As global data privacy laws become stricter and as marketing powered by Artificial Intelligence (AI) becomes the norm, brands face a challenge. They urgently need to collaborate on richer data to achieve accurate targeting, yet they must do so within very tight privacy boundaries.
What is the solution emerging worldwide? Data Clean Rooms (DCRs). These are secure, privacy-safe environments that allow multiple parties to analyse shared data without revealing any raw Personally Identifiable Information (PII).
For insight and analytics teams, this represents a major step forward in designing research that prioritises privacy from the start.
Why Data Clean Rooms are Gaining Momentum
Laws like India’s DPDPA (Digital Personal Data Protection Act) and its global equivalents, such as GDPR and CCPA, are now being strictly enforced. Consequently, traditional ways of sharing data (between advertisers, platforms, or research vendors) are becoming much riskier.
Therefore, clean rooms enable organisations to:
- Collaborate: Brands and research partners work together on data insights without exchanging direct identifiers.
- Innovate: AI models learn from combined datasets while the strict control over who accesses the data remains in place.
- Comply: Data governance is auditable and bound by policy, helping you meet regulatory standards.
How Clean Rooms Transform Market Research
Clean rooms help businesses conduct sophisticated research while keeping customer data safe. For example, you can:
- Enrich Your Audience: Combine your own Customer Relationship Management (CRM) data with information from a publisher or partner. Crucially, you can identify audience overlaps while keeping both original datasets completely anonymous.
- Measure Campaigns Without Cookies: Use privacy-safe data joins to measure advertising performance and its impact across different platforms.
- Share Insights Safely: Conduct joint research projects between an advertiser and an agency, or even between two different brands, without ever exposing raw consumer data.
Practical Architecture: How It Works
A standard clean room architecture uses several layers to protect data. Specifically, the process involves:
- Secure Data Onboarding: Both parties upload datasets that have been encrypted or hashed.
- Matching and Analysis Layer: Privacy-preserving computation (e.g., differential privacy, homomorphic encryption) identifies common patterns and insights.
- Result Extraction: Only aggregated, non-identifiable insights are accessible to the parties involved.
Eklavya's Privacy-First Data Collaboration Model
Eklavya assists organisations in moving away from ad hoc data sharing toward structured privacy ecosystems. As a result, Eklavya helps you to:
- Design research frameworks based on clean rooms for smooth multi-party collaboration.
- Integrate workflows for anonymisation and managing customer consent.
- Build compliant dashboards that achieve a good balance between in-depth insight and privacy guarantees.
- Support training your models on aggregated behavioural data for powerful predictive and sentiment analysis.
Industry Applications
Organisations across many sectors are already benefiting from this approach:
- Retail and Consumer Packaged Goods (CPG): Cross-analyse loyalty data with e-commerce partners to pinpoint high-value shopper segments that overlap between the two.
- Healthcare: Combine anonymised patient experience data from multiple providers to drive service improvement studies.
- Finance: Jointly model customer churn and risk profiles with affiliate partners, without ever exposing the customer’s PII.
Eklavya's Privacy Advantage
Eklavya embeds privacy-by-design analytics into all its solutions. Consequently, every data-driven insight you gain adheres to the highest global compliance standards.
The result is smarter, faster, and fully compliant insights that truly power responsible growth.
Want to future-proof your data collaboration? Eklavya offers privacy audit and clean-room setup consulting to help you monetise insights without compromising compliance.
