Last year TED speaker, best-selling author and Duke University professor Dan Ariely posted this status update on his Facebook page: “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
In all honesty, this post sums up the current status of big data unprecedentedly. Big data is a big mystery for many – the companies don’t know how to use it and the customers don’t know how it’s being used. A recent study by Gartner1 revealed a curious fact – a considerable number of companies run or at least claim to be running big data projects without even understanding what it is. And if companies don’t understand it, the customers are even more puzzled as to what this means for them.
Big data is a collection of data from traditional and digital sources inside and outside a company that represents a source for ongoing discovery and analysis.2 In other words, it’s the whole deluge of digital and non-digital interactions companies have with clients – from web behaviour and social media interactions to traditional data derived from product transaction information, financial records and interaction channels.
Often referred to as the crude oil of the digital world – highly valuable but useless if unrefined – big data has the potential to decode human DNA in minutes, find cures for cancer, accurately predict human behavior, foil terrorist attacks, pinpoint marketing efforts and prevent diseases. It allows companies to understand their customers’ needs with incredible precision and craft their services and products with a much more personal approach. That is, of course, if the company knows what to do with the raw figures pouring in from various sources.
The volume, velocity and variety of the data require much more than an analyst with Excel skills to translate it into business insights and intelligence. We’re talking about strategies, software systems and whole departments focused solely on big data analysis. And the companies who have these factors in place are wasting no time turning the newfound knowledge into incredible new services and products.
Five years ago, Google scientists were the first to track the spread of influenza across the U.S without needing a single medical check-up. It only took them a day to assemble a picture of the spread, whereas the doctors could come to the same conclusion within a week. Google was faster, because they were tracking the outbreak by finding a correlation between what people searched for online and whether they had flu symptoms.3
That was the infamous trigger that caused Big Data to become a household name. Suddenly, everybody was trying to figure out what the vast flow of untranslated knowledge being passed through companies every day in various forms, could be capable of. More precisely – how the people, companies and governments could benefit from the information it holds. Every move we make in the modern digital world has a potential to be recorded as a highly valuable piece of data about our habits and needs. Does that sound scary? Maybe it does, but if we’re being completely honest with ourselves, we’re already enjoying the benefits of it.
Analysing and implementing the results of big data has a circling effect. In business, the information is used to create better and more personal products and services for the customers, who in turn respond with stronger brand loyalty and therefore generate bigger revenue for the companies. For example, Amazon has been leveraging big data for years, generating personal recommendations to their 152 million customers by mining their customer behaviour data. That even includes individualising the Amazon web page according to the customer browsing it.4 In healthcare, big data could help cut the costs by $300bn every year in the U.S alone by driving efficiency and quality. Real-time traffic monitoring with personal location data could help people choose smarter routes to their destinations, saving drivers an estimated 20bn hours on the road and $150bn dollars in fuel consumption.5
Could big data transform financial services?
Big data has been collected in financial services for years – transactions, log data, call recordings, emails – but the analysis of that information has been quite arbitrary, if not entirely absent. Still, experts say this kind of data entails highly valuable guidelines. For instance, analysing transcripts of call center conversations and interpreting nuances of language, such as sentiment, slang and intentions could bolster efforts to understand behaviour and preferences and improve the overall customer experience.6
But these insights could be taken far beyond customer service. One of the most burning questions in the financial sector is security – how to comply with the high security regulations without unnecessarily burdening customers. By harnessing big data in the right way, financial institutions can spare their customers from the extensive KYC procedures and skip straight to the business. Leveraging big data for customer background checking could save time in the on-boarding process, resulting in lower costs and a happier customer. The open source data about each individual is also much more reliable than having the customers prove their identity based on their electricity bill – a measure still used today.
In addition to that, it allows financial institutions to map their customers’ real-time behaviour patterns and thus detect any suspicious activities done on the customer’s name within seconds and alert the customer before he would ever have noticed. This could help detect identity fraud, money laundering and terrorist financing with incredible speed and precision.
So could big data really transform financial services? The answer is: yes, absolutely. However, given that traditional banks’ hands are still very much tied by their legacy systems, implementing modern big data analytics tools in these systems is like trying to use the “Find” command on a stack of papers. Therefore, this innovation is much more probable to come from the younger generation of financial institutions popping up in the fintech start-up scene where the big data analysis tools are integrated into the systems from the get-go.