensitive data. With such solutions, banks can expect to understand their clients better, have higher cross-sell acceptance rates leading to lower campaigning costs and higher revenues.  The sensitive data can be processed to provide better fraud control and risk management thus providing in-time detection and mitigation.  Real time reporting is another promising or hot area that banks and other financial institutions are looking forward to and we recognize this not only as a regulatory requirement but also a data monetization opportunity for our clients.

3.      Has the term lost its touch as a next-gen tool to new technology terms of-late?

Much of the insights needed for understanding customers, markets, products, promotions and sales lies in the correlation and analysis of that data. Historically, a lot of this analysis would happen in the back office and typically be produced as monthly reports for executives to analyze and react to. But the world has changed and the expectations of the end consumer are now driving more real-time analysis of transactional and social data feeds. In the banking sector, Real-Time Analytics around transactions from their system is invaluable and this is something which almost all the financial institutions are working on. Another trend we see in the market is for enabling faster and efficient retrieval of complex data. The digital world is seeing an enormous data explosion. With the advent of technology and increased digitization, there is an information explosion with huge amount of structured and unstructured data created and stored at an even faster pace. As the size of data that an enterprise carries increases, enabling faster or more efficient retrieval becomes extremely critical in order to serve their clients efficiently.

There is also considerable traction towards using Big Data technologies for data crunching to gain operational efficiency. Customers are also seeking analysis solutions for social media data to help with their businesses.

The other advent is in the form of small data. Customers these days with the prolific use of social medium and digital media, leave digital footprints that can be captured. This data gives insights on individual activities that can be gathered to generate meaningful insights.

Having said that, most of the examples above include data feeds from multiple and diverse sources, such as social media, webcasts, blogs, third party data, location based data and other unstructured sources. These data sources justify the usage of big data as a new-gen tech. Bearing in mind, the fact that banks are the consumers  of the applications, for them it could be a new technology per se, as most of the banks view big data as relatively new. However, we are convinced that this is the time to raise awareness and better equip them for the technological change that lies ahead.

4.      What are the potential large case use scenarios of Big Data solutions in BFSI industry?

It is crucial that organizations focus big data initiatives on areas that can provide the       most value to the business. Banking companies should identify the processes that most directly interact with customers, followed by strategy to enhance costumer experience at that touch point. Even small improvements matter as they often serve as points of proof that demonstrate the value of big data, and the incentive to do more. For most banking companies, this will mean start with customer analytics that enable better service to customers. Banks use these insights to generate sales leads, enhance products, take advantage of new channels and technologies, adjust pricing and improve customer satisfaction.

Today’s millennial customer prefers customized products. They demand to be valued, taken care of and to be timely provided with feeling of exclusivity. To effectively cultivate meaningful relationships with their customers, banking companies must connect with them in ways their customers perceive as valuable. Tapping big data banks can enhance customer retention by delivering personalized offers in real time. This can be achieved by learning customer attitudes by understanding and anticipating customer behavior across all channels. Adding social analytics as an additional source of valuable insight can boost revenue, and help increased ROI. By accurately predicting products that appeal to micro segments, banks can also successfully optimize cross-sell/up-sell.Thus, the key to understanding what different customers want from their bank lies in big data. By collecting and analyzing all of the data that banks have available about their customers, they can then group those customers into different segments based on their expectations and banking needs. To use big data effectively, organizations need to have a vision for what they want to achieve and how they want to become a leader in customer experience. They need to be willing and able to analyze all of the data available no matter how voluminous. Organizations need to employ the right people who can merge their data analysis skills with their inherent curiosity and creativity to correlate data sets that seem unrelated to help uncover new insights.

5.      Can Big Data be termed as Volume, Velocity and variety, to drive customer behavior? what can be the large case benefits ( within customer behavior and analyzing customer profiles)? Please cite an example?

As per industry research the Big Data market has already crossed the billion dollar mark. In fact, according to an IDC estimate, Big Data technology and services market will grow at a 31.7 per cent CAGR with revenues reaching $23.8 billion in 2016. This data is being generated from various sources such as social media, sensors, digital photos, videos, stock trading across industries, financial transactions and telephone calls.The basic challenge that Big Data presents for many organizations is its unstructured nature, high volume, variety of sources and speed at which it is produced. While traditional data sets have some of these characteristics, the complexity and scale of Big Data makes it much harder to process, organize and store in a cohesive manner that makes it easy to analyze. This has all been aptly captured as the 3 V’s of Big Data (Volume, Velocity and Variety). The sheer number of consumers and an increasing younger population pose a gigantic challenge in identifying products and services which cater to the demographic.

Specifically, w.r.t. customer behvaior, one of our big banking clients leveraged our expertise to improve responsiveness towards its customers. To enhance the usage of their applications, usage details needed to be captured at the individual product level in the huge web of data available. The goal was to develop BI reports that could capture this usage. Virtusa’s expertise on Hadoop, helped implement a solution that increased product usage and ROI, coupled with a competitive edge in the marketplace. The product level usage details also enabled the development of new functionalities.

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