Big Data Strategy

Big Data in India: A CxO Perspective

By Jasmine Kohli and Prerna Sharma

A growing level of digitization means that data is now being churned out at an even more frantic pace than before. With India’s scale and diversity of data, it has been a highly challenging task for CIOs to create the foundation for intelligent analysis of data. To get a clearer perspective on the state of big data in India, Dataquest spoke to leading CxOs from different verticals to gauge the usage of big data in their respective organizations and how it has made a difference to the competitiveness. A first-person account of spokespersons from different organizations follows

 

Fullerton1

With a strong network of 400+ branches and being a consumer lending enterprise focused on financing the underserved, we rely on data-driven decision making, which in turn, is the bedrock to our customer segmentation and acquisition strategy. We have information of over 1 mn customers, which provides us an immense opportunity to run statistical models for a responsible financial growth of the franchise.

For any meaningful analysis, it is important that we get accurate customer information upfront so that we can apply decision science rules for quicker and seamless customer acquisition. The core of business intelligence is the transformation of data into insight and insight into action that can add value to the enterprise.

Over the last three years, we have invested extensively in analytics and technology. We believe analytics is critical for businesses to make better high-volume decisions to achieve breakthrough business performance; deeply understand and engage customers to earn and inspire their loyalty; and change the game and drive competitive advantage through big data and technology.

The industry is witnessing an accelerated growth in the amount and variety of useful information, thanks to growth in digital, increasing mobile penetration, hyperconnectivity and related trends. When we combine these data sources with information from our high-quality decision science models, we get the best of both worlds— high quality, highly granular, highly descriptive information that when used well, creates big opportunities to improve the effectiveness and efficiency of our marketing, sales, and innovation activities. The dynamic nature of our business requires decision sciences, an interdisciplinary approach of business, applied math, technology, design thinking, and behavioural sciences to solve constantly shifting and evolving business needs. This largely summarizes our need for quality analytics.

How analytics gives us a competitive advantage

There are seven inter-connected steps, which explain how the usage of analytics has given us an advantage. Firstly, data capture is the key. Customer information is made accessible through a data warehouse platform. Secondly, historic customer information is used for segmentation, and in turn, create appropriate customer acquisition strategy. But to move beyond segmentation and customization requires a more complex data-rich and event-driven analysis. Thirdly, many customer insights are intuitively obvious. One can predict the behavior pattern of top 5% but the remaining 95% need extensive modelling and robust analytics capability.

Fourthly, once the data streaming happens in real-time, we anticipate opportunities to board the right set of customers. Fifthly, the power of real-time data analysis is its ability to enable real-time decisions. We need to differentiate between our existing borrowers and new borrowers and need to inculcate the culture to promote customer loyalty. Sixthly, we have automated information management so that our frontline staff can take not just faster but accurate decisions for a unified and delightful customer experience. Lastly, we measure and evaluate the efficacy of our processes on customer acquisition. This continuous, closed-loop process incorporates strategic and operational intelligence and in doing so enables granular analysis, refines event-driven models, and generates new insights and opportunities.

Challenges

While big data has increased the opportunities available to businesses, it also creates more challenges to capturing, storing, and accessing information. Utility of some of the data points declines rapidly which means that we need to churn data into information very quickly. In India, there is a challenge of getting relevant information (like age, income levels) especially in the rural markets, but the heartening news is that this is soon changing given the penetration of banking and unique identification number in most of the markets that we operate in.

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