The Year of Big Data in Banking
American retailer Target made a lot of headlines last year for figuring out that a teenager was pregnant, before her family did, simply based on her buying behavior. A poignant yet persuasive case for data analytics.
Cut to the world of banking, where the value of information cannot be overemphasized. Besides the data that banks generate within their organizations and organize neatly within their databases, digital consumers are creating a profusion of unstructured data across non-traditional, largely mobile touch points. Big Data is increasing exponentially in volume, variety and velocity, and banks should be mining its depths to make an early discovery of emerging trends, customer needs and competitive practices, or like the retailer in the earlier example, for making inspired predictions. The problem however, is that most unstructured data - trapped in social network conversations, online ratings, YouTube videos, Facebook likes, blogs, user-generated content and so on - is unstructured and beyond the analytical capability of traditional systems.
But not for long. We predict that 2013 is the year when banking analytics will come into its own.
The emergence of specialist Big Data business platforms will open up new dimensions in insight generation and decision-making based on unstructured datasets. These will enable banks to gather structured and unstructured data from virtually every source, in real-time. They will be equipped with sophisticated tools, like text and predictive analytics and natural language processing, to convert mountains of raw data into intelligence and insights. Again, in real-time. They will also facilitate the operationalization of these insights into action. While the platform will provide complete access to Big Data, it is critical for banks to figure out how to put the information to good use. Selectiveness is the key to avoid getting buried under a mountain of data. Indeed, the real value of Big Data lies in its "small data" insights, uncovered after sifting through huge piles of information. So, while searching for Big Data for the next big thing, banks should be looking for clear visibility to value. A number of European banks are planning to use Big Data analytics to generate insight into risk which clearly impacts return and ultimately shareholder value. Recent research says that there is a real potential for European banks to analyze both traditional structured data and new information from unstructured sources to understand market conditions better and thereby arrest the rising delinquencies in loan and credit card repayment.
A lot has been said about the right Big Data strategy, and its mostly generic wisdom about starting small and growing incrementally. Even so, it is probably a good idea for banks to implement Big Data initiatives by line of business, purpose or priority. So far, most of the activity has been focused on the management of fraud, customer relationships and marketing campaigns; banks should be looking at extending that to more strategic areas like long term planning or innovation. In fact, Big Data could play a big role in driving a virtuous cycle of innovation, where interesting innovations spark positive social conversations, which can be analyzed to drive further innovation.
And where else might banks go from there? We believe that they will deploy their Big Data skills to combine past and real-time information on customers, products, transactions etc. to transform the customer experience into one that is rich, highly personalized, location aware and seamless across channels. American Express is already leading by example with its services built on Smart Offer Technology; as the name suggests, there's a mobile app which enables merchants to make "right offers at the right time" to customers based on spending history and present location; it also presents customers with a sensibly organized list of offers to choose from.
On current evidence, this is the year that we will see similar meaningful moves from other banking institutions towards Big Data, analytics and everything in between.