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21-Oct

Big Data: More Than Analytics

Big Data: More Than Analytics

Monday, October 21, 2013

The most valuable asset that a commercial bank has is its customer data. This resource, when leveraged correctly, provides the bank with the ability to engage with its customers and personalise their banking experience in a hugely compelling way.

With customers increasingly engaging with banks across multiple touch points, they will immediately recognise if there is a change in the quality of their personalised experience across each touch point. It’s here, too, that big data plays a crucial role, in ensuring and consistency in the services being delivered.

Yet, despite the value that big data offers, it’s still a project that many banks have put on the back burner. In fact, according to Gartner statistics, just 34 per cent of banks have invested in big data. In a small market like Australia, the ability to utilise data as a competitive advantage will raise the barrier for the relatively minimal number of competitors to gain access to that customer. So now is the logical best time to start making big data investments.

This goes well beyond analytics. Big data analytics is what many organisations will be pushing to market, and it’s a good conversation to have, because the insights gained though analytics is immensely valuable to a bank. But analytics do not directly add value to the external customer. While it is important to leverage the data collected on customers for analytics purposes, analytics in itself isn’t going to provide the kind of consistently personalised service that customers will appreciate.

After all, the typical customer is not going to feel comfortable with their bank collecting information on them if they don’t see a direct benefit of it to their own engagement with the bank.

Customers can (and will) get annoyed with their bank when their credit card is mistakenly restricted for suspect transactions that should never have been suspect. They will be even more annoyed when, after ringing the support line, they are subsequently bounced back and forth between operators for support over different issues. An effective big data implementation can minimise these frustrations, leading to happier customers that are serviced on a more efficient basis, while at the same time catching out genuine cases of suspect transactions.

There is a great opportunity for financial services organisations to better optimise their technology stack for analytics. The current implementations of Hadoop in banks can be improved and enriched. Facebook announced in late 2012 that it was adding around a half Petabyte (PB) per day into its Hadoop warehouses. Banks need to be doing the same, because when it boils down to it, both Facebook and commercial banks share something in common; their business models are built around customer data.