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They can help, but they are not the key by themselves. Here, “data rooms” means data clean rooms: controlled environments where organisations can analyse data together without necessarily exchanging raw datasets. They are most useful when partners have complementary data and a defined commercial goal; revenue still depends on data rights, a useful result and someone willing to pay for it. This is distinct from a virtual deal room used in mergers and acquisitions.
What a data clean room does—and does not do
A clean room provides a governed environment for parties to run agreed analyses against their combined data. Depending on the platform and configuration, participants can learn from overlap, segments or aggregated results without seeing one another’s underlying records. AWS describes collaboration on models and audiences without revealing underlying datasets; Snowflake describes role-based collaborations with controlled resources. AWS Clean Rooms FAQs and Snowflake’s overview describe these workflows.
That makes a clean room enabling infrastructure, not a business model. It does not create permission to use data, a compelling insight, a buyer, or a commercial agreement. Platform documentation shows what workflows can be built; it does not establish typical revenue, margins or return on investment.
How clean rooms can support monetization
The commercial value may be direct—payment for collaboration, an analytical output or a measurement service—or indirect, by improving an existing product or operation. These are practical categories for understanding the documented use cases, not measured market-wide outcomes.
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Campaign measurement and activation
A publisher may contribute exposure data, an advertiser purchase data and an identity partner a dataset that helps connect the two. Snowflake documents a three-party advertising measurement workflow involving audience overlap, segmentation and possible activation. Such a workflow could support paid measurement or stronger ad sales, but the documentation does not report a publisher’s revenue gain. See Snowflake’s activation connector documentation and multi-party insights.
Retailer and brand collaboration
A retailer and a brand may analyse audience or campaign performance together while limiting access to customer-level data. AWS describes advertiser-publisher collaboration for model and audience use cases without sharing the underlying datasets. Its retail and commerce-media architecture places Clean Rooms alongside first-party data, identity resolution, audience building, advertising platforms and campaign analysis. That shows how a clean room can fit into a larger commerce-media operation, not that it guarantees incremental sales or revenue. AWS Guidance for Retail and Commerce Media Monetization.
Aggregate market insight
The UK Information Commissioner’s Office (ICO) describes a retailer comparing anonymised market-view insights with its loyalty segments. The resulting group-level spending-headroom insights can inform marketing. The example, developed with Truata, illustrates a potentially useful analytical output; it does not quantify a sale or uplift attributable to the clean room. ICO anonymisation guidance and its trusted-third-party market-insights case study.
When the business case is strong—or weak
A clean room is worth exploring when partners can state both what they want to learn and what they will do with the result. A clear use case helps determine which data is needed, which analysis is appropriate and whether the result has commercial value.
- Stronger case: the parties have complementary data, a shared objective such as campaign measurement or audience planning, documented rights to use the data, and a buyer or operational team prepared to act on the output.
- Weaker case: the organisation has no defined use, lacks permission for the intended use or disclosure, cannot produce a useful result, or has no buyer or internal use for the insight.
These are business-condition tests, not claims about the success rate of clean-room projects. The cited platform and regulator materials do not quantify how often projects generate revenue.
Privacy and governance are design work, not a label
A clean-room product does not automatically make a data use lawful or prevent disclosure. The Federal Trade Commission (FTC) wrote in November 2024: “DCRs don’t automatically prevent impermissible disclosure or use of consumer data; and unlawful disclosure or use of data is unlawful regardless of whether a DCR is involved.” The FTC says well-designed and monitored restrictions on queries and exports can reduce risks, but protections are not typically automatic. It also warns that clean rooms can introduce additional access points and that misconfiguration can create risk. FTC: Data Clean Rooms: Separating Fact from Fiction.
Hashing or pseudonymising identifiers, or returning aggregate results, is not proof that people cannot be identified. The ICO’s case study considers direct and indirect identifiers and whether datasets can be linked. Its example uses separate datasets, a trusted intermediary, technical and organisational safeguards, and aggregate group-level outputs. The ICO’s anonymisation guidance, published on 28 March 2025, says effective anonymisation depends on the techniques used and on reducing identification risk to a sufficiently remote level. The guidance is under review following the UK Data (Use and Access) Act; check its status for the relevant UK use, and seek legal advice for a specific situation.
Snowflake says customers are responsible for obtaining necessary consents for their use of its clean rooms, including third-party activation connectors, and for complying with applicable laws. Before a collaboration begins, the parties should resolve the following for their jurisdiction and use case:
- Whether each party has the rights and consent needed for the proposed purpose, sharing arrangement and activation.
- Which data fields are necessary, and whether direct or indirect identifiers or linkability create identification risks.
- Who can access the environment, which queries are allowed, and what outputs can be exported or activated.
- How query and export controls will be monitored, and who responds to misconfiguration or a security incident.
- What contracts, purpose limitations and retention rules govern the collaboration and its results.
How to assess whether a clean room fits
Compare a proposed clean-room approach against the actual collaboration—not against a generic promise of privacy or monetization. These questions synthesize regulator guidance and platform documentation; they are not a universal vendor scorecard.
Quick Recap
- Define the shared objective. Name the decision, service or commercial result the parties want, such as measuring a campaign or building an audience.
- Document data rights and necessity. Identify the data each party would contribute, why it is needed, and whether the intended use and disclosure are permitted.
- Specify queries and outputs. Establish what each participant can analyse, what results can leave the room and whether any result could expose an individual or sensitive information.
- Assess identifiability and linkability. Consider direct and indirect identifiers, other datasets that might be combined with the output, and the controls needed to reduce risk.
- Check technical fit and operating requirements. Confirm partner workflows, cloud regions, deployment and activation destinations. For Snowflake’s documented policy-enforced sharing, data providers need Enterprise Edition; activating results to another Snowflake account also requires Enterprise Edition. Availability varies by region and deployment. Consult Snowflake’s current overview for applicable conditions.
- Assign cost and accountability. Decide who pays for implementation, analysis and ongoing governance, and who is accountable for access, monitoring and incident handling.
- Set a success measure before launch. Choose a commercial or operational result that can be assessed, rather than treating use of the platform itself as proof of value.
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