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Five Hiring Considerations for the Alternative Data Revolution

Data Science and Technology

By Richard Newman  |  April 10, 2018

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 still relying on the same data architectures, software, and workflows that they have for many years.

When it comes to data specifically, the content the financial community has been using for decades is typically publicly available, actual, and after the fact. For example, if a merger is announced, news about it is delivered via filing or article; this is valuable data but it’s already happened. Even earnings estimates, which sound like they’re prospective, are generally a consensus of what multiple people have already said.

Enter alternative data.

The Coming Data Revolution

Much like it sounds, alternative data offers different sources of data (images, billing statements, heat maps, social media interactions), often at a higher frequency than the traditional filing and price data. Satellite data, for example, can offer real-time insight on the foot traffic or business a specific corporate location is attracting. If a private satellite can see that there are 80 spots open in a food chain’s parking lot and only four are filled, by aggregating information about all the parking lots that connect to that restaurant, you’d have the data to make an educated guess on how it’s performing. Access to this information offers a competitive advantage to those in the investment community who can see it, and since these satellites are usually privately owned, brokering deals with the firms collecting that data is becoming ever more critical to success.

While 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. That 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.

The People Behind the Data

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.

The questions you’ll hear most frequently in this space are, “what’s the career path for this market?” and “what should you look for when you're hiring?”

Here are five areas on which you should be concentrating:

1) Consider Diverse Backgrounds

A strong financial background used to be the key to success in the investment space. Today, however, while finance is nice, having a background in quantitative math or being a creative thinker is equally important. It’s no longer “get a CFA and follow the traditional path.” There’s a diversity of non-obvious skills that must be considered as alternative data explodes.

2) Prioritize Programming Skills

In nearly every industry, in-demand skills continue to trend towards technical and programmatic. Because alternative data is so complex, a diverse array of computer science and programming disciplines are crucial when building a team. Mastery of statistical packages like R and Matlab, as well as programming languages like Python, will power the widespread use of this data, and the sooner your team has them, the faster you’ll be able to decode and apply these diversifying alternative datasets.  

3) Keep an Eye on the Future

Following up on the point above, the pace of change means that today’s programming skills are not necessarily an indication of where this field is headed. Disciplines like natural language processing, artificial intelligence programming, etc., could easily overtake today’s standard programming skills as must-have attributes. Look for mindsets that can pick up new skills in addition to the skills themselves.

4) Hire People that Can Articulate Your Needs and Find the Right Providers 

As an investment manager, your challenge is not determining which data companies offer you the best information, or which are the most stable, but rather which alternative data sets have the coverage, history, and frequency you need. Even if you want to start incorporating satellite data, it’s easy to become so overwhelmed by the volume of choices that determining which vendors are valuable becomes a full-time job in itself. The faster you find a person and implement a process that can make these calls, the faster you’ll be able to incorporate cutting-edge data into your models.

5) Defer to Experts 

Perhaps most importantly, you can’t do it all yourself. Even the biggest players today cannot take on all of the tasks required to make the most out of alternative data. When you can’t hire a new role, you should be prepared to partner your internal skills with support from external experts. 

Right now, alternative data feels urgent because of the media attention it’s receiving and the emergence of companies who are generating new forms of data. While this data hasn’t yet been incorporated on a large scale, the potential benefits it heralds are groundbreaking on many levels.

While capitalizing on this shift may require new headcount spend, finding the right people to lead the alternative data charge is critical if that charge is to succeed.

Learn more about what FactSet is doing with alternative data on the Open:FactSet Marketplace 

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Richard Newman

Senior Vice President, Head of Content & Technology Solutions

Mr. Richard Newman is Senior Vice President, Head of Content & Technology Solutions at FactSet. In this role, he leads the development of FactSet’s off-platform products including financial data solutions and application technologies. Prior to this role, he was the head of the Quantitative Investment Management group, which focused on creating solutions for the quantitative workflow. Mr. Newman was a co-founder of Insyte, FactSet’s first acquisition in 2000. He is a CPA and started his career at Deloitte, Haskins, and Sells in New York. Mr. Newman is a graduate of the Wharton School of the University of Pennsylvania.