The Data Economy That Didn't Explode
Data was supposed to be the new oil. If we increase availability and access, more companies should buy and sell it. The market should explode. But to date, it hasn't.
Auren Hoffman, who built SafeGraph on this premise, put it simply on his podcast: "I would have thought by now it would have grown an order of magnitude," he said. "And it's grown very small, very slight."
The numbers back him up. The alternative data market grew about 33% to ~$2.5B in 2024—solid, but not exponential. Per Auren, in hedge funds the number of serious data buyers has actually fallen versus five years ago.
At the infrastructure layer, there has been progress: Snowflake's data marketplace added 98 new listings in Q4 2024, up 26% year-over-year. AWS Data Exchange now offers 3500+ datasets from 250+ providers. The pipes are there. But transaction volume remains modest.
Meanwhile, 61% of U.S. companies launched a product based on external data in the past year. So companies want external data. They're just not buying it at scale.
So what's going on?
A Quick History Lesson
As recently as the 1950s, if you wanted to ship goods across an ocean, you faced an expensive, slow, and chaotic process. Ships would arrive in port and sit there for days or weeks while longshoremen hauled crates, barrels, sacks, and boxes of every conceivable shape and size to and from shore. This was called "break-bulk" shipping, and it was essentially how humanity moved goods by sea for centuries. Labor costs alone meant that for many products, it didn't make economic sense to trade across oceans at all.
In 1956, a trucking entrepreneur named Malcolm McLean had a simple idea: what if we standardized the container? By the 1970s, with ISO specifications in place, the same crane in Rotterdam could lift the same shipping container that a truck in Los Angeles could carry, that a train in Kansas could haul. Shipping costs fell by roughly 90%. Port turnaround dropped from days to hours.
The box didn't make goods valuable. It made moving goods cheap and reliable. And that's when global trade exploded.
Data Is Still Break-Bulk
Here's what I think is happening: We're still shipping data the same manual, labor-intensive break-bulk way we shipped goods in the 1950s. Despite progress on making data more easily available, the core interoperability problem remains: every dataset arrives in a different format, different schema, different quality standards. Each one requires bespoke integration work. Each one needs its own legal review and security audit.
There's no standard "container" for data.
Every data deal faces the same gauntlet. Months of legal review. Security audits. Schema mapping. Identity resolution. Engineering to actually integrate the thing. Ongoing maintenance and SLA management. And at the end of it all, a fuzzy ROI that makes procurement nervous.
The stats bear this out. When asked about obstacles to using external data, companies cite compliance barriers and data quality issues at nearly the same rate as budget constraints. 16% struggle just to make the business case. The market isn't limited by value. It's limited by friction.
The Interoperable Shipping Container Era for Data
So what would change this?
At Narrative, we believe the future is automated data normalization. Imagine a "Rosetta Stone" for data interoperability: Software that understands every data dialect and translates it into (and out of) a common language. Standards that make data interoperable by default. Privacy-preserving tech that makes legal review routine instead of bespoke. Quality metrics that are transparent and trustworthy.
This is what Narrative's Rosetta Stone Normalization Engine is designed to do: create a shared data language that works everywhere. Map schemas automatically. Normalize data on the fly. Make datasets from any source instantly compatible with any destination.
Think about what data interoperability unlocks: faster time-to-value, repeatable activation patterns, lower risk. When you eliminate the bespoke integration tax on every dataset, suddenly buying three datasets costs only marginally more than buying one. When you can evaluate data quality in minutes instead of months, you're willing to experiment.
That's the "shipping container" moment – when the cost of moving data, combining data, and trusting data across organizations becomes cheap, predictable and reliable.