Harnessing Big Data To Gain Financial Advisor Insights

A few months ago, “big data” was named “the Most Confusing Tech Buzzword of the Decade” (so far) by The Global Language Monitor. It’s deemed even more confusing than “the cloud,” which came in second.

So, what is big data? I like this generic explanation, which I'm paraphrasing from a few comments I've seen on Quora. According to Vikalp Jain, founder of mozvo.com, four Vs characterize big data:

  • Volume—so large that its size becomes a problem
  • Velocity—how quickly data is coming or changing
  • Value—data should have intrinsic value to justify the processing
  • Variety—which refers to the breadth of sources and the lack of common (or any) structure

The financial industry is one of many industries exploring the challenges and potential of harnessing big data. It's estimated that the amount of data produced by financial markets has increased 10,000% in the last 5 years.

Closer To Home

The investment management function in your firm may be wrestling with its own big data initiatives (see Turning Big Data Into a Dashboard for Investment Managers), but there's a play here for Sales and Marketing too. Better understood data can refine the work of digital marketers, in particular.

A recent joint on-demand presentation by Lattice Engines and kasina provided a glimpse of how you may be able to use big data, and I thought I'd share a few notes.

According to Lattice's Ian J. Scott, big data for purposes of mutual fund and exchange-traded fund (ETF) distribution refers to:

  • Internal data your firm collects somewhere, including sales and redemption transaction histories at the advisor level, historical contact data (meetings/calls/emails etc.), Website click-stream, marketing campaigns
  • External data some functional department at your firm buys from Discovery, Meridian IQ, Coates, Market Metrics, DST and others. This includes fund and asset class performance data, advisor profiles (local office demographics, client profiles, licenses, schools attended, professional associations, etc.) and social media (e.g., LinkedIn connections).

In all likelihood, you are well aware that there’s data being collected and stored in silos. You know that’s not optimal but what going to change that? Here’s where the big data focus kicks in as the argument is starting to be made for structuring and organizing the data to drive sales and retain assets by deepening advisor insights.


As suggested by the chart above, the disparate data sources might be corralled to feed an overall understanding of advisors and advisor segments. With the data structured and organized, Scott explains in the presentation, asset managers can turn to predictive analytics to engage in “intelligent targeting of prospective advisors most likely to use your funds.” Conversely, a whitepaper on the Lattice site explains how big data can help stem redemptions.

As you sit through the full 45-minute presentation yourselves, you’ll hear that only the earliest of adopters are using these techniques and that the contribution is not perfect but “directional.”

“It’s not about increasing your effectiveness by 100%, it’s about being 5% or 10% smarter than you were,” says kasina's Lee Kowarski, who presents some slides on using multiple channels to engage prospects.

Confused? There's no reason to be. These are the early days. Now—before your competition has a significant head start—is a good time to invest in learning more and beginning to plan.