The largest asset owners still struggle when it comes to
managing the vast amounts of data now available to inform
This is according to new research examining how the
worlds biggest pensions, sovereign wealth funds, and
endowments handle data. The paper, from Ashby Monk, executive director
of Stanford Universitys 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.
[II Deep Dive: 1996 Called. It Wants Its Tech Back]
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
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
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 organizations 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.