Covering Disruptive Technology Powering Business in The Digital Age

Nearly 90 per cent of the quantum of data generated from the beginning of civilization until 2003 was created in 2015 alone. In 2020, the same amount of data will be generated every second!

Even so, the significance of Big Data extends way beyond its size. In a World Economic Forum survey conducted last year, more than 800 senior executives from the ICT sector named Big Data and Artificial Intelligence one of six mega trends that would make a deep impact on society in the coming decade. The other mega trends included People and Internet; Computing, Communication and Storage; Internet of Things; Sharing Economy; and Digitization of Matter.

It’s not hard to see why. By providing a way to collect, organize and analyze large sets of data, unimaginable even a few years ago, Big Data is enabling us to see intricate patterns, discover unknown correlations and acquire insights that help us take better decisions. But to derive this value, individuals and enterprises need to know how to leverage the Big Data at their disposal. Software tools that mine text or make forecasts are only one part of it; what is more important is to have a clear vision of how to optimize their output. For data is of little value until it completes the transition to information to knowledge, and finally, to wisdom.

As possibly the only industry to have access to customers’ transaction information (even for non-banking activity) and financial behavior in one location, banks are in a great position to exploit the Big Data opportunity.

A very obvious benefit of Big Data is that it enables banks to right sell, that is, propose the right product at the right time at the right price and through the right channel to every customer. They can target customers with relevant and contextual marketing offers instead of employing generic cross-selling tactics that are costly but not very effective.

The downside of digitisation is increase in cyber crime. Banks, for whom security is a paramount concern, invest billions of dollars in security systems and infrastructure to prevent criminals from accessing their data. With Big Data, they can fortify those defenses by analyzing customer activity to spot atypical patterns and immediately investigate if those transactions are genuine. If something is amiss, they can block the transaction quickly and minimize damage.

Big Data can also help performance assessment, an area that banks need to work on. While it is known that 80 percent of profitability comes from 20 per cent of customers, not much is done to rationalize the customer base by increasing business from customers with potential or retiring those who are a drag on profit. Although banks conduct extensive customer satisfaction studies, the scores don’t tell the entire story. For deeper insight, it is necessary to drill down the data to distinguish the rationally satisfied customers from the emotionally satisfied ones. This is very important because rationally and emotionally satisfied customers – even with the same satisfaction score – are very differently motivated, and will therefore react differently to the same set of events. Where emotionally satisfied customers are loyal and will often champion their bank’s services, the rationally satisfied are always on the lookout for a better deal and will switch to whichever bank provides it. Mining Big Data – performance of products, branches and tellers, or social media posts, for instance – can give banks invaluable leads about customers that they can use to convert rationally satisfied users into emotionally satisfied loyalists.

A very interesting use case for Big Data comes from the field of behavior analytics. As people take to wearable devices, they are generating a wealth of biometric data providing insights into their lifestyle choices, habits and behaviors. A number of businesses, such as retail or insurance, are using this type of information to make decisions based on predicted behavior.

Clearly, there are many ways in which banks can use Big Data to improve their business. However, they should also be looking to monetize it. One option is to sell the data to various businesses, within the boundaries of data confidentiality and privacy laws. Another is to strike strategic partnerships with retailing companies or telecom operators to personalize offers for different customers, and share the resulting revenues. Yet another is to take a leaf from the Pokemon Go book, mining data to discover patterns of behavior and other insights, and passing that on to interested enterprises for a fee.

Done right, Big Data could well mean Big Money.

This article was originally published on www.ciol.com and can be viewed in full

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