Financial firms have always relied on an array of data and technological processes to get the intelligence they need to make decisions. However, with the transition to today’s advanced technology environments occurring almost overnight, many organizations are falling behind by relying on the same data architectures, software, people, and workflows that they have for many years.
That being said, firms are beginning to see the need for technologies that can help give them an edge. For example, alt data is well beyond critical mass in terms of institutional acceptance. Today, about seven of 10 asset managers believe that using alternative data gives them an investing edge over competitors and corporate budgets for the information increased 52% in the past year, according to a Greenwich Associates report released in May.
During this year’s Cantor Fitzgerald Innovation Summit, Rich Newman, Senior Vice President and Global Head of Content & Technology Solutions at FactSet, discussed these undercurrents, what they mean for the industry, and how firms can employ strategies to protect themselves from the associated risks while reaping the potential rewards. Here are three key takeaways from Rich’s conversations at the summit.
While alternative data and other cutting-edge technologies fulfills the dream of taking unique and novel information and distilling it into enlightening insights and decisions, it is also a difficult prospect to manage. That challenge has given rise to careers such as Data Scientists and Data Engineers, which have become increasingly important. The industry needs people who can find clear signals in the noise—enter the Data Scientist.
According to Rich, “The growth in the market that we've seen now is around data science and it’s come full circle in my career. When I started, the quants, were in the corner; now they’re a critical resource. Firms are hiring data scientists, quants, people who can code in Python, code in environment to test data, or come up with new strategies to generate alpha to minimize risk. And that's been the big change in the market.”
At the same time, an ever increasing volume of data challenges the industry to move as quickly as possible to make innovation happen. This means that active investing is likely to remain critical to finding alpha.
“The good thing about this market, is people are always going to want to get an edge. There's always going to be ways to find alpha. That is the alternative game. And when you think about that market now, we're going to alternative data, it's never-ending. It's a market that's going to keep growing, because the iterations—and if you encapsulate various types of queries and things that you're trying to do, there's just infinite ways to try to generate alpha now. That's been the big shift. So, to me, active management is going to require that creativity,” Rich says.
As Rich points out, in nearly every industry, skills continue to trend towards technical and programmatic. Given the complexity of getting data technology implemented, a diverse array of computer science and programming disciplines are crucial when building a team. However, these hard skills are not the only candidate attribute to take into account.
According to Rich, “Firms, the market, and publications are sometimes overestimating how quickly organizations are going to production with cutting edge data. A lot of it's in the evaluation stage now. What I find in data science is, again, it's the evaluations, building an environment to test data, and begging to formulate hypotheses that matters today, because trying to integrate that data for production can take months, if not years when you’re unfamiliar with it.”
He continues with, “What we're finding is there isn't a limit to how many [people] can do data science; people can learn that. People can learn programming skills. The key attribute now, is really thinking differently. And I like to say it's a great opportunity for liberal arts majors and people who come from different background, because the idea to find alpha in that and gain new insights and data requires a different way of thinking.”
While the pace of data incorporation has a tendency to make skills like coding attractive, it sometimes happens at the risk of underestimating the creative skills also needed.
While the importance of human capital can’t be overstated, artificial intelligence’s role in helping firms incorporate novel data and technologies is on the rise.
Rich says, “Machine learning is helping to link data together, but you still sometimes need people to help. You still need that. To do it right requires sophistication and data knowledge. You can't just slap on unstructured data. That's where machine learning and AI is going to come in. It’s going to help firms go through lots of data and look for trends, but ultimately, a person is making the decision.”
As Steve Cohen, EVP, COO, and Co-Founder of Basis Technology pointed out in his article The Problem with Magic, AI has a complexity problem. The technology can seemingly do anything and it’s getting more and more difficult to explain how it does what it does. This is challenge for financial services firms who hope AI can light the path to better trades and lower costs. If they can’t get AI to comply—or get a good handle on what it really does—then they lose out on the technologies true potential.
Firms are realizing the benefits of embracing new technology and data, but they’ll need to keep in mind that incorporating it into their standard practices is not as easy as flipping a switch. As Rich points out, learning how to hire for the data revolution, work alongside AI, and finding new opportunities to utilize rapidly evolving technologies is likely to continue to play a role in their plans for a long time to come.
Learn more about what FactSet is doing with alternative data on the Open:FactSet Marketplace