By: Natalie Johnson
The alternative data market is currently valued at almost $ 12 billion, with forecasted annual growth over the next 7 years. It reflects a growing effort by hedge funds, asset managers, and early-stage companies to find signals outside traditional earnings reports and sell-side research.
Alternative data often comes from places most people would recognize but overlook: public agencies, regulatory portals, retail sites, app stores, and online platforms where everyday activity leaves a digital trace. While much of this information sits in plain sight, its financial value is often missed because it is messy, fragmented, and arrives faster than most teams can process. The firms that commit to organizing this chaos can turn apparent noise into a timing advantage. Those who interpret public signals early often move before competitors have even registered the shift, converting information that looks ordinary into strategic insight that moves capital first.
“The edge is not secrecy. It is speed, structure, and linkage,” says David Martin Riveros, CEO at Iceberg Data, a user-friendly web-data tools company. For Martin, the real story is not market size, but the strategic potential unlocked when messy, unstructured information is converted into clarity at a speed that matters.
Despite its availability, public information isn’t usually processed with the urgency required to affect asset pricing. Many assume that if something is accessible to everyone, its value must already be reflected in the market, but there is a bottleneck between raw access and structured understanding. “Even though the information is out there, it is not processed with enough speed and structure so that it is fully priced,” he says. Parsing government portals, permits, inspection outcomes, and regulatory announcements demands the ability to resolve entities, normalize formats, and extract meaning before others move.
This dynamic explains why funds that treat public data as infrastructure, rather than background noise, often outperform. “What you do with public data and how you process it determines if you can extract alpha.” And access alone does not generate alpha; transformation does, as artificial intelligence reduces the friction of that transformation, the value of public data rises, along with the stakes of ignoring it.
“The scrape is easy, but generating trust on the collected data is hard,” he says, drawing a line between gathering information and turning it into something people can act on. The challenge goes beyond technology and into culture. Hedge funds are accustomed to audited financial statements and predictable reporting cycles, yet organic public data behaves differently. It arrives continuously, coverage shifts without notice, and formats rarely align with traditional governance structures, creating uncertainty about how much weight to give the signals it contains.
Martin describes a persistent hesitation to let probabilistic thinking guide decision-making. Data may live in “the quants’ notebook” rather than a central system, and accountability becomes distributed. Without clear ownership, trust lags. The funds that overcome this hesitation tend to share a repeatable approach. They form hypotheses, define signals, validate findings, monitor drift, and establish data governance guardrails such as quality checks, provenance, and error budgets. By shifting from deterministic reporting to probabilistic modeling, they unlock the ability to act faster and with conviction.
Few scenarios illustrate the value of timing more clearly than government authority publications. Regulatory notices, permits, enforcement actions, and procurement outcomes all influence companies long before those movements appear in earnings reports. Martin recounts the implications of a construction permit granted in a significant city. Detecting the update within minutes, mapping it to the right developer, and translating the impact into an actionable thesis becomes a competitive advantage. “Knowing sooner is not about predicting the future with certainty,” he says. “It is about updating probability models before the broader market forms a narrative.”
Teams that move slowly may interrupt repricing, lose optionality, or become hostage to public sentiment once a story has already formed. In that gap between event and narrative, unstructured public data becomes strategic leverage. “Speed shapes outcome.”
The same principle applies to retail and e-commerce. Take monitoring customer reviews, supplier counts, and product listings to estimate quarterly revenue before official disclosures. If ten reviews appear on a product and analysts assume fifty purchases go unreviewed, sales estimates begin to take shape. Apply this daily, across multiple products, and a picture of performance emerges long before earnings calls. “At the end of scraping this mini Amazon, you would be ahead of other analysts,” he says, noting that such forecasting informs decisions to go long, go short, or rebalance positions ahead of expectations. While simple in design, the example communicates something fundamental. Alternative data does not replace traditional reporting, but reshapes when and how insight forms.
Martin sees a future where usage shifts from sporadic experiments to repeatable execution, and where processing pipelines become as trusted as financial disclosures. He expects a continued race between awareness of public signals and the ability to act on them systematically. For funds willing to embrace probabilistic thinking, the reward is not secrecy but speed. As alternative data continues its rapid expansion, the firms that treat public data as a latent advantage, rather than background noise, will find opportunities others overlook. The return on investment lies not only in what is known, but in how quickly knowledge is converted into action.
Follow David Martin Riveros on LinkedIn for more insights.
Disclaimer: The information presented in this article is for informational purposes only and does not constitute financial, investment, or trading advice. Any potential outcomes or strategies mentioned are based on general observations and should not be interpreted as guarantees of financial returns or specific results. Investments carry risks, and individuals should consult with a qualified financial advisor before making any investment decisions.
