The importance of curated data for robust portfolio and risk management
Only a few years ago, there was much hoopla in the global media around the term ‘big data’. Many people were led to believe that it had become possible to easily access and utilize massive amounts of data, resolving major challenges facing businesses and other institutions.
Why then did this fanfare surrounding big data seem to almost suddenly disappear? One likely explanation is that people came to the realization that simply having access to large quantities of data, in and of itself, does not make decision-making any easier.
In fact, in many cases, large quantities of data have had the opposite and unintended effect of making things worse, because people were now potentially drowning from having too much data.
As a result, a new offshoot of the concept of big data has emerged: the idea of ‘curated data’. This refers to having select data, and derived data and statistics, that businesses and institutions can use to function more effectively. In the investment management realm, curated data has become an essential differentiator for an institutional investor or asset manager to be able to have a sound and robust portfolio and risk management.
Why are investment managers of all kinds, whether they are institutional investors such as endowments, foundations, pensions, and family offices, or sophisticated asset managers such as hedge funds and private equity funds, without curated data?
For institutional investors, the answer lies in the fact that they have unwittingly painted themselves into a proverbial corner. Many institutional investors have allocated a disproportionate amount of their budget to fees to consultants for asset allocation and manager selection recommendations and to asset management fees, thus leaving themselves with a little remaining budget to properly perform some of their primary investment management functions, such as portfolio monitoring and risk management.
As a result, institutional investors all too often lack the data infrastructure, staffing, reference market data, and analytics necessary to produce and maintain curated investment data. Typically, we see many institutional investors manually managing large quantities of their ‘raw’ investment data in Excel spreadsheets, with virtually no ability to produce ‘transformed’ curated data that can provide actionable insights. In particular, very few institutional investors have curated data that provides them with the badly needed deep-dive performance attribution and risk management metrics and analysis they need to do proper investment management.
In some cases, institutional investors with a larger wallet have been talked into buying software that pretends to be portfolio management or portfolio analytic system, when in fact, in terms of functionality, these systems do little more than summarise the investment data that has been inputted into the software by the institution. Natively, these systems most often lack in-depth analytic capabilities and lack reference data; and, therefore, cannot produce the kinds of curated data that provides meaningful portfolio management insight and decision-making support.
Furthermore, from an operational perspective, these kinds of portfolio management analytics software require that the client either spend large amounts of their staff time doing data aggregation and data management to run and maintain the software or pay expensive add-on fees to the software vendor to perform the data aggregation and data management needed to keep the software running.
For asset managers, we typically see them fall into the three broad categories outlined below.
First, a large share of asset managers continues to resort to Excel spreadsheets to manage their investment data, hoping someday to glean some curated data nuggets out of their spreadsheet for portfolio and risk management.
Second, other asset managers attempt to repurpose portfolio-related data intended for another purpose. For example, we have commonly seen asset managers that are attempting to use data from an accounting system for investment management purposes. Not surprisingly, the asset managers employing data, in this unintended way, are not successful in using this data to monitor and manage their investment portfolios in an effective manner.
Finally, some asset managers attempt to cobble together disparate data from several service providers, such as their bank custodian, fund administrator, and prime broker, in the hope of having an aggregate view of their investment portfolios and making sense out of their investment data. This strategy for most asset managers has been a failure because they lack the critical infrastructure, such as a structured query language (SQL)-based data warehouse, experienced staff, and a wide array of analytics needed to aggregate and normalize their investment data and then enrich and model this data to finally produce derived and curated data.
What are the alternatives?
The major alternatives for having good, actionable curated data that is truly useful for investment and risk management purposes fall largely along three paths. The first alternative requires a significant capital outlay and several years to hire the right team of professionals to build and maintain the necessary data infrastructure, processes and controls, and analytics to aggregate, normalize and enhance an institution’s investment data and then produce derived and curated data to populate custom reports or a dashboard. For all but a handful of major global asset management firms and the very largest institutional investors, this path is not a viable option because of the great time and expense that such a ‘build’ would require.
The second alternative that some asset managers and institutional investors can choose is to outsource the manual work that they perform in spreadsheets to manage and store their investment data to a service provider, such as their fund administrator or custodian bank. However, choosing this path still does not produce the kinds of enhanced and curated data and metrics that will permit a robust understanding of their investment holdings, performance attribution, and risk management profile. Rather, by selecting this path, an institution is simply passing this manual work to its service provider, which will likely also do things manually in a spreadsheet or in an equivalent rudimentary internal system – and charge a large fee for doing rudimentary data management work.
The third alternative that has emerged is a new class of service provider that focuses on not only producing curated data that is actionable but also stores investment data in the cloud and provides clients with both custom reporting and dashboards as an integral part of their software as a service (SaaS) offering. In terms of cost as well as benefits, for most asset managers and investors this alternative would be the most practical and beneficial choice, assuming that this SaaS provider also has both the subject matter expertise and experience of working with a wide variety of asset managers and asset owners.
In the end, we believe the most important factors to consider as part of the ‘buy’ decision are cost, scalability, and which provider can provide the curated data that best informs investment decision making for all of the portfolio and risk management needs that funds and institutions face, both internally and externally.