January 22, 2026
16
minutes read
Data clean rooms help advertisers and partners collaborate on measurement and insights without exchanging raw customer data. In this article, we’ll explain what they are, why they exist, how they work, and when they actually make sense.
If you’ve been around programmatic and measurement long enough, you’ve seen the same pattern repeat: marketers want more certainty (better targeting, clearer attribution, tighter optimization), while platforms and regulators keep shrinking the set of “easy” signals.
Data clean rooms sit right in the middle of that tension. They’re not magic, and they’re not a replacement for good data strategy. What they are is a structured way for two (or more) parties to answer specific questions together—using sensitive data—while limiting what either side can see or take away.
This article breaks down what a data clean room is, why they exist, how they work, and the major clean room models you’ll run into.
💡 If you want broader context on how privacy and identity shifts are changing digital advertising, see our cookieless overview.
⚡Clean rooms are a response to constraints, not a new form of marketing freedom. If you treat them like a shortcut, you’ll spend months building something that answers no useful question.
A data clean room is a controlled environment that allows companies to match and analyze data together under rules (“constraints”) that restrict how the data can be used and what can leave the environment.
The FTC’s plain-English framing is helpful: clean rooms enable data exchange and analysis restrained by rules that limit data use, typically when two companies want limited information about shared customers (for example, connecting ad exposure to purchases).
⚡ Think of the clean room as the rules, not the room. The value comes from the constraints: what’s allowed in, what’s allowed out, and what’s provably prevented.
Most advertising use cases boil down to four things:
A quick reset that prevents a lot of confusion later:
That last point matters. The FTC explicitly notes that protections are not typically automatic—they depend on how constraints are configured, monitored, and enforced.
💡 Learn more about adtech and adtech stack in our dedicated guide: Adtech explained: definition, ecosystem, benefits, and trends in 2026.
Clean rooms didn’t become popular because the industry suddenly fell in love with governance. They became popular because they’re one of the few ways to keep doing cross-party marketing work when the old mechanisms—third-party cookies, unrestricted device IDs, easy cross-site tracking—stop behaving like dependable plumbing.
The story here isn’t just “cookies are going away.” It’s that third-party cookie availability is no longer something advertisers can assume, and the future has been shaped by delays, regulatory oversight, and shifting implementation plans.
For example, Google’s Privacy Sandbox update in April 2024 stated it would not complete third-party cookie deprecation in the second half of Q4 2024, and it pointed to ongoing review with regulators and industry testing as reasons for timeline changes.
By April 2025, Reuters reported Google would retain third-party cookies in Chrome and not roll out a new standalone prompt, reflecting how contested and changeable this terrain has been.
Even if your strategy isn’t “cookie-based,” this uncertainty creates second-order effects:
A useful adjacent signal: in the mobile world, consent frameworks have already normalized opt-in boundaries. AppsFlyer reported that U.S. opt-in to in-app tracking was 44% as of Q1 2024—a reminder that deterministic visibility is no longer a default assumption.
⚡ The biggest shift isn’t “cookies disappear.” It’s that marketers can’t plan around a single, stable tracking default anymore, so they need collaboration models that don’t depend on it.
In the U.S., one of the most practical drivers of clean room growth is the operational reality of compliance: the rules are expanding, and teams need ways to collaborate without crossing consent and purpose limitations.
By late 2025, BSA noted that 20 states have enacted comprehensive consumer privacy laws, creating new rights for consumers and obligations for businesses handling personal data.
At the same time, NCSL documented the pace of legislative activity: in 2024, at least 40 states (plus D.C. and Puerto Rico) introduced or considered 350+ consumer privacy bills, with many enacted.
This matters to clean rooms because many common marketing workflows inherently involve “sharing”:
Clean rooms are attractive here because they can be designed to enforce “you can learn this, but you can’t take that.” The FTC is also clear that clean rooms don’t eliminate legal obligations—companies can’t treat them as a loophole around promises or the law.
⚡ Privacy compliance is a business constraint that shows up in workflows. Clean rooms help teams collaborate without constantly renegotiating what’s permissible.
The third driver is less technical and more structural: a growing share of advertising activity happens inside ecosystems where the platform owns the data, the measurement, and the rules.
EMARKETER’s forecast for 2024 put it bluntly: Amazon, Apple, Meta, Microsoft, and Google will attract almost two-thirds of U.S. digital ad dollars in 2024.
As that concentration increases, advertisers and agencies run into a hard truth:
That’s one reason walled-garden clean rooms became mainstream early—and why independent and cloud-based clean rooms are now being adopted to regain flexibility outside any single ecosystem.
⚡ Walled gardens don’t just restrict data access; they shape what “truth” looks like. Clean rooms can restore some comparability, but only if you define consistent questions across partners.
💡 If you want a deeper read on the tradeoffs advertisers face inside platform-controlled environments, our walled gardens breakdown is a useful companion.
At a high level, every clean room (regardless of vendor) is trying to solve the same problem:
Let multiple parties compute joint insights without exposing raw, person-level data to each other.
The differences are in how that is enforced, what identity methods are allowed, and how strict the outputs are.
Clean rooms work best when the question is explicit, like:
If the goal is vague (“we want to explore data together”), you can end up paying for infrastructure that produces ambiguous output.
Each party typically contributes:
The FTC notes a key differentiator vs. “normal” data sharing is the constraints—rules limiting both the analysis and what can be exported.
There are multiple matching approaches (hashed PII, privacy-safe identity protocols, publisher/advertiser reconciliation), but the principle stays consistent:
On the standards side, IAB Tech Lab has been pushing interoperability and agreed mechanics. Their data clean room work includes protocols like PAIR (publisher-advertiser identity reconciliation) and ADMaP (attribution data matching).
This is where “clean room” becomes real:
For example, AWS Clean Rooms supports controls like minimum aggregation thresholds—a common pattern where an output row must represent at least X distinct users (or it gets suppressed).
Google’s BigQuery data clean rooms documentation describes analysis rules that prevent raw access and enforce restrictions such as aggregation thresholds.
⚡ If the only reason you want a clean room is “more data,” pause. The best clean room projects start with a single measurement or planning decision that needs a defensible answer.
Outputs usually fall into one of three buckets:
A “data clean room” is really a pattern. The way it’s implemented varies based on who controls the environment, where the data lives, and what the business objective is.
In practice, most clean rooms fall into three categories.
These are clean rooms provided by large platforms where the collaboration and measurement happen inside the platform’s environment, using platform data and rules.
Typical strengths:
Typical constraints:
This is why many teams treat walled-garden clean rooms as necessary—but incomplete. They’re powerful for platform-specific truth, but not automatically a holistic measurement solution.
💡 If you’re tracking how platform shifts reshape the ad tech surface area, Microsoft’s Xandr sunset is a good example of the broader consolidation trend.
Independent clean rooms are designed to be a neutral meeting place between parties: advertiser + publisher, brand + retailer, agency + multiple media partners.
Typical strengths:
Typical constraints:
This model tends to make the most sense when the business value is clearly tied to cross-partner collaboration (not just internal analytics).
💡 For a deeper dive on why AI Digital swears by its DSP-agnostic approach, see the explainer: The DSP-agnostic approach that gives AI Digital an edge in fragmented media buying
Cloud frameworks are clean rooms built on (or embedded within) cloud data platforms. Instead of buying a “clean room product” as a black box, you use cloud-native features to create a governed collaboration environment.
What this enables:
Examples of how this looks in practice:
Where cloud frameworks shine is also where they cost you: you typically need more data engineering, more governance design, and tighter stakeholder coordination. They’re a strong fit for organizations that want clean rooms to become infrastructure—not a one-off project.
If you strip away the hype, most data clean rooms are used for one thing: decision support under privacy constraints. They’re strongest when two parties already have meaningful data, but can’t legally—or practically—swap row-level records.
⚡ Attribution improves when you control definitions. Clean rooms help you connect exposure and outcome, but you still need to decide what counts as success, and what doesn’t.
Below are the use cases that show up repeatedly in real-world deployments (and in the research). I’m keeping these grounded on what clean rooms reliably do today, not what vendors promise they’ll do “soon.”
The most common reason teams pursue a data clean room is the same reason measurement conversations feel harder every year: signal loss reduces confidence, while leadership still expects proof.
IAB’s State of Data 2024 captures that tension clearly. Among companies asked how cookie deprecation plus proliferating legislation would impact them, 73% expected their ability to attribute performance, measure ROI, track conversions, and optimize campaigns to be reduced, and 57% expected it to be harder to capture reach and frequency.
A clean room helps because it lets two parties connect exposure and outcome inside a governed environment. The output is usually aggregated reporting, not person-level logs. That changes what you can do, but it’s still valuable.
Here are the measurement tasks clean rooms tend to support well:
📍 Key takeaway: clean rooms are a measurement workspace, not a measurement methodology. If your methodology is weak, a clean room will produce precise-looking results that still don’t answer the business question.
⚡ A clean room can reduce data risk. It can’t reduce thinking risk.
💡 For a deeper dive into CTV measurement, see our dedicated guide: CTV measurement: The key metrics that will define successful campaigns
Audience work is where clean rooms often surprise teams—in a good way. You stop obsessing over “who” (identity-level targeting) and start learning “what” and “how much” (planning-level insight).
This use case usually looks like:
What’s important is the boundary: most clean rooms don’t let you walk away with the other party’s audience list. You’re learning patterns and sizing, not building a portable identity graph.
This is also where clean rooms often get paired with other measurement approaches. For example, some organizations use clean rooms for overlap/duplication and then use MMM to understand channel contribution at a higher level.
📍 Key takeaway: audience insights in a clean room are usually about planning and strategy, not “new targeting superpowers.”
💡 For a deeper dive into the digital advertising supply chain, see The digital advertising supply chain explained: How ads really reach audiences
Clean rooms exist because collaboration is back, but it needs guardrails.
The publisher side is especially telling. A Digiday survey (sponsored research) found 64% of publishers and media businesses were collaborating with advertisers via clean rooms to share audience data, plan campaigns, and measure performance.
This use case typically lands in three buckets:
📍 Key takeaway: clean rooms don’t “open up” publisher data. They create a way to collaborate without giving away the asset.
This section matters because data clean rooms are easy to oversell. Even the FTC has warned that the name can be misleading: they’re not rooms, they don’t “clean” data, and the protections depend on how the system is actually configured and used.
Here are the misconceptions that cause the most wasted time and disappointment:
A clean room can support privacy-friendly collaboration, but it’s not a legal shield. The FTC’s position is straightforward: companies still need to honor the law, their privacy promises, and meaningful governance.
What to do instead: treat the clean room as one component in a compliance posture (consent strategy, retention policies, partner contracts, and access controls still matter).
A CDP is designed to unify and activate your customer data across your channels. A clean room is designed to collaborate on analysis with someone else under restrictions.
They can complement each other, but they solve different problems.
Rule of thumb: if the work is internal (segmentation, lifecycle messaging, personalization in owned channels), start with your CDP/warehouse. If the work requires a partner’s data (publisher exposure, retailer purchase signals, platform measurement), that’s where clean rooms become relevant.
⚡A clean room can reduce risk, but it rarely reduces work. Identity, governance, and partner coordination are the real cost centers, not the software.
Some clean room setups rely on identity signals to match records, but they do not automatically give you a durable, reusable identity graph.
IAB Tech Lab’s work on clean room interoperability (including PAIR) is partly about standardizing how two parties reconcile identity for specific collaboration use cases, not about creating an industry-wide identity backdoor.
Some clean rooms support limited activation (often within the same ecosystem where the data lives), but many are designed primarily for analytics outputs. The more “portable” the activation becomes, the more privacy risk and governance complexity you inherit.
Practical expectation: clean rooms are usually best for measurement and insight. If activation is the primary goal, clarify up front where activation will happen and what leaves the environment.
Clean rooms don’t fix:
They make collaboration possible. They don’t make it meaningful by default.
⚡ Clean rooms don’t replace strategy. They expose whether you have one.
CTV and programmatic are where clean rooms become especially relevant—because that’s where measurement fragmentation and platform constraints collide.
CTV is growing, and it’s increasingly treated as a core line item rather than an experiment.
More CTV spend means more executive attention on questions like: Did this drive incremental reach? Did it move sales? Are we hitting the same households everywhere?
Clean rooms can help because many CTV environments are “closed” by design. You often need the platform or publisher’s cooperation to connect exposure to outcomes in any controlled way.
💡 If you want a broader view of how CTV buying and measurement work (and where the common gaps show up), AI Digital’s CTV guide is a solid companion resource.
Programmatic runs on distributed systems: DSPs, SSPs, measurement vendors, data providers, and publishers all touch the transaction. That makes data collaboration both valuable and risky.
In practice, clean rooms show up in programmatic ecosystems in a few ways:
💡 For the programmatic basics (and how the ecosystem functions end-to-end), see AI Digital’s programmatic advertising guide.
Platform changes can also reshuffle how data collaboration happens. Microsoft’s decision to sunset Xandr is a good reminder that supply and platform strategies evolve, and measurement workflows need to adapt with them.
This is the point where a lot of teams want a simple answer: Do we need one or not? The real answer is a framework.
A data clean room makes sense when three things are true:
If any of those are missing, you can usually get farther with simpler approaches.
Before you commit budget and engineering time, pressure-test these areas.
A clean room is rarely justified for “nice-to-know” analysis. It’s justified when the decision changes spend, strategy, or partner allocation.
Examples of “worth it” questions:
IAB’s State of Data 2024 shows why this matters: companies are already anticipating weaker attribution and harder reach/frequency measurement under signal loss.
If your conversion events are inconsistent, your customer records are fragmented, or your consent signals aren’t trustworthy, a clean room won’t fix that. It will simply produce cleaner-looking confusion.
Minimum viable readiness:
Clean rooms are a two-party (or multi-party) sport. If your key publishers, retailers, or platforms aren’t willing or able to participate, a clean room becomes shelfware.
This is why many organizations start with the partners where collaboration is already natural—retail media, major publishers with authenticated audiences, and platforms that offer defined clean room workflows.
This is the unsexy part that determines success:
The FTC’s warning is relevant here: clean rooms don’t automatically protect privacy. The rules and monitoring do.
You can often say “no” with confidence if:
📍 Key takeaway: clean rooms are powerful when they answer high-stakes questions under strict constraints. They’re frustrating when they’re treated as a general-purpose analytics toy.
⚡ A confident “no” is a good outcome. If you can’t name the decision the clean room will change, you’re not evaluating a tool, you’re collecting complexity.
Data clean rooms matter because advertising is being redesigned around privacy boundaries, platform control, and a shrinking set of “free” signals. They are one of the few tools that let marketers collaborate with partners without reverting to risky data sharing.
But here’s the honest conclusion:
If you want help evaluating whether a clean room approach makes sense for your data, partners, and measurement goals, AI Digital’s team can help you sort the “worth it” from the “noise.” Why not get in touch?
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Inefficiency
Description
Use case
Description of use case
Examples of companies using AI
Ease of implementation
Impact
Audience segmentation and insights
Identify and categorize audience groups based on behaviors, preferences, and characteristics
Automated ad campaigns
Automate ad creation, placement, and optimization across various platforms
Brand sentiment tracking
Monitor and analyze public opinion about a brand across multiple channels in real time
Campaign strategy optimization
Analyze data to predict optimal campaign approaches, channels, and timing
Content strategy
Generate content ideas, predict performance, and optimize distribution strategies
Personalization strategy development
Create tailored messaging and experiences for consumers at scale
Data clean rooms protect privacy by keeping collaboration inside a controlled environment where access and outputs are tightly restricted. Instead of sharing raw, person-level datasets, parties bring data into a governed space, match it using approved identifiers, and run analysis under rules that prevent anyone from extracting user-level records. Those rules typically include restrictions on what queries can be run, minimum aggregation thresholds so results can’t be used to isolate individuals, and auditing so activity is traceable. The key idea is that the insight leaves, not the underlying data, and the “safety” comes from how the clean room is configured and enforced, not from the label itself.
Clean rooms do not fix weak measurement foundations. If conversion events are inconsistent, identity signals are unreliable, or taxonomy is messy, a clean room won’t turn that into trustworthy insight. They also don’t provide a universal view across every platform by default; each collaboration depends on available match keys, partner participation, and the constraints imposed by the environment. Finally, clean rooms are not a shortcut to portability: they are designed to prevent raw data extraction, so they won’t hand you a reusable identity graph or a fully exportable audience you can move anywhere without restrictions.
Some do, but many don’t need one immediately. If you have limited first-party data scale, minimal partner data access, or your biggest performance wins are still available through better instrumentation and testing, a clean room may be premature. It starts to make sense for smaller or mid-sized advertisers when their strategy depends on partner datasets they can’t otherwise use safely—common examples are retail media measurement, premium publisher collaborations with authenticated audiences, or CTV plans where you need privacy-safe outcome validation. The deciding factor isn’t company size; it’s whether you have a high-value question that requires two-party data collaboration and will change spend or strategy.
A CDP is built to unify and activate your own first-party customer data across your marketing and owned channels, usually with an emphasis on segmentation, personalization, and orchestration. A data clean room is built for collaboration between parties who cannot share raw data, enabling joint analysis and limited activation under strict constraints. In practice, the two can complement each other: your CDP or warehouse often prepares the data you bring into a clean room, and the clean room produces aggregated insights you use for planning, measurement, and partner decisions rather than for building a fully portable customer profile.
No. Privacy compliance is driven by lawful collection, consent and purpose boundaries, security controls, data minimization, retention practices, and honoring consumer rights. A clean room can support a privacy-forward approach to collaboration by limiting what’s shared and what can be exported, but it does not replace compliance work and it doesn’t automatically make a questionable data practice acceptable. Think of clean rooms as a way to reduce exposure when collaboration is necessary, not as a requirement or a loophole.
They can be used for activation in some cases, but measurement and insight are usually the most reliable core value. In walled-garden environments, activation often works best because the clean room lives inside the same ecosystem where media is bought, so segments can be created and applied without exporting user-level data. In independent or cloud-based frameworks, activation is more variable and often depends on integrations, governance rules, and the level of portability the parties are comfortable allowing. If activation is a primary goal, it’s important to clarify early where activation will happen, what exactly will leave the environment, and how privacy constraints will be enforced end-to-end.
Start with whether the clean room solution fits your specific data collaboration needs: the partners you actually work with, the match keys you can legally use, and the measurement questions you need to answer. The best choice is usually the one that enforces clear constraints, supports repeatable workflows, and integrates cleanly with how you already run reporting and activation.
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