Joint data analysis. Without personal data.
DataCleanLab is the multi-party data analysis platform of Harvey's Consultancy Services, enabling privacy-preserving record linkage. DataCleanLab enables secure, joint analysis of sensitive data from multiple sources. Identifiers are anonymized at the data owner, and only protected records ever reach the platform. With a project-based approach, multiple data collaborations can run simultaneously.
Secure, joint analysis of sensitive data from multiple sources
The best business decisions often require more than one organization's data. Joint analysis with partners and industry players can significantly increase business value. Until now, however, this required participants to share sensitive data, creating privacy, legal, and trust risks.
DataCleanLab resolves this dilemma. Cryptographic methods guarantee that personal identifiers remain with the data owner organization: neither the operator nor other participants can access raw data. Three deployment options let us adapt to your regulatory environment.
Data stays with each organization, results become shared
DataCleanLab's key capability is matching and jointly analyzing anonymous data from multiple sources, while original identifiers remain with the data owners at all times.
Data preparation at the data owner
Anonymization of identifiers and encryption of attributes takes place in the data owners' own environment. Thanks to the cryptographic methods used, identifying data cannot be reversed.
Loading into DataCleanLab
The platform receives protected data needed for joint processing. Data owners define which attributes to share and at what protection level.
Matching and analysis
Anonymous data from multiple sources can be matched, enabling joint analysis.
Business outcome
Joint data analysis provides more accurate and well-founded information, supporting better decisions.
Three approaches, same guarantee
All three options guarantee that identifiers stay in the source system. They differ in analytics capability and deployment model.
Delivers value where trust meets regulation
The stricter the regulation and the more sensitive the data, the greater the competitive advantage that secure collaboration creates.
Banking and finance
Inter-institutional fraud detection, joint anti-money laundering monitoring, and credit risk models without exposing customer data.
Healthcare and research
Joint analysis among hospitals, clinics, and universities while preserving patient data protection.
Energy and utilities
Predictive maintenance between operators, shared consumption analytics, and network optimization.
Telecommunications
Customer churn prediction across multiple providers and joint market research while protecting customer data.
Let's discuss which option fits your organization
From secure collection to encrypted analytics: the deployment model adapts to your regulatory and business environment. Let's explore the possibilities together.
The people behind DataCleanLab
Csilla Bábás
Csilla integrates the development team, contributing to strategy, built on extensive enterprise experience in solving complex data problems.
András Pátkai
András develops strategic partnerships and finds compelling use cases with public and private clients worldwide.
Zoltán Berta
Beyond day-to-day operations, Zoltán oversees the entire business and leverages many years of testing experience to support project teams.
Dr. Nóra Ságodi
Nóra ensures the company's legal compliance, supports strategic decision-making and handles contractual and regulatory matters.
