Golden Copy: Long Road to Disruption

Looking at financial services industry data operations practices along a continuum of iteration, innovation and disruption

In this column in recent weeks, I've considered the application of a couple different newer technologies to the data management realm—"regtech" and artificial intelligence. Remarks by Jeff Zoller, chair of the International Securities Association for Trade Communication (ISITC), at that organization's annual conference this past week (and in a follow-up interview), highlighted just how far the financial industry may still have to go to completely replace data operations systems with better methods.

Zoller cited and elaborated on a categorization of technology efforts by digital analyst Brian Solis, who studies the effects of technology on business in his role with Altimeter Group. Solis lists three types of technology changes: iteration—improvements on how existing processes work; innovation—the use of new processes; and disruption—the use of new processes that make existing processes obsolete.

Zoller sees financial industry operations as being somewhere "right in the middle" between iteration and innovation. "We're not making the old ways obsolete," he says. "We're trying to figure out how to take the old things and just make them better and change them to some degree. Firms are still trying to take in traditional sets of investment data and use them in smarter ways."

Predictive capabilities, based on data, applied to the design of investment products and strategies, choosing investment managers, and determining institutional investment managers' behaviors with handling cash flows and growth, undoubtedly can be greatly improved through machine learning or artificial intelligence. Doing so would see the industry doing even more than just innovation—edging toward disruption by potentially making old prediction methods obsolete.

Unstructured data, such as investment behaviors and patterns, the tone of commentary that industry analysts offer, and social and economic behavior, if harnessed, can also support disruption—going beyond innovation, as Zoller points out. Overall, Zoller says, he isn't so surprised that the industry can be slow to react to potentially disruptive technology capability such as deriving insight from consumer behavior, but he sees it as "something we need to pick up the pace on."

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• Topics
• ISITC
• Machine learning
• Regtech
• Artificial intelligence (AI)
• Data Management
• Data