There’s More Than “Data” in Data Science

Chris Marx Hi, I’m Chris Marx, senior investment strategist at AllianceBernstein, and I have the pleasure today of having Jonathan Berkow with me to talk about our efforts in data science. We’ve all been struggling through this COVID crisis for the last few months and the human toll that it has extracted. And that toll is extended onto economies and business practices and obviously the capital markets as well. And Jonathan and I are going to talk about ways we’ve been able to use data to help understand the world around us and understand some of the implications that might have for some of our investment decisions.

Jonathan Berkow What’s been interesting about this crisis, is that analysts come to us looking for answers. They can’t visit their companies. They can’t travel around the world to see what’s happening in the global economy. And so, they’re looking for information that can help them fill those holes. So what we’ve done is tried to build a virtual war room where they can go and find information on activity levels, information on supply chains and other kinds of data like that. It can help them fill in the gaps between what they can see out their window and what’s really happening in the world around them.

Chris Marx Can you talk about some of the examples of how the analysts access this information? Because I can imagine again, it could be overwhelming if it’s just raw data.

Jonathan Berkow I think that’s an extremely important point. Data that is just kept in the database in raw form is totally useless. So, what we try to do is make sure that all the data is easily accessible for analysts at any level of background in programing or data analysis. We want them to be able to click, click, click and get to the, the data and the answers that they need.

Another area that’s incredibly important and shouldn’t be understated is the collaboration we have with our fundamental analysts. A lot of these projects are extremely iterative. When an analyst comes the question needs to be refined, so our data scientists can tell them how they refine and how to help, help them look for data that’s appropriate or data that may be available via web scraping or other techniques. And we can help them iterate and find a better way to answer that question. And so, it’s a learning for both sides. And that’s why it’s incredibly important for our analysts to have a good relationship and collaboration without fundamental analysts. And you really get a lot of interesting insights by having that close relationship.

Chris Marx Yeah, that collaboration is really, it’s so important and it sounds soft, but I know from, from having worked here a long time, it takes a lot of effort to develop that connective tissue with all the different teams and really work together. And it’s nice to see it benefiting in this field as well.

Jonathan, one of the things that’s been clear to me is that a lot of these efforts you’re going through are allowing our team to do a lot of the same tasks, but much more efficiently across larger data sets and ultimately much quicker and allow us to make better investment decisions. Really fascinating stuff. I just want to thank you so much for your time.