Get Your Hands Dirty in Data Clean Rooms

The adtech industry doesn’t stay stuck for long. When one door closes, someone’s already kicking in a window.

Data clean rooms are one of those windows. They’re secure, encrypted software environments where companies can share and analyze anonymized, aggregated customer data without handing over raw records or personally identifiable information (PII). Think of them as a neutral meeting ground where two parties’ data can shake hands without either side seeing the other’s cards.

Data clean rooms may be the most practical path toward privacy-friendly measurement at scale — not a silver bullet, but a genuinely useful piece of the puzzle. This post covers what data clean rooms are, why they’ve become hard to ignore, and why your data standards strategy will make or break how much value you actually get from them.

What is a data clean room?

Data clean rooms are encrypted, secure environments where companies can match and aggregate anonymized first-party data with data from the clean room provider.

Media companies can safely share customer data, and marketers can layer in their own first-party data without worrying about privacy violations or competitive oversharing — because all data is hashed and encrypted before it goes anywhere near a PII field.

Clean rooms are relevant to any two parties that want to share data responsibly. Agencies and enterprises are building their own, with the goal of aggregating data sets across multiple sources. Unilever built a clean room to analyze data across platforms. Hershey’s pitched one to its retailers for safely storing customer loyalty data.

The basic mechanics: both parties upload data into the clean room environment. Identifiers (like email addresses) are matched using a one-way hash. The clean room returns aggregated, privacy-enforced outputs. No raw data crosses the boundary. That’s the whole value proposition — and also the primary constraint.

Why marketers are taking clean rooms seriously

Privacy regulations aren’t going away. GDPR, CCPA, and a growing patchwork of U.S. state privacy laws have made it harder to share sensitive data the way the industry used to. Data clean rooms offer a compliant path forward. They also reduce breach risk without sacrificing the business value of data collaboration. For cross-channel measurement, that combination is hard to replicate with other tools.

A few things have changed since clean rooms first entered the conversation. Google’s long-running plan to deprecate third-party cookies in Chrome was officially abandoned in April 2025 — after years of delays dating back to the original 2022 target. Instead of eliminating cookies, Google now lets users choose their tracking preferences. The Privacy Sandbox APIs Google spent years developing as replacements were deprecated in October 2025.

What this means practically: third-party cookies are still alive in Chrome, but they’re already dead in Safari and Firefox, and ad blockers are running on roughly 31% of devices. The signal loss problem hasn’t gone away. Apple’s App Tracking Transparency (ATT) framework still limits mobile tracking on iOS. Clean rooms exist to solve a real problem, regardless of what Chrome does next.

One other notable shift: Amazon expanded access to its Marketing Cloud (AMC) in September 2025, making it free for all Sponsored Ads advertisers — removing a cost barrier that had previously kept clean rooms a large-enterprise tool.

Measuring across walled gardens

Walled garden clean rooms get the most attention, and for good reason. Google, Amazon, and Meta all offer clean room platforms that let advertisers layer their first-party data with platform data to derive audience and campaign insights without using personal identifiers. The core use case: you upload your CRM data, the platform matches it against their audience data (using hashed identifiers), and the clean room returns insights — like whether you’re targeting the same people you’ve already reached on a different platform.

But if you advertise with more than one partner, comparing Google Ads Data Hub to Amazon Marketing Cloud to Meta Advanced Analytics isn’t automatic. Each walled garden uses its own methodology. They don’t share data with each other. To compare performance across rooms, you’ll likely need to export data from each clean room and combine it manually.

This is exactly where data standards become the difference between useful and useless.

Data clean room limitations (and why metadata management matters)

Walled garden clean rooms don’t play well with each other. They use different data structures, different privacy mechanisms, and different output formats. No one’s going to welcome a competitor’s clean room data into their environment. That interoperability gap is real, but the IAB Tech Lab’s Data Clean Rooms Standards Working Group published its first formal technical specification in late 2024 and is driving vendor alignment through 2025 and 2026. Progress is happening, but it’s slow.

In the meantime, the practical workaround is a solid metadata management framework. If you’re using consistent data inputs across every clean room — same taxonomy, same naming conventions, same parameters — then comparing outputs across platforms becomes a manageable problem instead of a chaotic one. Another open issue: there’s still no universal standard for how data gets implemented across clean rooms. Different platforms, different teams, and different regions can all be feeding in data in different formats. The result is aggregation headaches that get worse as your tech stack grows.

A quick note on metadata

Metadata is the data that describes other data. Campaign names, target audiences, creative types, channels, partners, file sizes, dates — all of it. For marketers, metadata management includes tagging digital assets with every characteristic that might matter for measurement: audience demographics, creative approach, emotional tone, placement, and format.

Without consistent processes for collecting and organizing metadata, it deteriorates fast. And deteriorated metadata means you can’t meaningfully compare performance across channels, periods, or clean rooms.

Data standards and governance for clean rooms

A growing number of enterprises use a marketing data standards platform to manage the fields, lists, and values that template their data inputs — and to maintain consistent relationships between metadata across channels and teams. These tools are part of a broader data governance framework: a defined set of processes for how data gets collected, stored, and used. Good data governance helps in ways beyond clean rooms. It streamlines internal processes, reduces the cleanup burden on data science teams, and improves ROI on campaigns by making your data truly trustworthy.

The investment is worth it. The global data clean room market was valued at $3.2 billion in 2025 and is projected to reach $18.6 billion by 2034. Organizations scaling into this space without a data governance foundation will hit a wall. The clean room will produce outputs, but those outputs won’t connect to anything meaningful elsewhere in the stack.

How data clean rooms fit into a privacy-first strategy

Clean rooms aren’t cookie replacements. They’re a component of a modern cross-media attribution approach — one piece of a larger privacy-first strategy that also includes walled garden platforms (Google, Amazon, Meta, LinkedIn, TikTok, and others) remaining primary channels, and their clean room offerings remaining the most accessible entry point.

Cohort marketing groups users by shared characteristics rather than individual identifiers, allowing for targeted advertising and measurement without PII. Google’s Topics API, which replaced FLoC as a privacy-friendly targeting signal, is still available despite the broader Privacy Sandbox shutdown. Universal IDs from providers like LiveRamp (now integrated with Habu) offer identity resolution across the fragmented web and are built directly into some clean room environments.

AI-native clean room interfaces now allow non-technical marketers to run advanced queries without SQL expertise. That’s a meaningful shift. What used to require a data science team is becoming more accessible — though the data governance requirements don’t get any simpler. Clean room data is also well-suited for experimentation: testing which audiences respond to which creative, or whether incrementality holds up across platforms. But experimentation only works if you’re using consistent inputs. A test run across two clean rooms with inconsistent metadata tells you almost nothing.

Getting your data ready for clean rooms

Data clean rooms give walled gardens a way to share customer data with advertisers, who can increase its value by combining it with their own first-party assets. That exchange yields reliable insights only if the data going in is clean, consistent, and governed.

Whether you’re building a clean room, evaluating clean room partners, or comparing outputs across platforms, the foundation is the same: solid metadata management and a data governance framework that ensure data integrity across every tool and team. That’s the thing clean rooms can’t do for you. They’re secure environments for collaboration, not substitutes for getting your own data house in order first.

And that marketing data foundation matters more now than ever. AI-powered marketing tools are only as good as the data feeding them — which means the same governance gaps that hurt your clean room strategy will follow you into AI. Our new eBook breaks down how to build a data foundation that actually supports AI-powered marketing at scale.