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4 Alternative Data Takeaways From FactSet's Experts

Data Science and Technology

By FactSet Insight  |  August 14, 2019

Over the spring and summer, FactSet’s content and technology experts joined a range of events to discuss the evolution of alternative data. While not a new concept, alternative data continues to change the way firms in every industry identify patterns and remain competitive. Here, we’ve assembled several key takeaways from these events that speak to the ongoing evolution of alternative data analysis, hiring, and application.

Expanding Alternative Data Applications

While alternative data allows investors to identify patterns that can lead to novel investment opportunities, it's also become a boon to risk analysis. 

“Today, we are increasingly seeing the adoption of alternative data not just for the alpha generation use case but also for the risk management use case. Deeper company, industry and economic insights are helping investors spot opportunities as well as risks.  However, as investment managers are evaluating new exciting sources of data they also must make sure strong vetting and due diligence processes remain in place.

Across our global client base we are seeing adoption of many more data sources, and we are seeing this occur both on the buy side and the sell side. Firms are hiring data scientists by the dozens, sometimes by the hundreds, to identify and evaluate new data sources that can be used across the enterprise.”

-Lauren Stevens, Senior Director, Content and Technology Solutions

Hiring for the Data Revolution

Alternative data offers the dream of taking unique and novel information and distilling it into enlightening insights and decisions, it is also cumbersome, voluminous, unstructured, complex, and incredibly difficult to work with. This challenge is why jobs like Data Scientist and Data Engineer are becoming increasingly important. The industry needs people who can find signal in the noise.

“Hiring engineers and data scientists is a new but necessary cost for integrating alternative data. Data engineers are critical for turning newly purchased alternative datasets into a format from which value can be derived. Data engineers can organize, transform, and link any data. Data scientists can then more easily take all of the datasets and discover alpha-generating signals. They have the analytical and technical skills to turn data into intelligence.”

-Richard Newman, SVP Global Director of Content and Technology Solutions

Improving Risk vs. Return Profiles

Access to new data offers a competitive advantage to those in the investment community who can see it, and that portion of the community is on the rise. 

 “Our research shows 64% of asset managers believe Alternative Data can help them beat an index benchmark and 59% of asset managers agree active managers can improve risk/returns profiles with it"

- Ed Piolet, Vice President, Regional Sales Manager, Asia

Dealing with an Ever-Increasing Volume of Data 

Alternative data offers different sources of information (images, billing statements, heat maps, social media interactions), often at a higher frequency than the traditional filing and price data. That being said, it is necessary to tailor your intake process to ensure that the new content is being incorporated successfully.

"The amount of alternative data produced from fintech companies today is incredible, and investors are looking for new ways to generate alpha and evaluate risk. We also see new technologies such as machine learning, artificial intelligence, with another key growth area in data science becoming a discipline.

Alternative data comes from multiple sources and arrive in multiple formats (structured and unstructured). One of the biggest challenge is the data connectivity. This is an area where FactSet can help solving this challenge, as data integration is a core competency of Factset."

-Elhams Berrhili, Vice President, Regional Director, Content and Technology Solutions

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