The Allure and Reality of Alternative Data

The investment community’s fascination with alternative data is palpable. Alternative data providers promise investors the potential to uncover hidden patterns and gain a competitive edge, branding these datasets as the next great innovation in the financial industry. As enticing as this promise may seem, its practical application presents challenges that demand a closer look.

Investors see pouring resources into alternative data as a way to gain an edge over competitors, though in practice, this strategy often feels somewhat speculative, almost like making blind bets and hoping for a payoff. Some of the largest funds are already spending upwards of $5 million on it annually, and industry sentiment leans more toward enthusiasm than caution.

The Rise of Alternative Data

The alternative data landscape has been experiencing growth, with over 400 active providers currently available, a substantial increase from just 20 in 1990. Despite the expansion, investment managers typically utilize a select subset of these datasets, engaging with approximately 20 different datasets on average.

Just like with traditional data, integrating new data streams is no simple task. Simply having data—of any kind—doesn’t inherently translate to an investment advantage without the right set of tools and expertise.

While alternative data holds promise, the industry is yet to mature, which creates hurdles that smaller fund managers, in particular, struggle to overcome.

The absence of uniform standards leads to inconsistencies, making data integration and comparison difficult. Many alternative data sources also harbor inaccuracies and biases that can distort investment decisions. Finally, incorporating alternative data into existing models requires sophisticated infrastructure and expertise, posing a non-negligible risk of misinterpretation.

The Role of Technological Infrastructure

Effectively leveraging alternative data demands robust technological capabilities and either human or computational power (ideally both). Firms with advanced systems can process and analyze large datasets more efficiently, but this underscores a crucial point: the mere possession of alternative data does not guarantee anything. The ability to harness it effectively is an entirely different story.

New technologies and flashy datasets may seem like the next big thing, but traditional analysis remains the most reliable and direct means of improving investment performance. Investors already have access to a wealth of data within their own portfolios—returns, drawdowns, volatility, sector exposures, attribution metrics—yet so many fail to extract the full value from these internal datasets.

Understanding how a strategy performs across different market environments, where risk is concentrated, and what truly drives returns is often more valuable than layering on external, often inconsistent, alternative datasets. For those without the resources to fully utilize traditional techniques, adding untested, lesser-explored approaches can do more harm than good.

Beyond that, portfolio analysis is infinitely more cost-effective than pricey alternative datasets. A deep dive into a portfolio can highlight insights based on standardized, auditable sources. This process instills a level of confidence that alternative data, with its varying methodologies and biases, simply cannot provide.

Back to Basics

Alternative data seems to be earning its place in modern investing, but its usefulness still depends on the infrastructure and expertise used to process it. Often, it adds complexity to an already intricate decision-making process. Couple this with emerging trends like artificial intelligence, trading signals, or whatever the newest buzz is about, and you may end up diluting an investment process that may not need fixing in the first place.

The edge in investing isn’t found in accumulating more data, but in asking better questions of the data that’s already there. Ultimately, the most powerful signals come from within one’s own analytical frameworks and understanding.