- The biggest challenges facing the widespread adoption of data by private-equity firms are human.
- There must be collaboration between data scientists and investment professionals, and those in data roles must approach data-sets with one goal upfront, rather than looking for insights in a vast trove of information.
- Those were among a handful of key takeaways offered by data professionals at this year’s SuperReturn conference in Berlin.
- Business Insider sat in on a panel discussion of alternative data and then interviewed several professionals to better understand the challenges and opportunities for data in the private equity industry.
- “It’s the challenge of collaboration and communication for a data scientist to walk into the room and explain what it is they did,” said Matthew Granade, chief market intelligence officer at hedge fund Point72.
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To understand why the use of data in the investment process is only in its early stages in the private-equity industry, look no further than conversations at a massive industry gathering this week.
In Matthew Granade’s mind, there are at least two big challenges in using data to invest.
The chief market intelligence officer of hedge fund Point72 told an audience at SuperReturn here in Berlin that one challenge is simply finding actionable insights from large swaths of information.
“There are thousands of data sets and only a handful of them are actually useful,” he said during a panel discussion about alternative data on Tuesday.
“Take a credit card,” he went on. “A lot of them have sample-size issues and biases in their data. There is a huge process of just getting to a useful data set.”
But that’s only the beginning.
Afterward, there is the challenge of explaining the importance of those findings to the firm’s investment professionals and collaborating with them during the investing process.
“My view is that we have the best portfolio managers in the world,” said Granade. Most, though, he said, “are not statisticians; they are not data scientists. It’s not what they grew up doing.”
“It’s the challenge of collaboration and communication for a data scientist to walk into the room and explain what it is they did … and what risks there are, in a sensible and clear way,” he said.
How to get data analysts to work better with investment professionals
Those comments touched on a cultural issue that some sources tell Business Insider has served as a road block for the expansion of data analysis within PE firms, which have traditionally best compensated and empowered their investment professionals, with others in the organization servicing their work.
Afsheen Afshar, who previously served as chief artificial intelligence officer at Cerberus Capital Management and is now a managing director at his own investment firm, Pilot Wave Holdings, agreed with Granade’s comments about collaboration as a challenge.
“I’ve seen the same movie so many times,” said Afshar. “EQ is more important than IQ in deploying this capability. There are many who have high-performing degrees who often forget about that.”
The pair didn’t get into specific examples of instances where they saw lack of communication become an issue, but Pamela Hendrickson, chief operating officer of private equity firm The Riverside Company suggested that a question of judgment is at the heart of it.
“I think sometimes, especially with our younger team members, there is a tendency to over-rely on the data set,” she said.
“We need the combination of technology and human judgment.”
Data was far from center stage at SuperReturn
Data, which has so far been viewed as greenfield opportunity rather than a must-have in private-equity firms, fittingly appeared in the earliest part of SuperReturn’s agenda, before all the biggest private-equity CEOs showed up and made headlines.
Instead, the data conversation was part of a pregame, which went under the radar to many people attending the main event. Business Insider sat in on the panel and then spoke with several data and technology professionals afterward to better understand the biggest challenges and opportunities in employing data — including alternative data, meaning non-traditional data sets that the hedge fund industry has already sized upon with hopes of gaining an investing edge.
Specifically, though, we were interested in learning more about data use in private-equity.
And Granade, even though he is a hedge fund executive, offered some comments that applied to the PE industry.
For instance, he noted that it’s important to have an end-goal in mind when approaching data.
“You can swim around in a pool of data and do all sorts of crazy stuff,” said Granade. But he said you have to ask yourself: “What are you trying to do and is that data set going to help you do it? If you’re trying to predict Chipotle’s earnings, can that line item get you want you want?”
In response to a question from an audience member, Granade also refuted the notion that the commoditization of data posed any threat to the role of the investor.
He said that data is simply one insight out of many, and that it is far more important how humans analyze the data, rather than the existence of the data itself.
“We produce whatever returns we produce on a lot of commoditized data,” he said. “People who analyze it well do well with it, and those who don’t don’t. For us, it’s another input. I don’t worry too much about the commoditization.”
Private-equity firms could eventually use data analytics as a fundraising selling point
If there is a commoditization challenge, private-equity firms, for their part, don’t seem to be contributing to it.
Vishal Shah, a director in the data intelligence group of financial services outsourcing company TresVista, told Business Insider that private-equity firms are not yet selling their data to outside parties, like hedge funds, which he thinks could be a good business later down the line, with the caveat that they anonymize the data.
Such a sale would go “straight to the bottom line of private-equity firms,” he said. “Currently they aren’t doing anything with the data.”
Shah also believes that there is an opportunity for PE shops to fundraise with data analytics capabilities as a selling point. This hasn’t yet been done, he said, but he’s confident it will develop in three years time, after specific examples develop where data was responsible for boosting PE returns.
To the extent PE shops are embracing data, Zineb Sebbar, product manager of investment software company eFront, said use cases include due diligence and deal sourcing processes.
“For example, sentiment analysis of customer reviews or social media mentions are tools that can help [PE firms] appraise investment opportunities,” she said.
Still, though, many PE shops have yet to build out their own data science divisions like some have done, such as Cerberus Capital Management and Blackstone.
Hendrickson, the Riverside Company chief operating officer, said that her firm has used data to look at employment growth in portfolio companies, as well as to understand how to target sales prospects. But she said the firm still relies on outside vendors for data analysis.
“Most firms will give you a pretty cheap testing opportunity,” she said. “It’s a competitive world out there.”
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