With almost all of the world moving online, it is seemingly understood that data privacy concerns are reshaping how companies handle consumer data. Over 65% of the global population will have its personal data protected by privacy regulations by 2025, a report by Gartner revealed. This shift has forced brands and marketers to rethink how they effectively gather, process, and use data to target consumers. Global regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) impose strict rules on how companies can collect and use personal information. As a result, marketers are increasingly turning to data clean rooms (DCRs) to balance compliance with the need for actionable insights.
But what exactly is a data clean room? In its simplest form, a DCR is a secure environment where companies can upload their data and perform analysis without directly sharing raw or personally identifiable information (PII). It allows different parties, often advertisers and publishers, to collaborate on datasets in a privacy-compliant manner. “By combining data sets within a clean room, marketers gain more accurate insights into campaign performance, customer behaviour, and attribution across different channels. Clean rooms also help in creating more precise audience segments and enable targeted advertising while preserving user privacy,” Gopa Menon, chief growth officer – APAC, Successive Technologies, told BrandWagon Online.
Data clean rooms offer an alternative, allowing advertisers to measure campaign performance, create customer segments, and optimise marketing strategies without the risk of breaching data privacy regulations. This ability to ensure both compliance and effectiveness is why data clean rooms are becoming an integral part of the modern marketer’s toolkit. “Data clean rooms play a crucial role in modern marketing by enabling secure data collaboration while maintaining user privacy. They allow content platforms to interact with advertisers without directly sharing first-party user data,” Darshil Shah, founder and director, TreadBinary, added.
Behind the scenes!
At its core, a data clean room functions as a highly secure, cloud-based environment where two or more parties, often a brand and a publisher, can share and analyse aggregated data. However, none of the participants ever access raw data or personally identifiable information. Instead, these rooms use cryptographic hashing and anonymisation techniques to ensure that individual data points cannot be traced back to real users.
“For example, a retailer might use a clean room to combine its customer purchase data with an ad platform’s user behaviour data. This allows them to target users who browsed online but didn’t buy, without sharing raw data. Similarly, a streaming service could analyse ad performance across devices by matching its user data with an advertiser’s data in a clean room, ensuring user privacy while improving ad targeting and effectiveness,” Shivangi Singh, growth director, streaming monetisation, Moloco, said.
Data clean rooms are currently being adopted by a range of industries, particularly those that handle large amounts of consumer data, such as retail, telecommunications, healthcare, and, most notably, media and advertising. “From a use case point of view, brands generating a high volume of first-party data or those investing heavily across platforms like Google, Meta, and Amazon have a strong opportunity to leverage clean rooms,” Anand Chakravarthy, chief growth officer, Omnicom Media Group India, explained adding that with the Indian Data Protection Act (Digital Personal Data Protection- DPDP) coming into play, privacy laws have become stricter. The ability to leverage first-party data effectively will necessitate the use of a Clean Room.
According to a report by BCG, over 80% of companies using data clean rooms have seen measurable improvements in their marketing performance and customer insights. “Currently, we’re seeing high application of clean rooms in the Auto, BFSI & D2C sectors – categories generating high volumes of first-party data and leveraging clean rooms allows them to derive more value from it. That said, even categories like FMCG that invest heavily across aforementioned players are leveraging platform-specific Clean Room solutions to generate richer, valued insights from their campaign data,” Chakravarthy explained.
The advantages of using a data clean room, reportedly, extend beyond just privacy. One of the key benefits is the ability to collaborate on data without needing third-party cookies. With browser-based cookies on the verge of becoming obsolete, data clean rooms provide an alternative mechanism for targeting ads and measuring campaign success. This is crucial as 69% of marketers, according to a recent Deloitte survey, believe that the end (as the ability to switch on and off will resider with users) of third-party cookies will make it harder to perform accurate audience targeting and personalisation. “We are still exploring the right tech provider and have not yet closed any partnerships. We need to ensure we choose a solution that aligns with our goals for privacy, security, and data insights. We want to make sure we have the best fit for our needs before committing,” Avi Kumar, chief marketing officer, FnP, stated his concern.
Furthermore, data clean rooms allow for secure data sharing without the involvement of intermediaries or third-party data brokers. This eliminates risks associated with exposing data to outside parties and aligns with the privacy regulations enforced by GDPR and CCPA. “Data clean rooms boost ad efficiency with improved targeting and optimization, leading to higher returns on investment. They also enhance data security and compliance, reducing breach risks and ensuring adherence to privacy laws like GDPR and HIPAA,” Manoj Karunakaran, VP- Technology, BC Web Wise, commented. From what is understood, marketers can still obtain valuable insights and improve performance without ever breaching data compliance rules. With the added benefit of increasing consumer trust through the use of privacy-first technologies, data clean rooms are quickly becoming an industry standard for responsible data use.
Challenges and limitations: Is it flawless?
However, while the benefits of data clean rooms are clear, they are not without their challenges. The integration of DCRs into existing data infrastructures can be complex and time-consuming, particularly for companies whose systems aren’t designed for such advanced collaboration.”In many organisations, data ecosystems are still evolving, with information often residing in silos. This fragmentation poses challenges when building a clean room, complicating the initial steps of data integration and collaboration,” Chakravarthy explained. The process requires a certain level of technical expertise and investment, making it less accessible to smaller firms that may lack the resources to deploy such solutions effectively.
“Implementing data clean rooms involves challenges such as maintaining privacy and security through effective anonymisation, ensuring data standardisation across platforms, and managing the technical infrastructure for scalability. Navigating stringent data privacy regulations also adds complexity to the compliance process,” Karunakaran added. Accuracy is another key concern. Clean rooms rely on anonymisation techniques to protect data privacy, but this can introduce issues with data precision. For example, by stripping away personally identifiable information, you risk losing some of the granular insights that are critical for hyper-targeted marketing strategies. In some cases, anonymisation could lead to generalisations that may not fully capture the nuances of individual consumer behaviours. According to research by Gartner, nearly 35% of marketers reported data accuracy issues when using clean rooms, which has raised questions about the true efficacy of the technology.
“Data clean rooms face challenges such as data interoperability, as different platforms store data in varying formats, requiring customisation to ensure compatibility—like integrating Google Ads data with a client’s CRM,” Singh cited. Latency in data processing can occur due to encryption and anonymisation, slowing complex queries and affecting real-time decision-making, especially when analysing large datasets. Additionally, clean rooms often limit access to granular data for privacy reasons, restricting detailed insights like user journey analysis and providing only aggregated metrics instead, she added.
The law and AI
“Data clean rooms are increasingly vital in the era of heightened privacy regulations. They provide a compliant way to leverage valuable customer data for marketing purposes without violating user privacy and thereby mitigating the risk of non-compliance and potential fines,” Menon explained. Moreover, the potential for data exploitation within DCRs is still a concern. Although cleanrooms significantly reduce the risk of data leakage or misuse, they are not foolproof. Recent reports suggest that data reconstruction attacks, where aggregated data is reverse-engineered to reveal individual identities, are technically possible, though rare. This has led to ongoing debates about whether DCRs truly protect consumer data or merely offer a veneer of compliance.
The rise of artificial intelligence (AI) adds another layer of complexity to the data clean room debate. While AI can enhance the capabilities of DCRs by automating processes and uncovering deeper insights from aggregated data, it also poses privacy risks. “These technologies can identify patterns, forecast trends, and enable real-time data analysis and could be utilised for providing the best customer experience to users on and off the platform,” Kumar added. Advanced machine learning algorithms can identify patterns in anonymised data that might inadvertently lead to the re-identification of individuals. In this context, experts argue that companies must be particularly cautious when integrating AI into cleanroom environments. A study by McKinsey highlighted that 28% of AI implementations in marketing could lead to privacy vulnerabilities if not carefully monitored.
“AI can enhance data clean rooms by generating deeper, actionable insights without compromising privacy. For instance, AI algorithms can identify patterns in user behaviour across datasets from various platforms, enabling marketers to optimise ad targeting while adhering to data privacy standards,” Singh said. Machine learning (ML) can also be utilised for predictive analytics within clean rooms, allowing brands to forecast customer behaviours based on anonymised data—like predicting which segments will respond to upcoming promotions—without accessing raw user data. Additionally, federated learning enables machine learning models to be trained on decentralised data sources without transferring sensitive information to a central server, facilitating the analysis of distributed datasets while keeping data localised and secure.
The future
So, what does the future hold for data clean rooms? While the technology presents a powerful solution for privacy-conscious data collaboration, it is still in its early stages of adoption. As more industries embrace clean rooms, stakeholders will likely see advancements in both their security and functionality. Companies are already exploring more robust anonymization techniques, such as differential privacy, to mitigate risks associated with re-identification. At the same time, regulatory bodies may tighten restrictions to ensure that even anonymised data is handled responsibly.
Although data clean rooms may offer a compelling alternative for marketers trying to navigate through the complexities of privacy regulations, it is far from a perfect solution. The technology presents challenges in terms of integration, accuracy, and potential exploitation. As data privacy continues to dominate the marketing conversation, the development and regulation of data clean rooms will undoubtedly shape the future of digital advertising, therefore, brands must tread carefully.