The Financial System for Data: Why Dataspaces Work Like SWIFT and Credit Card Networks (Not Stock Exchanges)

No serious investor confuses the stock exchange with the stock. Yet in the data economy, that category error is everywhere.

Mention “Dataspaces” in a boardroom and many still picture a grand repository, a lake, warehouse, or platform where data is poured and monetised of worse still a glorified database. Europe’s policymakers have been nudging the world away from that misunderstanding: common European Dataspaces are meant to make data available for access and reuse “in a trustworthy and secure environment.”

The trouble is that “trusted environment” sounds like a security feature. It is not.

Think more about a market design ambition. A way to make the exchange of data routine (and, crucially, the rights to use data not take ownership) enough to be scalable, investable and repeatable. To explain that, the stock-exchange analogy is a tempting opening, but it is not the best mental model.

A clearer pair of metaphors is hiding in the plumbing of modern finance:

• SWIFT explains the architecture: shared standards, governed rails, and trusted messaging that connect independent institutions, without SWIFT itself holding the asset. SWIFT describes itself as a “global member-owned cooperative” and a leading provider of secure financial messaging.
• Card networks ( Visa / Mastercard ) explain the economics: a multi-sided rulebook ecosystem where operators and service providers earn fees for enforcing trust and reducing friction. Visa states plainly: “Visa is not a financial institution. We do not issue cards, extend credit…”, banks do.

Put those together, and Dataspaces begin to look less like an IT project, and more like market infrastructure with a fee stack.

Markets are machines for trust (and information)

Finance’s most durable innovation is not the ticker; it is the trading rulebook.

Markets exist because trust is expensive and bilateral negotiation does not scale.

Dataspaces are trying to perform a similar trick: compress messy, bespoke negotiations about data sharing into repeatable patterns, identity, permissions, obligations, auditability and governance, so that organisations can exchange data (or insights derived from it) without losing control.

That is not a data storage concept (like a database); it is a data access and use rules coordination concept.

SWIFT: Why Dataspaces are similar, and where they differ

Let’s start with what SWIFT is not. SWIFT does not hold funds or manage accounts. It enables its global community to communicate securely by exchanging standardised financial messages. This distinction, messaging rails vs asset custody, is exactly the distinction many audiences need in order to stop treating a Dataspace as just a fancy big platform / database where the data lives.

The Open Data Institute captures the ecosystem nature neatly: “A Dataspace is an ecosystem of organisations working to share data towards a common cause based on an agreed set of rules to govern data sharing.” And Europe’s policy ambition makes the governance point explicit: the Data Governance Act includes measures to ensure data intermediaries function as “trustworthy organisers of data sharing or pooling” within common European Dataspaces.

So, like SWIFT, a Dataspace is best understood as:

• Standards and shared semantics (so participants can interpret “messages” the same way)
• Governance and participation rules (so trust is not purely bilateral)
• Interoperability rails that connect independent actors

Where the SWIFT analogy falls short: SWIFT explains the “how” (trusted, standardised connectivity) better than the “who gets paid and why”. Many executives can nod along to “secure messaging”, and still not see a business model beyond “sell the data”.

For that, payments networks offer a sharper lens. 

Credit Card networks: Why Dataspaces may monetise like Visa and Mastercard

If you want to understand how ecosystems monetise trust at scale, study card networks, not banks.

Visa is explicit in its annual report: it provides “authorization, clearing and settlement services” for Visa-branded card transactions on its network and may earn “service, data processing, international transaction or other revenue.” It also stresses what it does not do: it is not a financial institution and does not issue cards or extend credit.

Mastercard frames itself similarly as a network operator with a layered commercial model. It describes disaggregating net revenue into two categories: payment network and value-added services and solutions. It also describes value-added services as being monetised via fixed or transaction-based fees. And on the operational side, Mastercard states plainly: “Mastercard Switching Services encompass three core activities: authorization, clearing and settlement.”

Those statements matter because they reveal a pattern that maps remarkably well to Dataspaces:

1. A governed network with entry requirements and rules: A rulebook.
2. A scalable “authorisation” layer: Who is allowed to do what.
3. Operational rails: The transaction happens reliably.
4. “Settlement” as accounting and assurance: Fees, compliance, dispute resolution, evidence.
5. A large value-added layer: Risk, identity, analytics, compliance tooling.

For completeness, Visa even publishes public versions of its rules that “govern the participation” of financial institution clients in the Visa system.

Where the Credit Card analogy falls short: In a Dataspace, the “asset” is not a payment; it is a dataset, a data product, an algorithm, or, most often, permissioned use of data for a pre agreed and defined purpose. Yet the economic logic can be similar: participants pay to reduce friction and risk, not merely to obtain bytes.

Why the stock-exchange metaphor is useful, but ultimately weaker

The stock exchange analogy is often the first go to for most people trying to understand Dataspaces, mostly because it is familiar: a governed venue, rules, access, and intermediaries. But it breaks down where data is economically different to shares.

1. Shares are designed to be standardised and fungible. Data products are not. Even when two datasets share a schema, their provenance, completeness, bias, timeliness and legal constraints can diverge dramatically.
2. Securities are rival in the everyday sense: selling typically transfers ownership. Data is most often nonrival. This is not a trivial point as it reshapes pricing strategies, market structure, and return incentives (hoard vs share).
3. An exchange is designed to enable continuous price discovery. Many Dataspace interactions are closer to contracting and scale better with fixed or at least predictable pricing structures.

So, while it is tempting to use a stock exchange an an analogy it is the least applicable of the 3 in this article.

Who makes money in a Dataspace ecosystem

Here is a practical way to explain value capture, without pretending a Dataspace is a single “platform business”:

The Dataspace operator / Governance Authority

• Analogy: SWIFT cooperative governance + card scheme operator
• Monetisation logic: membership and onboarding, certification, cataloguing, compliance services, dispute processes, basically the “rulebook and trust” business as foreseen in IDSA’s framing.

Data Intermediary, Brokers & Facilitators

• Analogy: Acquirers/processors/PSPs in payments
• Monetisation logic: Contracting, integration, managed compliance, packaging, and “last-mile” interoperability. The Data Governance Act explicitly anticipates data intermediaries as trusted organisers of data sharing.

Trust Service Providers

• Analogy: Fraud/risk/identity layers in payments, possibly sold as a value-added service.
• Monetisation logic: Recurring assurance fees and compliance tooling, commercialising what regulators and boards increasingly demand: evidence.  Look at Mastercard’s example of how large value-added services and solutions can be in a network business model.

Connectors / Interoperability technology vendors

• Analogy: Network technology and processing infrastructure
• Monetisation logic: Software subscriptions, managed services, implementation and integration, essentially selling the “rails”. Here SWIFT is a good example of secure, standardised connectivity.

Data Providers

• Analogy: Merchants or issuers, depending on the flow of value
• Monetisation logic: Direct monetisation (typically subscription/usage fees/revenue share) and indirect value though insights use for better forecasting, and operational optimisation. The key is keeping control while sharing.

Data Consumers

• Analogy: Consumers and merchants
• Monetisation logic: Anyone that is willing to pay for reduced friction, reduced legal uncertainty, and faster time to value, in other words, for market-quality.

Again for the umpteenth time: The Dataspace model is less “sell data” and more “sell trust at scale”, precisely what card networks’ disclosures illustrate.

Actionable Steps for company executives and investors

Analogies are only useful if they change decisions. Here are six practical steps, equally relevant to corporate strategy and investment diligence:

1. Separate “data custody” from “data exchange”.
2. Treat the rulebook as a product, not paperwork.
3. Design “authorization” and “audit” first.
4. Monetise trust services, not just access.
5. Assume nonrivalry will distort incentives, and plan for it.

The Closing Thought

Here is a one line way to think about Dataspaces that will survive both the CFO’s scepticism and the CIO’s critical eye-roll.

Dataspace are SWIFT-like in architecture and Visa/Mastercard-like in economics.

A governed interoperability and trust fabric that standardises permissioned data exchange, while an ecosystem earns fees by operating rules, certification, transaction facilitation, and value-added assurance services.

• SWIFT for the rails,
• Visa/Mastercard for the fee stack, and
• A rulebook for the logic that holds it together. 

Note: The author was not paid by nor, at the time of writing, a shareholder in or had any economic relationship, incentive or reward agreement with any of the companies mentioned in this article.

About the author

Brendon Grunewald advises boards and investment committees at strategic inflection points where technology changes the business model faster than the investment thesis can adapt.

He is known for rapidly distilling complex situations into decision-ready insights on capital allocation, risk, valuation, and governance.

Brendon also circulates a private monthly briefing for a small group of board and IC participants on these themes.

If you would like more detail on these points or receive our checklists see Praefidi and request access to the briefing.