Asset owners are not great at data governance, but it is key for better investment decision-making, which can lead to higher investment returns, according to a working paper from Addepar and the Stanford Initiative on Long-Term Investing.
The paper, titled Governing Your Way to Better Long-Term Returns, was written by authors Dane Rook and Ashby Monk.
A Q&A with Dane Rook, on this recent research paper.
They described data governance as “ensuring that the right data – and, in particular, the right quality of data – underpins each investment decision.” Data governance differs from data management, however, and asset owners should understand the difference.
“Simply managing data isn’t the same as governing data for better decision-making – which is a point that’s misunderstood by many investors,” the authors wrote. By implementing good data governance, asset owners can extract long-term alpha, but most investors are approaching data governance poorly, they said.
Good data governance has three main hallmarks, according to the paper. It is ever-evolving, it is people-centric, and it is properly resourced.
the authors differentiated between data management’s potential to contribute “operational alpha” to a portfolio, which they say is “warranted because a large fraction of most investment organizations’ internal data is un(der)-utilized. Improvements to DM can make more of that data discoverable and usable, while also reducing storage costs, eliminating redundant data subscriptions, etc.” Data governance, however, is different, they wrote.
“In the present piece, we avoid any deep discussion of the specific architectures and tools that support good DG. We do so for two reasons: first, to concentrate attention on the people and process elements of DG; and second, because the question of architecture as it relates to DG is a complex and extensive one (which we will be covering in a subsequent paper),” Rook and Monk wrote in a footnote. “That said, readers should bear in mind that the quality of DG is tightly coupled with the quality of technology/architecture (both the tools and systems that directly support DG, and the data tools and systems that DG seeks to govern).”
The authors interviewed and studied dozens of asset owners since 2016, with most of these investors managing between $10 billion and $1 trillion in assets across geographies.
Management is Not Governance
The paper stressed the difference between data management and data governance. “[Data management] mostly deals with how efficiently data is handled, whereas [data governance] is concerned with how the quality of data affects the quality of investment decisions,” the authors wrote.
Specifically, they cite the quality of data, including its accuracy and completeness; the provenance and chain of custody of a specific data item, its transparency; and the existence of functional mechanisms “to discourage the use of the wrong types of data as the basis for particular categories of decisions (e.g., data that isn’t of sufficient quality can’t be used as the basis for trades).”
According to the paper, there are more incentives for consultants and technology companies to offer data management solutions to asset owners because it is easier to sell these products, as the best data governance practices are still insufficiently understood.
The authors wrote that they would release more in-depth practices to implement good data governance in future papers.
Having access to, and ensuring your data is high quality “[data governance] affects more than just operating alpha: It impacts the quality of overall alpha, since high-quality alpha relies on high-quality decisions.”
According to asset allocators that were interviewed, good data governance has a distinct impact on various dimensions of their returns. Better data governance has reduced “noise trading” or trading on false signals. Good data governance has also made portfolios more efficient and manageable.
The paper stated that although there are some overlaps between the two, investors should have differentiated data governance and data management strategies.
“Good data governance (DG) is key to improving the quality of an investor’s data, decisions and long-term returns. Most investors, however, lack a cogent DG strategy and fail to appreciate the crucial differences between DG and data management,” the paper concluded.
“Therefore, savvy investors who are able to implement DG best practices can build serious competitive advantages, as long as they ensure that their chosen approach to DG aligns with their resources and objectives.”
Vital in the AI Age
Asset managers and asset owners have been adapting artificial intelligence tools to their workflows, but most of these institutions are using AI for clerical tasks, such as summarizing meeting notes. Data governance is important when incorporating AI tools.
“However, AI tools are not flawless, and investors will — for the foreseeable future — need to audit AI outputs in terms of being able to trace those outputs back to the underlying data that informed them. This traceability will be helped by sound DG. DG can also help in improving the quality of AI outputs by driving higher-quality data inputs,” the paper stated.
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Tags: Addepar, Ashby Monk, Dane Rook, Data Governance, Stanford, Stanford Research Initiative on Long-Term Investing