The largest asset owners still struggle when it comes to managing the vast amounts of data now available to inform investment decisions.
This is according to new research examining how the world’s biggest pensions, sovereign wealth funds, and endowments handle data. The paper, from Ashby Monk, executive director of Stanford University’s Global Projects Center, and Kensho Technologies’ Daniel Nadler and Dane Rook, is part of a larger study into the changing technologies of institutional investing. Monk and Nadler are also columnists for Institutional Investor.
The authors outline a new way for them to maintain higher quality data while using it efficiently. They define data as “any recorded measurement or observation about the world” – for example, analyst expectations about stock market performance.
“Many Giants face significant challenges in appropriately managing data, which limits their ability to use data effectively and restricts long-term performance and innovation,” they wrote. “In our increasingly digitized world, a growing share of actionable investment information is derived directly from data, and most Giants are missing out on these opportunities.”
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Monk, Nadler, and Rook analyzed the data management abilities of large investors through case-study and survey research, drawing evidence from investing symposiums held at Stanford in May 2016 and 2017, as well as more than two dozen formal interviews conducted over an eighteen-month period ending in spring 2017.
“None felt that their organization uses data as efficiently as it could, or should be doing,” they wrote. “All grapple with issues of data quality, in the sense of struggling to understand whether systems and processes in their organization reliably deliver appropriate levels of quality.”
Low-quality data is a problem, they continued, because it could lead to “flawed or under-confident decision-making, as well as encumber decision-makers with delays and extra effort from needing to independently verify data accuracy.” This could, in turn, lead to worse investment performance and encourage outsourcing investments to managers with better data capabilities, resulting in more fees and expenses.
The authors also warned that a lack of quality data could sabotage innovation efforts. They said the role of data quality as a value driver for institutional investors is only likely to go up over the foreseeable future.
Given these data management struggles, Monk, Nadler, and Rook suggest a “data budget” approach, wherein investors can “plug in” data to the existing budgetary frameworks used by many allocators to manage resources tied to operations, risk, and governance. For example, an organization’s existing risk budget could be modified to take into account the risk of data being wrong.
“In the current setting of scarce opportunities for sizeable investment returns and yield,” the authors said, “institutional investors need to continually innovate to meet the performance expectations placed upon them.”
They added that “Of all the available improvements that might be made, technological changes appear the most feasible source of meaningful – that is, deep and durable – innovation available to institutional investors.”