Alternative data has been labeled as a cure-all for the ongoing aches faced by asset managers, but in reality, these sets often do not generate numbers for managers to derive meaningful insights from. This typically comes down to the fact that far too many funds are zeroed in on the pure hype of the data and not its practical use case, which are most typically forged by domain expertise practices.
However, the rate at which firms are implementing alternative data sets into their investment process seems to have reached new highs as of recently. Global consulting firm Deloitte has noticed a unique uptrend of private equity and large investment management firms joining their riskier hedge fund counterparts in the use of these non-traditional data sets to generate new sources of alpha. The consulting firm notes that this adoption is at its tipping point with alternative data’s use growing at a rapid pace.1
As the industry continues to discuss the new hype of alternative data, it’s important to bring the conversation back to alternative data’s foundation. The most classic example of Alt Data 1.0 is the ADP’s National Employment Report payroll data. There was once a time that managers identified and harnessed ADP’s data to drive investment insights, but now it is ubiquitous across the industry and managers have moved on. This same struggle is playing out in today’s broader alternative data offerings and analysis capabilities. Managers are faced with an industry-wide democratization of both tools and capabilities, further leveling the competitive playing field.
Deloitte has noticed a unique uptrend of private equity and large investment management firms joining their riskier hedge fund counterparts in the use of non-traditional data sets to generate new sources of alpha.
Gurvinder Singh, CEO and Founder, Indus Valley Partners
The desire to drive AUM has become increasingly prevalent in this environment of rising cost pressures, and as a result, managers are increasing their allocations into the expansion of their data set capabilities in order to maintain and attract new investors. According to a recent study from Greenwich Associates, firms’ budgets for alternative data experienced a 52% year-on-year growth in 2018, further signifying the value the industry is seeing for this type of data.2 Not only are some of the largest asset managers diving into this space, but industry giants like Nasdaq have recently jumped into the deep end as the firm acquired alternative data provider Quandl in 2018 in order to “partner more closely with the investing community as the industry continuously seeks ways to evaluate an endless supply of information to drive new insights, investment ideas and deliver alpha.”3
Funds are realizing firsthand that access and tools alone won’t drive success. Data integrity and a “data-first” mindset is essential to a fund’s ability to effectively leverage the tools and data sets available to the market. Before a fund can even try to harness alternative data to differentiate themselves, they first need to ensure they have a comprehensive data strategy in place to properly scale enterprise intelligence and analytics.
Many funds we work with, big and small, are finding that implementing an impactful data strategy is no small feat. Funds are beginning to realize they cannot achieve success with these new tools without first ensuring their core data sets are accurate. Some key steps for success are in a firm’s ability to first collect and secure core thought processes on data capture, governance and curation for data sets that need to be governed and curated (trades, securities, positions, PnL, counterparty, risk, performance). As funds increasingly use base data sets for additional analytics, many are leading to definitions of a data layer that continuously expands and evolves without tech involvement. Regardless if implemented as a scalable data warehouse or data lake, clearing and segregating governed and curated data sets, as well as semi and non-governed data sets, is vital. As a fund prepares for the enormous influx of alternative data sets produced by machine learning and artificial intelligence technologies, it is crucial that a process is set in place to seamlessly classify the data into a common set of attributes to be used to drive insights across the board. With this classification process, powered in part by a flexible data layer, managers can more readily ingest new data sets, derive analytics and report in more insightful ways.
The seamless shift to harnessing and implementing alternative data effectively does not come overnight. A sometimes overlooked point in the path to success with alternative data is getting buy-in at a business level for treating data strategically. The most successful data initiatives have full strategic buy-in, which in turn enables transparency in governance, data lineage and cataloging, making implementation and change management much easier. Although the hype that surrounds this data may lead many to believe it will “change the game” in delivering an investing edge, those that take the more thoughtful route with a well laid out strategy will be the true winners in the end as they uncover this non-traditional data’s true potential.
1. InFocus: Alternative data adoption, Deloitte, 2018.
2. Rob Daly, “Alt Data Investments Continue Their Rapid Rise,” MarketsMedia, June 10, 2019.
3. Bjorn Sibbern, “Nasdaq Acquires Quandl to Advance the Use of Alternative Data,” Nasdaq, December 4, 2018.