Spy Satellites, Road Cameras, Phone Trackers. How Alternative Data Companies Are Changing Investing.

From left, technicians install cameras for GenLogs. A worker during the manufacturing process for an Albedo Space very-low-earth-orbit satellite. (Courtesy GenLogs (left), Albedo Space)

New companies are collecting any kind of business and consumer economic activity they can get their hands on and selling it to investors.

If you took a road trip in the past year, chances are good that you drove by one of Ryan Joyce’s cameras. They’re everywhere: hanging from highway streetlights, protruding from the walls of warehouses, and sitting on the roofs of car dealerships.

Joyce is the CEO of Genlogs, a start-up that collects data on the movements of America’s semi-trailer trucks. The company operates nearly a thousand cameras that monitor the nation’s roads, snapping photos of 18-wheelers as they drive past. Joyce says Genlogs can tell its clients where every delivery truck in the country is located on a given day.

Most of Genlogs’ customers are logistics and freight brokerage companies, which use the start-up’s data to reduce theft and optimize delivery routes. But recently, Joyce says he has been receiving lots of interest from a new set of potential clients: investors. Hedge funds want Genlogs’ data because it can tell them the volume of truck traffic in the U.S., a useful clue to the health of the American economy. Genlog cameras could even help investors bet on specific publicly traded stocks, like Walmart or trucking giant J.B. Hunt Transport Services, Joyce says.

Welcome to the world of “alternative data.” It’s a fast-growing market where companies collect information on everything from credit-card transactions to the number of travelers passing through security at airports. Investors then buy the data, hoping to glean insights beyond those provided by traditional company filings and government reports.

The use of alternative data by investors is nothing new. For decades, various funds have incorporated unusual data sources—including measures of electricity consumption and counts of the number of cars parked outside retail stores—into their research processes.

What has changed since the pandemic, insiders say, is the volume, sophistication, and variety of the data being collected and used. Thanks to recent advances in computer vision technology, language processing models, and web scraping tools, even small firms like Genlogs can harvest far larger and more complex data sets than ever before.

It isn’t just trucks. Data vendors now tell investors how many customers are clicking on a particular product on an e-commerce website. They track how many pedestrians are entering a specific bricks-and-mortar store. They monitor blockchain activity, Reddit and X feeds, and satellite imagery.

Not surprisingly, as the quality of alternative data has improved, a rapidly growing share of the investment community has come to embrace it. “The hedge fund part of our business is growing 100% year over year,” says Craig Fuller, CEO of FreightWaves, a company that tracks 85% of global cargo volume as it moves around the world.

Investment firms will spend at least $3.3 billion on alternative data sets in 2025, according to Neudata, a consultancy. Spending has grown by 21% a year since 2020, and could climb to more than $8.6 billion by 2030 (estimates go as high as $39.9 billion). A poll released in February by law firm Lowenstein Sandler found that two-thirds of institutional investors now use alt data, double the share compared with just two years ago.

Richard Lai, Bloomberg’s global head of alt data, says investors’ appetite for data has dramatically exceeded expectations: The number of firms using Bloomberg’s alt data analytics platform is more than four times the company’s estimate of the market size in 2023, he says.

Prices for alt data vary widely. More than a quarter of data sets tracked by Neudata have a sticker price of $25,000 or less, while only 3% of data sets sell for $500,000 or more. But being on that upper end pays huge dividends. The industry’s biggest players can rake in tens of millions of dollars in annual sales from a single data set, according to Neudata.

Stock market investors aren’t the only ones gobbling up data: private-equity firms use alt data to assess potential acquisitions; insurance companies use it to set premiums; big-box retailers use it to pick new store locations. But sales to investors are a growing share of the pie, according to Daryl Smith, Neudata’s head of research.

An Arms Race

Fund managers are tight-lipped when discussing how alt data informs their investment strategies. But one thing isn’t a secret: Data sources once considered unconventional are now part of the day-to-day investment process at most big asset managers. And securing new data sets, sometimes at significant cost, is one of the key ways that elite funds stay ahead of the pack.

“There has been a technology arms race within the quant world to find and engage with data,” says David Easthope, a senior analyst at Crisil, a research firm focusing on the financial industry. “Investors who are seeking superior returns are always looking for an edge.”

To make their trades, hedge funds’ quant models rely on clean, high-frequency data sets that go back for years or even decades. Until recently, the number of data sets that met quant funds’ strict criteria was relatively limited. But as data sets mature, quant investors say the number of data sets digestible by their models is growing.

Artificial intelligence is opening up new frontiers, as well. It’s allowing companies to analyze new types of data, like Genlogs’ camera footage or pdf files of government documents. It’s also helping vendors improve the quality of their existing databases.

“The growth opportunity isn’t just in the data itself,” says Carolyn James, director of strategic sales at ImportGenius. “It is the ability, with AI models that really do deep learning, to actually link data to other sources.” Thanks to AI, ImportGenius says it can provide its clients with container-level specificity of every product entering and exiting the U.S. by boat.

“AI is definitely a tailwind,” says Topher Haddad, CEO of start-up Albedo Space. Albedo plans to launch satellites into very low earth orbit, where they will capture images of the earth’s surface at a resolution previously reserved for military spy satellites and drones. As computer vision improves, he says, “You can actually pull out and extract more intelligence, in an accurate way, from any given image resolution.”

An employee at Albedo Space, whose very-low-earth-orbit satellites will take high-resolution photos of the Earth’s surface. (Courtesy Albedo Space)

AI is also making it easier for non-quantitative investors to use alternative data sources. Analysts can use AI models to analyze the text of social-media posts or the audio of investor calls, without spending millions on developing in-house models. They can also buy data directly from a growing number of AI-powered data vendors, or use information published on Bloomberg’s alt data analytics platform.

Thanks to improved accessibility, many data businesses say they’re now seeing strong interest from the “long tail” of investors: the small hedge funds, family offices, wealth advisors, and retail traders who previously lacked the resources to make use of large volumes of alternative data.

Company filings and government economic reports simply aren’t good enough to stay ahead anymore, says Jay Hatfield, who runs investment firm Infrastructure Capital Management. For one, they’re too slow: Earnings and government statistics are only published quarterly, while alt data can deliver insights in real time. For another, public data is often unreliable: Reports published by the U.S. Bureau of Labor Statistics have been getting less accurate for years, even before President Donald Trump fired the head of the bureau after a particularly weak July jobs report.

To supplement traditional information sources, Hatfield’s firm has turned to alt data. Analysts use information from Placer.ai, a start-up that harvests anonymized geolocation data from tens of millions of mobile apps, to see how many customers are walking into stores. They also use data from Similarweb , a company that tracks how many users are downloading apps and visiting specific webpages.

At Bloomberg, part of Richard Lai’s role is to identify and add new data sets to Bloomberg’s alt data platform. The task is “never-ending,” Lai says. As information becomes widely adopted by investors, its ability to deliver market-beating returns can dissipate, causing hedge funds to look for even better information. “This is something that will never stop,” he says. “Data is going to get better, it’s going to get faster, it’s going to get more accurate.”

A Better World?

Cesar Orosco, head of alpha equity investments within Vanguard’s quantitative equity group, says the prevalence of alt data is changing the art of investing. Decades ago, information was scarce, and researchers spent much of their time trying to find reliable data. Now, information is abundant—and the real edge comes from interpreting, rather than obtaining, data.

Many data providers and investment professionals argue that alt data is making financial markets more efficient. Orosco isn’t so sure. As investors wade through oceans of data, much of which sends noisy or contradictory signals, they can easily draw incorrect interpretations. Speculative bubbles and meme stocks endure, he notes. “Are the markets more or less efficient? I think the jury is still out,” he says.

Vanguard’s alpha team within its quantitative equity group. (Courtesy Vanguard)

Privacy remains a big concern. Every data provider interviewed by Barron’s stressed that the data they analyzed and sold was anonymous at every stage, and couldn’t be used to identify individuals. Data platforms such as Similarweb and Placer.ai say the information they collect from partners is already anonymized when they receive it. To protect privacy, Genlogs says its cameras instantly delete images of private vehicles.

“Trying to balance the privacy risk with the benefits is going to be a major question over the next five years or so, as AI capabilities become more robust,” says Patrick Schmid, chief insurance officer at the Insurance Information Institute, a research group for insurers. “Trying to mitigate personally identifiable information associated with the bits is going to be critical.”

Easthope says the biggest privacy concern comes from investors collecting information on executives and employees. Data about where CEOs are traveling—obtained, for example, by tracking their private jets—can provide useful investment information, while posing a risk to privacy and safety.

“There’s a voracious appetite for this data,” Easthope says. “And where the line is, I frankly don’t know. I think that line will get pushed.”

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