In today’s data-driven economy, leading organizations recognize that securely sharing and analyzing data with partners can unlock tremendous value. Gartner research indicates that companies which share data externally realize three times greater economic benefit than those that do not. It’s no surprise, then, that enterprise adoption of “data clean rooms” and other shared analytics platforms is on the rise. IDC predicted that by 2024, 65% of Global 2000 enterprises would form data-sharing partnerships via data clean rooms. Yet, despite this enthusiasm, many firms still struggle to fully capitalize on collaborative data analytics. A 2024 Forrester study found that while 93% of data leaders agree improved data collaboration is critical for revenue growth, few organizations can actually leverage it today because they cannot unify data across sources while preserving privacy and quality. In other words, data silos and privacy concerns are preventing organizations from realizing the full potential of multi-party data analytics.

In this white paper, we provide an in-depth analysis of OPAQUE’s advantages over conventional Data Clean Rooms, drawing on independent research from Gartner, Forrester, IDC, McKinsey, BCG, and others. We examine four key dimensions of differentiation and illustrate them with real-world use cases in financial services, insurance, and high-tech industries.

1. Security Model & Trust Boundaries – Hardware-rooted trust (OPAQUE) vs. software-based controls (DCR).

2. AI & Analytics Capabilities – Full machine learning on encrypted data vs. limited aggregate queries.

3. Scalability & Performance – Distributed confidential computing vs. centralized clean room architectures.

4. Regulatory Compliance & Enterprise Integration – Encryption-in-use with strong assurances vs. policy-based privacy management.