For wealth managers and RIAs, the rise of alternative data represents one of the most significant shifts in how markets are analyzed and investment decisions are made.
What began as a niche tool used by hedge funds has rapidly expanded into a mainstream resource, with new companies aggressively collecting economic activity from consumers and businesses, then packaging and selling it to investors.
If you’ve taken a road trip recently, you’ve likely driven past one of Ryan Joyce’s cameras. His company installs them on highway streetlights, warehouse walls, and the roofs of car dealerships. They silently track traffic flows, consumer behavior, and business activity—all without the individuals driving by realizing their movement is being logged as part of a vast economic dataset.
This is just one example of how alternative data is reshaping market intelligence. For wealth advisors and institutional investors, the question is no longer whether this data matters, but how to effectively harness it for insights without losing sight of compliance, cost, and ethical considerations.
Breaking Down Consumer Data
The alternative data industry captures and categorizes information into four major types, each with unique investment applications:
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Personal Data. This includes identifiers like Social Security numbers and gender, but more commonly focuses on non-personally identifiable information such as IP addresses, browser cookies, and device IDs. While advisors don’t trade directly on such granular details, aggregations of this information can inform macro-level trends on consumer connectivity, digital activity, and purchasing power.
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Engagement Data. Engagement measures how consumers interact with businesses across digital and physical channels—websites, mobile apps, emails, customer service platforms, and social media. For investors, engagement data provides leading indicators of brand traction and market share shifts before they show up in earnings reports.
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Behavioral Data. This is the heart of alternative data’s appeal. Transaction histories, usage frequency, and product-level activity provide critical signals. For instance, an uptick in repeat purchases or service adoption rates can foreshadow revenue growth. Behavioral data has become a favored tool for funds seeking an informational edge.
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Attitudinal Data. While harder to quantify, attitudinal inputs such as consumer satisfaction, purchase motivations, and brand loyalty are increasingly tracked. Surveys, sentiment analysis from social platforms, and review aggregations give investors insight into demand durability, allowing them to anticipate whether revenue momentum is sustainable.
From Raw Data to Investment Signals
The value of these datasets lies in their ability to generate cash flow for both the companies that collect them and the investors who interpret them. A growing network of data brokers specializes in aggregating information from disparate sources, cleaning it, and reselling it to institutional clients. This industry now rivals traditional financial data providers in both scope and profitability.
Advertisers represent a primary customer base, willing to pay significant premiums for granular profiles of consumer behavior. Yet investors are increasingly the end users. Hedge funds, quant shops, and even long-only asset managers rely on these datasets to monitor real-time business activity, test hypotheses, and generate trading signals well ahead of quarterly financial statements.
For example, satellite imagery tracking cars in retail parking lots can provide a near-instant view of consumer traffic compared to reported sales data weeks later. Similarly, road camera feeds tracking trucking and freight movement can offer forward-looking indicators of supply chain health or industrial production. For wealth advisors managing client assets, these tools can sharpen macro analysis, sector positioning, and fund manager due diligence.
Implications for Wealth Advisors and RIAs
For fiduciaries, alternative data presents both an opportunity and a challenge. On the opportunity side, access to unique datasets can help validate manager claims, improve risk management, and inform portfolio construction with real-time signals that traditional data sources cannot provide. Being conversant in this emerging field enhances advisor credibility with sophisticated clients, particularly those allocating to hedge funds, private equity, or other data-driven strategies.
The challenge lies in navigating cost, compliance, and ethics. Data subscriptions are expensive, and the return on investment is not always clear for smaller advisory practices. Compliance concerns loom large: regulators are increasingly focused on privacy and consent, particularly as personally identifiable data enters the mix. Advisors must be vigilant about ensuring that any third-party research providers adhere to best practices in data collection and usage.
There are also reputational considerations. Clients may question the ethics of investing strategies that lean heavily on tracking consumer movements, phone data, or web activity. Advisors must be prepared to explain how alternative data is sourced, anonymized, and applied in ways that support fiduciary responsibility.
The Future of Investment Intelligence
The trajectory is clear: the demand for alternative data is growing. The more diverse the data sources, the more comprehensive the profiles that brokers can build, and the higher the value to both advertisers and investors. As adoption spreads, the competitive edge will depend less on mere access and more on interpretation—the ability to transform raw signals into actionable insights.
For wealth advisors, understanding the mechanics of alternative data is no longer optional. While you may not install satellite imagery feeds or purchase truck traffic datasets yourself, your clients increasingly expect you to know how institutional managers are using this information to generate returns. The role of the advisor, then, is to contextualize these tools, evaluate their merit, and ensure they align with client objectives and values.
In an era when cameras, satellites, and trackers silently record the rhythm of economic activity, the edge goes to those who can see the patterns first—and explain them best.
