How to Harness Big Data to Grow your Business
With markets becoming ever more challenging for portfolio managers wishing to allocate client assets and business pressures mounting on financial advisors, many see big data as part of the solution for both these challenges.
But using big data is itself a challenge for advice firms. And “the algorithm is often not the hardest part,” Gauthier Vincent, lead management consulting partner of Deloitte, says. The true challenge of using big data in an advice firm is understanding and implementing the required technology and staffing your firm appropriately.
Reaping big data’s rewards means “aggregating, organizing, cleaning, and putting information in a place where advisors can analyze it in real time,” to better understand business issues like asset allocation, client servicing or customer segmentation, Vincent says. And to do this, advice firms need both the right technology and skilled experts.
Firms need several software capabilities to harness big data including “predictive analytics, data mining, text analytics, native language processing, statistical analysis, business intelligence, and data visualization,” an Ernst & Young report shows. Creating such a data infrastructure is not always easy or inexpensive.
Deloitte creates “data factories” to help wealth firms gain data tools, Vincent says. “Data factories are the infrastructure that lets relevant structured and unstructured data be accumulated in ’data lakes,’” he says. From there data factories extract the information, analyze it and establish a layer of governance and regulatory transparency, he says. But “firms need to have data scientists for this” to work, Vincent adds.
Baron Silver Stevens Advisors specifically hired a staff member for data analysis, Michael Silver, CEO of the $610 million AUM RIA says. The firm’s data-focused staffer “uses nothing but data all day long to access multi-source information” on both “broad economic issues and client investments,” he says.
How wealth firms choose to develop big data capabilities varies based on asset size, and firms may choose either internal or external approaches, experts suggest.
“Large firms all have their own data experts in-house and many will add more and more data scientists to their payroll,” Vincent says. “A lot of wirehouses are also building data scientist teams in-house,” he adds.
But instead of taking on the substantial cost of forming internal teams, smaller firms might choose to contract FinTech partners, Candice Tse, head of U.S. market strategy for strategic advisory solutions at Goldman Sachs Asset Management, says.
Silver agrees. “Advisors will increasingly rely on FinTech for big data. FinTech changes so quickly, it’s easier to outsource services than to completely do things internally,” he says.
And rapid technology changes are hard for smaller firms to handle. For big data to be used correctly firms “need all of the components and it's very costly,” Tse says.
Tse spoke with FA-IQ at the Charles Schwab annual conference in Washington, D.C. and explained internal versus external approaches to big data infrastructure.