RBC Elements - Bridging Cultures, Building Collaboration

Published July 22, 2019 | 4 min read

Data scientists and equity analysts at RBC combine talents to raise the bar for research and produce differentiated insights for clients

Technology like artificial intelligence and harnessing new types of data is proving to be a game-changer in the financial services industry, but the introduction of new technologies always raises questions. Will they be accepted in the workplace? Will clients recognize the added value they bring? How can the new technologies best be integrated with existing human expertise, so they build on each other’s strengths?

These are some of the questions RBC faced, and successfully answered, in its innovative use of data science to produce advanced, differentiated equity research for clients. When RBC CEO Dave McKay called for the firm to prioritize innovation and technology in 2016, global research leaders seized on the opportunity to incorporate data science— a multidisciplinary field that involves AI, data analysis, and other specialized ways of crunching big and varied data.

But first, the team had to assemble the right talent and harmonize two very different cultures—a task attempted by many businesses and mastered by few. Here’s what RBC did right, and how its collaborative culture led to a new way of working that has paid off for clients and RBC employees alike.

 

1.Build non-traditional teams. When building the data science team, RBC leaders looked for experts with a wide array of backgrounds: statistics, engineering, even environmental science. The team members of what became known as RBC Elements needed outstanding analytical capabilities and data science skills—but they also needed critical “soft skills” related to on-the-job learning and communication. “None of our hires came from a finance or capital markets background,” says Faezeh Khabbaz, who has been with RBC Elements from the beginning and who now serves as head of data science for RBC Capital Markets. “So, it was important that the members of our team were quick learners. They also needed good communication skills so they could translate analysts’ business questions into data problems.”

2.Explore new ways of working. RBC analysts initially weren’t completely sold on the idea that data science could improve their work. They were experts in their fields; in many cases, they had covered a particular sector, and followed a specific set of companies, for years. Their work depended on their well-honed ability to produce complex financial forecasts from often-obscure corporate documents and their deep understanding of such fundamentals as earnings, return on equity, and cash flow.

What Elements brought to the table, says its founder and RBC Capital Markets head of strategic initiatives Fardeen Khan, was new ways of finding and analyzing alternative data—including weather patterns, online consumer reviews, geolocation data and more. “We wanted to bring in data sets that analysts hadn’t traditionally been able to access and use state-of-the art data science to manipulate it in ways that would produce differentiated insights,” Khan says. His team also aimed to help analysts corral massive data sets that were challenging to manipulate manually.

3.Learn to speak the same language. Analysts typically come to the Elements team with a question about a company or market that they want to answer, or a thesis they’d like to test. But they don’t necessarily know how to frame the question as a data science problem. That’s where the soft skills come in, according to Khabbaz. “Being able to ask the analyst the right question really matters,” she says. “We’re working in two different environments: Our team might need to write code in Python, a programming language, as they’re looking at the problem from a very technical point of view, while the analyst wants to know how our results will ultimately affect the stock of the company in question.” Analysts, meanwhile, are helping the Elements team understand what clients are looking for in research materials, and helping finesse their presentations accordingly.

4.Think big. Together, RBC analysts and the Elements team are redefining what’s possible when it comes to data analytics. For instance, earlier this year an analyst wanted to determine how many people Amazon should be able to reach with one-day shipping, based on the location of its distribution centers. “We thought we could look at the problem more precisely,” says Alison Chang, Product Manager for Global Research. “We were able to use location intelligence to map all the zip codes in which Amazon is actually achieving same-day or next-day delivery.” Ultimately, the collaborators determined that the megaretailer can already reach 72% of the U.S. population—a key finding that made national news.

5.Create a new culture of collaboration. Collaborations between analysts and the Elements team have created their own momentum. For instance, RBC Managing Director and consumer discretionary sector analyst Scot Ciccarelli was curious about the values Elements could bring to help him prove his thesis that Home Depot had location advantage over Lowe’s. Elements team conducted sophisticated demographic analysis to back up his thesis and collaboratively, he published a report on the research, which was later covered by Barron’s, among other news outlets. Since then, Ciccarelli has repeatedly brought project ideas to the Elements team, including the Amazon case. “In the beginning, it was challenging to get everyone excited about what we were doing,” Khan says. “But when you have analysts become the biggest brand ambassador of RBC Elements, it’s fantastic’—that’s when you know you’re doing the right things to drive momentum and adoption.”

A bit of concrete evidence that these two cultures have become one: The Elements team recently was walking an analyst through the results of a data-intensive project they had worked on together. In the middle of their conversation, the analyst—an expert in assessing the health of publicly traded companies, not a data guru—mentioned that he had recently come across a useful tool for scraping publicly available data off the internet. “We were unaware of this tool, and now we are using it,” Khabbaz says. “This wasn’t his job, but he went above and beyond to help make our lives easier. That’s a great example of bridging cultures and building collaboration among teams, and it’s amazing.”

Artificial Intelligence Insights

Explore
AIRBC Elementsdata science