When Bank of Nova Scotia hired Neil Bartlett, the former chief technology officer for business analytics at International Business Machines Corp., the bank's internal announcement made it clear that his hiring marked an important shift in strategic direction.
"Data is going to define banking in the next five to 10 years, and building a world-class analytics strategy for Scotiabank will be critical to our success in an increasingly digital economy," the December announcement said.
Scotiabank isn't alone in its vision of a data-driven future: Globally, banks are embracing the potential of analytics, which crunches all sorts of data to get a better understanding of consumer needs and behaviour.
Analytics not only gives banks a better way to market their products – say, by offering the right credit card to the right person at the right time; it allows them to verify information and streamline online applications, ideally making banking far more efficient at a time when banks are keen to cut costs as revenue growth slows and financial technology competitors emerge.
Analytics can also be used to develop nifty services, such as using location information to identify bank machines in your vicinity, or voice identification to displace cumbersome passwords.
Banks and other companies have always embraced consumer data, of course. But better technology and rising consumer expectations – driven by experiences at companies such as Amazon.com and Netflix – are now pushing its use dramatically.
"The two processes feed on each other," said Michael Zerbs, Scotiabank's co-head of information technology and enterprise technology. "The more customer expectations grow and are satisfied, the more demand there is for [analytics] tools. And the more demand there is for tools, the easier it is to support those [customer] expectations."
Scotiabank isn't trying to develop everything itself.
In January, the lender provided $2.2-million to create the Scotiabank Centre for Customer Analytics at the Smith School of Business at Queen's University, allowing professors, students and bank executives to collaborate on research projects. The collaboration can also help satisfy strong demand for data scientists at the bank.
Within the bank, one of the key challenges is to connect all the areas that have been developing their own independent analytics capabilities, from risk management to wealth to retail banking, and make them more systematic across the bank.
"How do you bring it all together, how do you connect it, how do you create the tools that support more scalable, repeatable analytics?" Mr. Zerbs said. "The urgency is to bring these different pieces together."
The bank must also change its approach by becoming faster and more responsive to customer needs. Traditionally, analysts tested a single hypothesis over a number of weeks or even months. Now, they can use technology to test different approaches – by varying credit card offers to different groups of customers, for instance – and learn immediately what works best.
"It's much more of a trial-and-error approach," Mr. Zerbs said. "And what you get is a much faster ability to learn about what goes on in the market, and a much richer set of potential solutions."
Nonetheless, he believes the use of analytics to extract insights from unstructured information is evolving quickly, leaving plenty of work over the next decade.
"Fundamentally, we're probably at the beginning of the process," he said.
Banks also have plenty to learn about staying in line with consumer expectations, so that customers understand that data is used to know their preferences rather than alarm them or invade their privacy.
"The fundamental rule is: Think about why the customer gave you the information and what do they expect you to do with it? And if in doubt, ask," Mr. Zerbs said.