Thematically, data governance and distribution sounds less exciting than watching paint dry, and while they can be nebulous concepts, governance and distribution remain the building blocks of successful investment processes across firms, locations, and mandates globally. Understanding a firm’s present and desired states around these concepts is critical to asset gathering and asset retention.
Active to passive. Fee compression. ESG. Alternative data. Business intelligence. Digital transformation. While the catchphrases and buzzwords are inescapable, there are real governance issues that arise in pursuit of remaining relevant in a changing industry.
In this five-part series, FactSet’s Pat Reilly, Director of Analytics, will examine the theme of data governance and distribution through the lenses of data sourcing, integration, quality, analysis, and distribution across internal and external clients. Combined, these provide asset managers and asset owners with an overview of the key elements to be considered when constructing an efficient data governance and distribution process.
Part one will tackle the theme of data sourcing; the full series can be downloaded here.
Data Sourcing in the Rearview Mirror
In the beginning, there was data. Lots of paper and duplication of efforts. Then punch cards, floppy disks, room-sized computers. Few graphics and even less flexibility. Eventually, systems developed. Some of them even talked to each other. But most did not.
Security master, reference data, pricing, holdings and transactions, benchmarks, fundamentals, estimates, derived analytics, alternative data. The list continues to grow, with each addition complicating efforts for transparency, accuracy, and usability even further. There is a better way to connect these datasets, and it starts with effective sourcing.
We at FactSet understand this better than most. FactSet is known for industry-leading research and analytics solutions, but our roots stem from delivering company fundamentals around Wall Street via bicycle messenger over 40 years ago. Since then, FactSet has matured into a content machine with symbology and concordance at its core.
Leveraging a robust identifier waterfall built around an Entity ID allows for global, single security terms and conditions across asset classes to align with proprietary and third-party reference data (like sectors, ratings, or ESG data) while connecting downstream with client-provided and/or FactSet-collected portfolio holdings. FactSet does this for our clients by leveraging our proprietary symbology and concordance process, reducing the burden of data management whether on or off platform. This enables users to focus on their core competencies: generating alpha and servicing clients.
That kind of connectivity should be table stakes, even if aspects of it may seem akin to a dream state. It also creates a dilemma, however. Where do we go from here?
Taking data downstream for point-in-time analysis, benchmark-relative positioning, performance, attribution, risk management, or client reporting is a natural next step, and one that is made easier with a sound data architecture.
It is also meaningful to think about how to better utilize this singular source of data upstream for idea generation and portfolio construction. Linking to an issuer’s fundamentals (financials, filings, third-party research, management information, etc.) and estimates for analysis is critical, while aggregating issuers into industries or regions poses a different challenge.
The Evolution of Content Requirements
It is one thing to identify a recurring challenge like symbology or concordance that is experienced broadly across the industry, but what about future-proofing against trends such as ESG or alternative data?
To determine which datasets are most valued today and gain insight to where the industry sees future value, FactSet surveyed Quantitative Analysts, Data Scientists, and Chief Data Officers from 50 global institutional asset managers.
Unsurprisingly, benchmarks, prices, and fundamentals lead the way for today’s core data sourcing priorities while business performance information, ESG, location, and social media dominated front of mind from an alternative data context.
The future state indicates a change in preference and focus. Significantly, core data like market aggregates, benchmarks, estimates, mergers and acquisitions, and private equity hold less import in the future relative to present day. They are replaced by datasets such as corporate activism and governance, fixed income, industry and sector classifications, private company, and ownership.
Our findings also show that the perceived value of alternative datasets in the future varies considerably from today. Credit scores, location information, and search trends all dropped in perceived value, whereas datasets like ESG, shipping information, supply chain analysis, geolocation, web traffic, and sentiment all saw marked increases in perceived value. Being able to easily explore and validate alternative data without the need to install or integrate will allow for faster development and acceptance in the space. At FactSet, we’ve solved for this need by developing an open platform which allows for this discovery and exploration independent of existing infrastructure.
Takeaways for 2020 and Beyond
This leaves us with three takeaways. First, while specific data elements will come in and out of favor, sound architecture that facilitates concordance and connectivity while remaining source-agnostic is imperative. Second, ensuring consistency across third-party platforms and internal solutions reduces business risk and smooths the investment process. Finally, the ability to easily discover and test new content for “fit” in the investment process and/or portfolio will become a prerequisite; the daily data management burden to keep the business operating regularly is too great to add a sandbox for exploration.
As requirements for proper sourcing of data are met, integrating this data into the existing technology landscape and investment processes becomes the next hurdle to clear.