Data Clean Rooms: How Enterprises Balance Data Privacy with Business Intelligence

How technology and data analytics leaders at Fortune 500 enterprises are turning to data clean rooms to glean critical business insights from aggregated, privacy-friendly data.

Data is more critical to enterprises than ever.

However, mitigating cyber threats and maintaining legal compliance while personalizing the customer journey has posed a thorny challenge for CIOs, CTOs, and Chief Data Officers.

Amid increasingly stringent data privacy regulations, cybersecurity risks, and industry-driven factors like the slow death of third-party cookies for online tracking, technology and data officers are under pressure to adapt quickly to how they handle customer data online.

One adaptation that has seen success among the nation’s top enterprises is the use of data clean rooms.

What are Data Clean Rooms?

A data clean room is a secure, cloud-based environment that allows organizations to access and analyze sensitive data without exposing personally identifiable information (PII) and running afoul of privacy legislation like HIPAA, GDPR, or California’s Consumer Privacy Act.

Data clean rooms provide aggregated, non-personally identifiable data from collaboration between several parties

Here’s how they work:

A pair of entities – such as an advertiser and a publisher (e.g., Google Ads) – are required to assemble their first-party data packages and upload them onto the data clean room. The clean room then encrypts and anonymizes the data to ensure privacy is maintained. Once the data is aggregated, both parties are provided with insightful, non-PII data in the form of cohorts and comprehensive, aggregated reports to help them make informed decisions.

Privacy techniques, including encryption, hashing, pseudonymization, access restrictions, and noise injection, are employed to ensure compliance with regulatory requirements for data clean rooms.

The concept of data clean rooms has been around for several decades and was initially used in the healthcare industry to enable researchers to analyze patient data without violating privacy laws. However, as the use of data has become more prevalent across all industries, clean rooms have become a more widely adopted solution for organizations seeking to maintain data privacy and security.

The Need for Data Clean Rooms

Apple’s Safari browser, as well as Firefox, have already blocked third-party cookies by default, and Google plans to do the same later in 2023.

As online activities become more private, consumers’ appetite for personalized experiences continues to increase. This is a well-documented phenomenon known as the privacy paradox.

This poses a challenge for data analysts, who are under competitive pressure to deliver more granular customer insights and a deeper, nonlinear understanding of the customer journey.

In addition, customer attribution models are reaching levels of maturity that require rich data, encompassing both online and offline touchpoints.

Collaboration with Digital Ecosystem Partners

Data clean rooms help solve these problems by aggregating and anonymizing first-party data from several ecosystem partners.

For example, major AdTech platforms like Meta and Google Ads aggregate data from their advertising customers and deliver important insights on audiences and user interests without distributing identifiable information. This is known as a walled garden clean room because data is kept within a single platform and cannot be easily intermingled with external data sources.

In other cases, companies might build their own data clean rooms, or seek out an independent provider. Companies like Snowflake, InfoSum, and Aqilliz have all deployed solutions that enable companies to process shared data sets, while enterprises like Hershey’s and Disney have deployed their own clean rooms to maximize their advertising efforts.

Data Clean Room Considerations for Data Officers and CIOs

To achieve success in this landscape, it is imperative for ecosystem participants to adopt a proactive and strategic stance towards integrating clean room technology, processes, and skilled personnel. It is equally important to remain cognizant of market trends, such as the growing need for more detailed attribution and measurement, in order to stay ahead.

Data leaders must understand the benefits of data clean rooms, as well as certain limitations, and evolve their strategies accordingly.

A significant drawback of leading data clean room providers, such as Google and Facebook, is the limited interoperability of their data. Due to their "walled garden" approach, integrating data from one clean room with another is a near-impossible feat.

In addition, data clean rooms do not enable direct access to first-party data of other participants. Of course, this is the point of a clean room in the first place. However, without access to the data, it is essential for CDOs to independently verify the quality of data that is aggregated within a clean room.

Building a Privacy-Friendly Data Ecosystem

As data privacy standards and regulations continue to evolve, it's becoming increasingly important for organizations to take steps to protect sensitive data. Data clean rooms provide a way for organizations to comply with regulations while still gaining valuable insights from their data.

By using a clean room, organizations can stay competitive, maintain customer trust, and avoid legal and ethical violations. As such, they are becoming an essential tool for large organizations in a wide range of industries.