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To stay competitive, banks need to understand their customers so they can offer the right products at the right time. Despite having vast amounts of information about their customers, banks are not using this data meaningfully for competitive gain. Recent research by IBM revealed that banks are lagging behind other industries in terms of the scope of data that they use and their analytic capabilities.

What is "big data"?

It seems that 2013 has been the year of "big data". In loose terms, "big data" means a greater volume of data than an organisation can currently analyse. Banks are acknowledging that the amount of data they collect, store, and analyse is overwhelming. Collecting hoards of customer data and failing to use it in a meaningful way is effort wasted.

In a recent study by leading financial industry researcher, Celent, 60% of banking executives believe that getting a handle on big data and analytics is a significant competitive advantage for financial institutions, with 90% believing that successful big data initiatives will define the winners in financial services in the future. Despite this, only 24% of banks in the study had implemented a big data solution.

Connecting the dots

One of the major challenges for banks is that data often exists in independent silos. This makes it difficult to know where to start in terms of a technology solution. According to leading banking consultancy, CCG Catalyst Consulting Group, banks first need to connect the dots across existing data. Banks should invest in technology that integrates existing data, and then use that as the foundation to build bigger.

As noted in its 2013-14 Global Banking Survey, Ernst & Young promotes an enterprise-wide approach as the only viable solution, rather than altering existing systems which may already be suffering from end-of-life issues and years of under investment. An enterprise-wide approach also means that banks can address multiple needs using one data management solution. According to Microsoft, for instance, banks can combine product and service marketing with fraud monitoring. A bank can analyse its credit scoring for credit card applicants, and if it wants to lower its acceptance score, it can determine the impact on its default rates while targeting customers and examining pricing.

It's also about people

The big data problem isn't solved simply by investing in technology. According to Deloitte, many leading organisations recognise that harnessing data requires a special blend of talent and technology – man and machine – to create the magic of breakthrough insights.

In fact half of the banking executives in the Celent study, cited lack of analytical talent as the biggest barrier to implementing a big data solution. Subject expert, Scott Bales from Movenbank, suggests that banks may want to look outside their organisation for help. Bales proposes that banks look to data scientists who can create stories from data to derive patterns, trends, insights and add context to interactions with consumers.

Data should have a face and a purpose

Deloitte also advocates that data should have a face – to be made personal, to foster a more meaningful relationship, and to be expressive. Sam Maule from Carlisle & Gallagher Consulting Group reinforces the importance of collecting data for a purpose. Maule believes there should be less focus on drilling into data to create executive dashboards for PowerPoint decks and more focus on drilling into the contextual data that matters for customer engagement. It must ultimately lead to actual application and engagement with consumers.

To remain competitive banks will need to embrace a culture that best manages, and meaningfully uses, ever-increasing amounts of customer data, and early movers will have an advantage over those still grappling with where to begin. May the bank that best leverages its data (soonest) win!