Managers Must Avoid 'Cultural Clash' as Data Teams Emerge: Report
Oct 29, 2018
Asset managers borrowing data-driven techniques to hunt alpha pioneered by their quantitative peers risk a "cultural clash" between data and investment teams, PGIM warns in a new report.
The boom in alternative data and the implementation of new data science techniques, including predictive analytics and machine learning, is helping managers improve returns. But fusing together investment professionals and data experts, who have recently joined and may come from different industries, is creating a new organizational risk.
Fundamental managers must "solve for the real risk of cultural clash between traditional analysts and data scientists trained to work with these new datasets," the PGIM report states.
Managers that navigate the issue stand to boost performance for public and private investment strategies by tapping this emerging field of technology. The tools in question include natural language processing, where computers scan documents such as earnings call transcripts for sentiment, using cellphone location data to measure retail foot traffic, or marrying real estate operating cost data with satellite imagery to identify tenant behavior, the report states.
As these technologies become embedded in fundamental managers’ processes, asset owners must determine which managers can successfully integrate data scientists within their investment teams, says Taimur Hyat, chief strategy officer of PGIM, the investment management arm of Prudential Financial with more than $1 trillion in assets under management.
"They come from different backgrounds and disciplines and using an investment process that lets these two people speak to each other [is key]," says Hyat, who is also the principal author of the paper.
Asset managers are rushing to build out data science teams, causing a fight for talent. Blackstone Group is in the midst of building a new data science team, while BlackRock and T. Rowe Price have tapped so-called data science "bootcamp" training schools for staff, as reported. Others building out teams include AB and UBS Asset Management.
CIOs aiming to vet prospective or existing managers can include a section in their request for proposals (RFPs) or due diligence assessments on technology preparedness, the report states.
Investors can specifically ask managers how technology has changed their front office investment management process in recent years, if they have considered hiring a data science team – and if so, how fundamental portfolio managers integrate their perspectives into investment decisions, the report continues.
As asset managers look for tech talent, "sometimes it’s hard to integrate folks like that into an organization that is fairly established in terms of the way fundamental research works," says Katie Vande Water, partner at executive search firm DHR International.
"There needs to be a real commitment to integrating the skillset that they have and recognizing how important each are to shaping the industry going forward," she adds.
Separately, the industry as a whole will need to "build new temples of data," due to the proliferation of data sources being used in the investment process, including unstructured data, locational or GPS data, and data from newsfeeds, Hyat says.
Data sets may be of different quality, he explains, and may also be subject to new regulation, for instance, the European Union’s General Data Protection Regulation (GDPR) privacy rules.
Private equity firms, in particular, have already begun to contend with the sweeping changes brought in by GDPR, which extends down to their portfolio companies, as reported. The law, which became enforceable on May 25, aims to protect data that companies collect from EU citizens, requiring firms to track identifying information they store and transfer across borders.