4 min read

Some Alternative Data may seem commoditized but Alpha Generation is not.

Mark Trowbridge

Mark Trowbridge

VP, Sales

Hedge Funds pioneered the investment industry in Alternative Data adoption. This led to significant alpha extraction from a surprising number of data sources. Today, some versions of this data have become almost ubiquitous among sophisticated investors. Talk of “commoditization” has also become prevalent at conferences and roundtables. At the same time, many smaller managers have become disenchanted with this data due to its complexity, challenges with monetization, and the apparent dominance of larger competitors. “Commoditization” is sometimes used as a reason to not pursue an Alternative Data strategy at all.


Managers do this at their peril. While some vendors do publish widely read research that moves prices and shapes buy-side expectations, the data itself can hardly be called commoditized and the processes of monetizing it, are even less so.


The word commoditized implies a generic, undifferentiated, and widely available product. Most traditional market data fits the description – there are multiple sources that track Apple’s revenue and they will all (mostly) agree on the numbers. But any data scientist understands that Alternative Data is anything but generic and undifferentiated, even though it has become increasingly accessible. Two analysts looking at the same data may or may not agree on the signal.


Even when looking at the relatively ubiquitous transaction data, there are a dizzying number of ways to analyze and interpret it. From creating panels to controlling and correcting for biases, to carving out cohorts and demographics, the same underlying data can yield opposing signals for two separate investors analyzing the same company KPI.


While there is no doubt that the industry is maturing and data consumption is much higher today than even five years ago, there are still countless ways that managers can apply alt data toward generating alpha. This is especially the case with less popular data sets and the technology investors use to combine and analyze the data. Even the timing of when an investor will access and act on the data matters a great deal. Wall Street’s rush to hire armies of data scientists makes it clear that the alpha isn’t just in the data itself, but rather, in the way you monetize. That process of extracting consistent alpha is both art and science and not remotely close to being commoditized.


Some may think that generating alpha consistently and at scale is only possible for the most sophisticated funds. There is no doubt that multi-million-dollar data budgets and large data science teams provide a competitive advantage. But what was once only available to the select handful of funds with massive resources and teams is becoming democratized thanks to advances in technology and data processing.


What are your thoughts on the level of commoditization in Alternative Data?


We’d love to hear from you.




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