The 3 Clean Room Principles That Every Marketer Must Know To Protect Their Data

Steve Silvers Neustar

Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media. 

Today’s column is written by Steve Silvers, SVP product, GM customer experience, Neustar.

Advertisers have spent years building marketing machines that rely on digital user data. Now they have to adapt to a new era in which privacy rules and consumer preference limit the amount of data available to marketers.

To prepare for these big changes, the industry should look for inspiration from the inventor of the clean room: Willis Whitfield.

Whitfield was a physicist at Sandia National Laboratories. In 1960, he was asked to come up with a solution to stop microscopic dust particles from infiltrating mechanical components in the manufacture of nuclear weapons. His design filtered and circulated the air in a lab so well that cigarette smoke blown in one side came out clean on the other side.

His workspace became known as a "cleanroom," and revolutionized manufacturing in electronics and pharmaceuticals, made hospital rooms safer, and helped further space exploration.

Whitfield’s invention of a safe and protected space can also revolutionize the digital advertising ecosystem. Stay with me here.

Without perishable IDs (e.g. cookies), marketers will have to rely more on sensitive customer-level data (e.g. hashed email and phone numbers) to target ads and measure performance. The use of personal data poses a problem for the $325 billion global digital advertising industry, because the more times personal user data changes hands, the higher the risk for security and privacy breaches.

Marketers need safe, secure digital environments, so-called data clean rooms, where multiple parties can put their data without ever exposing the raw data set to any other party. Data clean rooms are the collaborative tool essential to sustain and nurture digital advertising in a new privacy era on the Web.

But not all data clean rooms are the same. When collaborating within a clean room, keep in mind how these three characteristics – which vary by clean room – will protect user privacy and enable (or complicate) various marketing use cases.

1.Data security, compliance, and control: These are the table stakes for clean rooms. Organizations sharing data in a clean room need to have a data governance framework that guides how data is collected, validated, stored, organized, and protected. They use clean rooms to enforce their framework. A clean room makes data control possible. Each party has absolute control over the selection of data it allows to be analyzed. Everyone can perform mutually sanctioned marketing use cases without ever accessing the raw data.

The beauty of the clean room is twofold: data does not need to move, and no single party will have control over all the data, including the clean room vendor.

Businesses that previously were reluctant to share proprietary data are becoming more open because of clean rooms’ data security.

For example, retailers rarely share prized shopper transaction data with consumer-packaged good companies. But a data clean room would be a good place to share data in a socially distant way, so to speak. The CPG company gains rich data to improve its targeted ads to shoppers and encourage repeat purchases. Retailers can more quickly turn their online stores into marketing platforms for advertising to fuel growth.

2. Identity protection: Fully protected identity data requires the use of privacy-preserving technologies. If the information from a clean room leaks out, no problem. There are no personal identifiers associated with any of it. The data can’t be connected back to any source of personally identifiable information.

There are several technologies in use today that protect individual privacy.  A common approach, known as cohorting, creates groups that have meaningful behaviors and interests in common without identifying any individual or record. Other data privacy techniques include differential privacy, k-anonymity, imputation, and perturbation.

Although privacy-first data clean rooms are not a new concept, they haven't been widely adopted in practice. Google and Facebook, for example, have cloud environments where stored aggregated data is accessible to their largest advertisers. But organizations find this approach limiting because they can't join the data to build a full user journey to execute specific addressable media plans across channels.

3. Privacy-centric learning: Matching first-party data with additional consumer behaviors, transaction data, and granular measurement data involves the collaboration of data sets from several parties. But organizations are hesitant to share data because they are afraid of data leakage in a rapidly changing legal landscape and want to limit the number of parties who have access to their proprietary data.

Clean rooms make data sharing risk-free as well as more effective and more efficient. Commonplace marketing use cases such as frequency capping and audience activation are a good place to start to use clean rooms. If several parties get involved, they can also enable advanced use cases such as multi-touch attribution.

The bulk of the data along a customer journey is protected behind multiple walled gardens, which complicates things for advertisers who want to measure performance along that journey.

In a data clean room, walled-garden data can come together with data from other platforms. Then, advertisers can extract valuable information about consumer engagement across all touchpoints, channels, and gardens.

Today, privacy-centric insights are mostly limited to audience sizing, which allows marketers to discover where relevant customers intersect using historical data and to target with more precision. But in the future, clean rooms will open doors for more advanced analysis. You could run analytics across two data sets without co-mingling the data. Insights will become richer and enable brands to optimize tactics and marketing spend. It’s not a fantasy to think that clean rooms and decentralized data collaboration will be the new standard in a privacy-first world.

With data privacy as the only way forward, companies have to invest in new ways to collect and use first-party data. It's helpful to think of data clean rooms as a type of next-generation business process improvement software. Why not create the new infrastructure, like Mr. Whitfield did in the 1960s, and make the leap forward in data-driven marketing?

Follow Steve Silvers ( @SteveSilvers), Neustar (@Neustar) and AdExchanger (@adexchanger) on Twitter.

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