A screen displays the trading price for Bank of America and Black Rock stocks on the floor of the New York Stock Exchange, January 17, 2013.
In the competitive world of finance, success is driven by deep-and-wide knowledge of markets, products and customers.
Yet when it comes to tapping big data to build a competitive edge, financial companies are "massively behind," an expert panel told an audience recently at a financial and risk summit in Toronto. "It's almost ironic because in financial markets, information really is the lifeblood of what we do in trading and investing," said Debra Walton, chief content officer of financial and risk at Thomson Reuters, a New York-based media and information firm that organized the summit.
"A hundred years ago traders and investors would hang out in dining rooms at lunch time to gather information on markets."
Walton pointed to a recent survey by her organization that found only 5 per cent of financial firms had experience and knowledge of big data. There are a number of factors behind this slow rate of adoption, she said, including the industry's tendency to focus more on technology and processes that help financial firms ensure regulatory compliance. There's also a lack of positive use cases that demonstrate how companies can make money using big data.
"To a large extent, those firms that have worked that out are not in a rush to share with the industry," Walton said. "There's a propensity in the market to keep successes to themselves."
The panel, which included a poll of audience members about their data analytics activities and priorities, was moderated by Globe and Mail banking reporter Tim Kiladze. He asked how organizations can prove in a fixed time frame that their analytics projects are worth the cost.
Massoud Charkhabi, director of advanced analytics at CIBC's retail and business banking arm, said the best way to do this is to measure return on investment in financial metrics. Instead of measuring, for example, how many new customers are brought in thanks to analytics information, companies should be looking at how much revenue these new customers generated.
Charkhabi acknowledged the difficulty of tying the use of analytics information directly to results. "It requires a lot of discipline to see how much of your change can be attributed to analytics information," he said. "It's important to stick to financial measurements … if you don't have the discipline and patience to stay focused on financial metrics, you will very quickly lose a lot of credibility in your organization and become marginalized."
At the same time, organizations have to ensure they're managing expectations around their analytics projects. "When people talk about big data, they talk about it as a saviour," Charkhabi said. "But the gains are won much harder and take much longer to develop and materialize than usually thought. There hasn't really been one 'a-ha' moment in analyzing data, it's more incremental."
Another big challenge for financial firms and other companies looking to implement analytics is the shortage of workers with strong data science skills. Walton said a lot of data analytics workers seek jobs at high-tech companies such as Google or Yahoo, which they view as "sexier" than working for a financial firm. To attract this talent, Thomson Reuters has built a data science R&D centre in Silicon Valley, she added.
Despite all these challenges, the financial industry is facing a significant data shift, Walton said. She's already seeing this at her organization, where customers increasingly want to link Thomson Reuters data with their own. Companies want to be able to pull data from outside sources, she explained. They want data on demand.
The use of big data will also evolve from largely descriptive to more predictive analytics, Walton said. "That's what I expect to see – using data for predictive results. How can we harness social media data and footfalls from mobile phones to see what Wal-Mart is going to be doing in the fourth quarter. And if that's the likely outcome, what's the action I should take?"
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