Data Clean Rooms: A Thorough Overview

Imagine you are heading a pharmaceutical company on the brink of finding a groundbreaking cancer treatment. You have only one hurdle: Finding the right patients for the clinical trial without breaching HIPAA. Thankfully, there is a healthcare provider with access to patient data, including anonymized records that could direct you to eligible candidates. 

Both of you face a common challenge. 

"How can we collaborate without exposing sensitive patient information?"

Enter the data clean room.

In this privacy-compliant environment, your team uploads the trial eligibility criteria while the healthcare provider adds their anonymized patient data. The clean room’s advanced algorithms match patients to the trial, giving you crucial insights without revealing private patient information. Your company can bring life-saving medicine to the market without sacrificing patient confidentiality. This is just a small example of how data clean rooms are transforming the gathering of collaborative insights in a secure setting. 

What Is A Data Clean Room?

A data clean room (DCR) is a collaborative environment where companies share their first-party data for analysis and insights. Stringent protocols and technologies ensure compliance with privacy and regulatory requirements and restrict access to the room, safeguarding data and companies’ reputations. 

Why is It Important & Why Are Companies Using It?

In the modern, digital era, business collaboration is a non-negotiable for success. However, this also compromises data privacy and security, expanding the attack surface. Given this, data clean rooms provide a safe platform for enriching insights through shared data, without affecting the integrity of the information. Let’s look at why a data clean room is important for companies. 

• Using data clean rooms shows a company’s commitment to protecting customer information at all times
• DCRs empower privacy-focused industries like healthcare and finance to share data for research, fraud detection, and advanced analytics
• They ensure data privacy and compliance during collaboration, especially with evolving regulations like GDPR, CCPA, and HIPAA
• They offer appropriate tools and infrastructure to perform advanced data analysis and extract valuable insights without data breach risks to any party
• Data clean rooms facilitate partnerships, even unconventional ones, between multiple, vetted parties to collaborate on sensitive data without losing its integrity. For instance, an FMCG Fortune 500 can collaborate with a UN research wing to understand nutrition better.

Types Of Data Clean Rooms:

There are different types of data clean rooms that serve their unique purposes in helping with data collaboration. To help you understand the data clean room landscape, let’s dissect each one of them.

Walled Gardens:

Google, Amazon, and Meta were the first ones to introduce walled gardens. They wield control over the hardware, applications, and content. A Walled Garden clean room is usually created and managed by major tech companies as it helps them commercialize their 1st party data without any security issues. Without affecting consumer privacy, it makes entry-level data available for marketers to make informed decisions.

The data is used to build better marketing and advertising campaigns within a particular platform. Walled Gardens are a popular choice since Google, Amazon, and Meta alone account for 60% of all digital ad spend. 

Source for statistic: Click here

Data Warehouse Clean Rooms for Adtech:

Some of the leading data warehouses (DWHs) such as AWS, Google, and Snowflake offer an optional data clean room service in partnership with adtech vendors, Here the partner companies, usually including advertising players, share their first-party data in a neutral room for improved marketing and consumer insights. 

DCRs by Data Onboarding Vendors:

There are data onboarding vendors who provide DCRs. These vendors offer additional capabilities like access to their data marketplace. Here, you can also leverage data from their network and not just your partner. They can help you with enriching first-party data with second and third-party data. 

Features Of A Data Clean Room:

Let’s look at a few features of a data clean room that ensure effective collaboration.

Data Isolation:

Data from different platforms is employed in an isolated environment, thereby preventing data leakage or unauthorized access. To maintain the integrity and confidentiality of the data, there are access controls and permission levels.

Privacy and Security Controls:

Personal Identifiable Information (PII) is hashed or anonymized to protect individual identities. It prevents unauthorized access by ensuring that data is encrypted both in transit and while at rest. Only authorized personnel are allowed to perform tasks in the clean room. Identifiable data never leaves the environment. Users interact with the data through secure queries. 

Compliance With Regulations:

A major feature of data clean rooms is that it is compliant with data regulations such as GDPR and CCPA. They maintain audit trails for transparency and accountability. Features like consent management ensure that data aligns with the agreed terms. 

Seamless Data Collaboration:

A data clean room allows multiple parties to upload and analyze datasets but without allowing either of them to view each other’s original data. They can execute custom queries and gather insights from the combined datasets without compromising the confidentiality of their partner’s data. This feature is enhanced in some clean rooms because of their ability to operate with different data formats. 

Advanced Analytics Capabilities:

Its advanced analytics capabilities allow businesses to perform statistical analysis, predictive modeling, and train machine learning models on shared data. The collaboration makes the output of such exercises especially valuable. And of course, these analyses are anonymized or aggregated so that sensitive information remains protected. The ability to process shared big data in real-time means that clean rooms can be used by market leading firms for large-scale initiatives.

Pros Of Data Clean Rooms:

• It’s a sophisticated solution for optimizing your marketing campaigns, creating better data management strategies, or gathering enhanced insights for research and development
• They are industry-agnostic, allowing varied parties to collaborate without exposing the original data to one another. This is useful for all - from privacy-focused industries such as finance to consumer insights-led sectors like advertising where first-party and third-party data work together for campaigns.

Cons Of Data Clean Rooms:

• Setting up or renting data clean rooms requires significant investment and might not be feasible for smaller firms
• The data may have to be unified into a single format, straining resources and limiting usability
• Data that is aggregated, especially for ad targeting and reporting, might be less accurate than identity-based data
• Anonymization, which is used to preserve privacy, can result in less accurate insights as it reduces the granularity of the data stored and analyzed
• Since many data clean rooms are walled gardens, they work only for a specific platform (Eg: Google or Facebook). Therefore, it forces advertisers to combine results from different data clean rooms driving up costs and time spent
• Companies might be hesitant to share all of their first-party data into a data clean room impacting how comprehensive the results are
• Since some data clean rooms are manually managed, they are vulnerable to human errors.

Risks Of Using Data Clean Rooms:

• Many clean rooms are provided by third-party vendors, which creates dependency, limiting your flexibility and innovation over time
• The insights derived from clean rooms can be reverse-engineered, if the outputs are overly granular, potentially leading to privacy breaches
• Lack of clear governance frameworks can lead to non-compliance with regulations
• Time-sensitive decision-making might be hindered if the architecture isn’t optimized for real-time analytics
• The security protocols and functionalities of data clean rooms vary significantly across different vendors, resulting in compatibility issues and inefficiencies across collaborations
• The effectiveness of a clean room is dependent on its proper implementation, maintenance, and governance. Even a single misstep will undermine its purpose. For example, in the banking industry, unintentional exposing of identifiable customer financial data can lead to regulatory fines under GDPR or CCPA, significant damage to the bank’s reputation and disastrous consequences for the customers placed at risk.

Role Of Data Clean Rooms In Data Strategy:

With customers creating complex digital footprints across private and public channels, a business today has to look outside its digital boundaries to completely master customer engagement. This is where data clean rooms play a crucial role.

Enable a unified customer view:

To get a 360-degree view of customers, data collaboration is essential. For instance, a lending fintech has to work closely with multiple banks and marketing and advertising agencies. It can use DCRs to build customer understanding useful across this ecosystem without endangering sensitive information.

Power Cross-Channel Analytics:

For a comprehensive customer strategy, you have to integrate data from multiple sources and touchpoints, such as retail POS, OTT viewership and social media use. Data clean rooms allow for an analysis of such cross-channel data. Doing so allows businesses to identify shifts in consumer behaviour, respond with targeted marketing, and measure customer reaction to these campaigns.

"At Kantar, we aim to use the Snowflake partnership to connect unique users of certain websites, apps, or devices with our global panelist network to enable survey based measurement, in order to identify media impact and consumer behaviours. The ultimate goal for Kantar is a new, fully privacy compliant, and a more controlled way to establish cookie-less integration with publishers. Kantar is helping brands, agencies and publishers prove the value of their media campaigns or platforms and enable media optimisations." - Robert Katz, VP of Global Product, Ecosystem Partnerships at Kantar, a marketing and data analytics company

Source for graphic and quote: CLEANING UP: Will data clean rooms clear up cookie crumbs?

Boost Ecosystem Cooperation:

Collaboration is inevitable, but if there are no structured environments like data clean rooms, data can be manipulated or exposed during shared analysis. Clean rooms ensure that data remains secure and unaltered, encouraging business to cooperate and innovate to serve the customer. 

Data Clean Rooms Best Practices:

To fully reap the benefits of data clean rooms and to operate with minimal risks, organizations should adhere to the following best practices.

Define Objectives:

Establish the specific goals for which you are leveraging a data clean room. Whether it is to conduct clinical trials, improve marketing attribution, or enhancing customer segmentation, ensure that you have clearly defined objectives so your and your partners’ time and resources are not wasted.

Classify Your Data:

Define and divide the data based on sensitivity and compliance requirements. When data is categorized into the appropriate tiers, it allows for the right access controls and makes sure that it is compliant with the regulations.

Choose the Right Provider:

Select a data clean room that meets your organization’s technical, regulatory, and operational requirements. Evaluate their encryption standards, compliance certifications, scalability, integration capabilities and market reputation. 

Implement Security Measures:

Security protocols such as encryption, access controls, and secure authentication will safeguard your data in the clean room. Ensure strict frameworks for how the data is uploaded, accessed, and shared in the data clean room. Only authorized users, with clearly defined roles and permissions, must be allowed to execute specific tasks. 

Data Anonymization:

The usage of advanced data anonymization techniques such as differential privacy or hashing will protect individual identities. Check if the data clean room applies these techniques securely and consistently. 

Focus on Compliance:

Make sure that the clean room is compliant with data protection regulations such as HIPAA, GDPR, or PIPL. They must regularly audit their data practices and maintain a log of all their activities to demonstrate their adherence to compliance.

Data Governance: 

Establish clear data ownership and management processes for data handling and lifecycle management for all partners. Make sure that clear documentation is easily accessible at all times. 

Use Aggregated Outputs:

To reduce the risk of data exposure, limit outputs from the clean room to aggregated or anonymized insights. There must be clear parameters for the level of granularity that is allowed in analytical results. 

Train Staff:

Invest in specialized training for employees and stakeholders on clean room technology, compliance requirements, governance policies, and crisis handling. Users who are well-trained are less likely to make big mistakes.

Align Expectations Clearly:

Since you will collaborate with multiple parties, make sure you establish agreements on data-sharing terms, usage policies, and output limitations. When there is a well-documented agreement in place, it will prevent disputes and disagreements.

Regularly Audit and Monitor Safety and Security:

Continuously audit and monitor access controls and data usage to determine imminent threats. There must be regular security assessments for effective, proactive threat mitigation. 

Evaluate Flexibility and Scalability:

As the volume of data increases and use cases evolve, the clean room infrastructure must be able to scale seamlessly. Also, choose solutions that support interoperability with other tools and emerging technologies. 

Data Clean Room Use Cases

Let us look at some of the data clean room use cases that empower industries to gain insights without exposing sensitive data. 

Marketing and Advertising Attribution:

Marketers can use clean rooms to measure the effectiveness of marketing campaigns without exposing individual user data. 

Example: An advertiser can compare their ad impression data with a retailer’s sales data to find out the percentage of sales that were driven by a specific campaign. The clean room makes sure that neither party’s data is revealed while delivering the insights.

Customer Segmentation and Personalization:

Retailers and brands can collaborate to refine customer segmentation strategies. 

Example: A luxury fashion retailer can partner with a credit card company to identify high-spending customers without exchanging sensitive customer details. It helps the retailer send targeted offers to the right audience. 

Mergers and Acquisitions:

Data clean rooms provide enterprise decision-makers with a safe environment for aggregating and analyzing data. Decision-makers can compare sensitive data and find out if there are potential roadblocks that can harm the deal. Confidentiality for all parties while accessing the insights is a guarantee.

Example: When company A decides to acquire company B, due diligence requires a deep dive into sensitive areas such as financial projections, customer databases, and intellectual property portfolios. Instead of sharing such sensitive information with each other, the companies can upload their data into the secure data clean room. The clean room reveals valuable insights such as a strong alignment in customer segments and no major compliance risks. The deal gets sealed successfully without compromising the integrity of both companies.

Fraud Detection and Prevention:

Financial institutions can use clean rooms to detect fraudulent activities by comparing transaction patterns across organizations. 

Example: Multiple banks can pool anonymized data and identify patterns that are indicative of fraud. It could be an unusual cross-bank activity that can set the alarm bells ringing. 

Healthcare and Clinical Research: 

Pharmaceutical companies can streamline patient recruitment for clinical trials with the help of data clean rooms. 

Example: It can match anonymized patient data with trial criteria. Using this, organizations can find eligible candidates without violating any healthcare privacy laws.

Supply Chain Optimization:

Manufacturers and suppliers can collaborate to share inventory details, production schedules, and demand forecasts. It will allow for better coordination, and reduce the chances of stockouts or overproduction, while ensuring that sensitive and proprietary information remains protected. 

Media and Entertainment Insights:

Audience engagement can be evaluated using clean rooms by streaming platforms and content creators. 

Example: A streaming service can share aggregated viewership data with a production studio to determine the success of a particular series or identify trends in content preferences. 

Data Clean Room Examples:

Conclusion:

With the end of third-party cookies looming large, the need for data clean rooms will grow fast. By offering a secure environment where businesses can pool data for analysis without exposing original data, clean rooms offer a solution that today’s privacy-focused and data-driven world needs. 

Interested in learning more about this powerful data collaboration tool? Our experts have rich experience in anything to do with data and will be glad to work with you.

Talk to Tredence now. 

FAQs:

What is a Data Clean Room?

It’s a secure environment which allows multiple parties to share data for joint analysis, without exposing original data. There are guidelines and regulations in place to protect sensitive company information and personal identities of users. Any personally identifiable information data input into the data clean room is encrypted. The data owner has full control over how their data is used.     

What is the Difference Between a CDP and a Data Clean Room?

Embracing the strengths of both CDPs and DCRs is a great way to unlock the full potential of customer data for your data strategy. 

How to Build a Data Clean Room?

The first step toward building a data clean room involves creating a secure environment where multiple parties can share and analyze their data without exposing sensitive information. 

• Before building a DCR, identify the use cases and intended applications. Some of them are: fraud detection, marketing attribution, joint product development, and research partnerships.
• Select the right tools and platforms to build a DCR. Cloud platforms such as AWS Clean Rooms, Google BigQuery DCR, and Snowflake are great solutions for secure data sharing.
• Build the architecture: data ingestion layer, data governance mechanisms such as role-based access controls and encryption, analysis environment, and output restrictions.
• Establish security protocols such as zero-trust architecture and secure encryption key management.
• Have data governance and privacy compliance standards.
• Train stakeholders. 

How Are Data Clean Rooms Used?

DCRs are used across industries for privacy-compliant collaboration and analysis. Here are some of the key applications:

• It’s used in marketing to measure the effectiveness of advertising campaigns
• They are used in retail for improved product targeting, personalized marketing strategies, and enhanced customer experiences
• Financial organizations use DCRs to collaborate on fraud detection without exposing sensitive customer information
• DCRs allow companies to share and analyze data on inventory levels, supplier performance, and shipping routes. This helps improve visibility across the supply chain, optimizing operations and reducing costs 

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Editorial Team
Tredence