Getting on the AI Menu: The Case for a Direct Data Marketplace

The strategic case for monetising market data has been settled for years. What is newly within reach is the ability for the data owner to serve that demand directly, under its own brand, from its own marketplace, as agents and pipelines take on more of the discovery and wrangling, and the cost of the enabling technology falls. Global market data spend reached $49 billion in 2025, and strategies built on timely data are improving forecast accuracy by up to 25%. The demand is large and growing, and there is now a direct route to it.

Modern data users want data that is raw or cleanly normalised, machine-readable, delivered in near real time, and plugged directly into their own pipelines, cloud environments and AI engines. The connectivity specification has moved from “human downloads a file” to “agent queries a source” and the data owners who meet that specification capture the demand.

What the AI data user actually wants

Data aggregators are, and will remain, an essential channel. They give a data owner scale, global reach and integration into the terminals and workflows clients already live in. A direct marketplace is not a replacement for that reach; it is a complement to it.

What a direct channel adds is the one thing aggregation cannot: a relationship with the end client. Selling through an intermediary means the aggregator, not the data owner, holds the client account, the usage insight and the demand signal. A branded marketplace running alongside the aggregator channel lets the data owner see who is using what, hear directly what they need next, and capture a greater share of the overall revenue generation.

When the buyer is a machine, discoverability at source becomes the whole game, and the owner of the data is best placed to win it.

For an exchange or a specialist data owner, a direct channel does more than protect margin. It makes your data discoverable to AI from the source of truth, it returns a greater share of the data revenue to you, and most importantly, it creates a direct line to the customer.

Why being on the AI Menu matters

Owning the customer relationship compounds. Direct client feedback tells you which products to build next. Usage data reveals which signals the market values. And once you are the source that agents query first, you are positioned to move up the value chain, from raw feeds toward derived analytics, thematic indices and models. The direct channel is not the destination; it is the foundation that makes everything above it possible.

What a direct channel adds is the one thing aggregation cannot: a relationship with the end client.

Across our engagements with exchanges, four qualities separate a direct marketplace that grows revenue from one that merely exists.

Buy Versus Build. The Execution Question

Agreeing that you need a direct marketplace is the easy part. The harder question is how to get one built well, fast, and at a defensible cost. Four trade-offs determine the answer: speed to market, implementation risk, cost predictability, and where your internal team best spends its attention. On every one of them, building the platform in-house works against the outcome you actually want.

A data business wins on its data product, its go-to-market, and its responsiveness to clients, not on having engineered an e-commerce platform. Building diverts your best people onto plumbing; buying a proven platform lets you keep them on the product and clients.

Trade-off	Build in-house	Buy a proven platform
Speed to market	Lengthy build cycles before the first product lists	MVP in months; test-and-learn from day one
Implementation risk	Unproven, carried entirely by you	Proven platform de-risks delivery
Cost profile	Open-ended build and maintenance spend	Predictable setup and run costs, low up front
Team focus	Engineering the platform	Product, go-to-market, and client feedback

The pattern we see with data owners that partner rather than build is consistent: keep platform setup cost low up front, use an MVP to demonstrate progress to key clients, and prioritise speed to market, then iterate quickly with a regular cadence of new data product releases. Creation of a pilot client group creates early adoption and a feedback loop that feeds directly into the roadmap. Progress is visible early, which is exactly what makes go-to-market conversations easier.

Moving up the value chain

The direct channel is not the destination; it is the foundation that makes everything above it possible.

Direct channels are the foundation that lets data owners engage the market on their own terms, particularly as AI agents take on more of the heavy lifting around data feeds and wrangling. Once the channel is live and the feedback loop is running, client demand signals point the way to higher-value products: derived analytics, indices, market impact models. The marketplace you launch to distribute raw data becomes the platform on which you build everything that follows.

How DataHex Data Shop fits

DataHex Data Shop is a proven, white-label, AI-native marketplace, highly configurable and built for capital markets. It’s built for exactly the trade-offs above: speed to market, a proven platform that mitigates risk, rapid product innovation, and low, predictable implementation and run costs — so your team stays focused on the data product, not the plumbing.