How Can Synthetic Data Exchanges Become the Infrastructure for Collaboration in High-Risk Data Environments?

In 2024, enterprises globally spent an estimated US$150 billion on advanced analytics and AI initiatives. Yet, industry benchmarks show that more than two-thirds of high-value use cases fail to scale due to data access and sharing constraints. The constraint is the inability to collaborate across institutional, regulatory, and competitive boundaries without incurring unacceptable risks to privacy, legality, and reputation.  

Synthetic data exchanges are emerging as a structural response to this failure. They are not incremental privacy tools, but a new form of market infrastructure designed to unlock collaboration where direct data sharing is no longer viable. 

The Structural Failure of Traditional Data Collaboration Models 

Enterprise data collaboration today is governed by mechanisms that were not designed for modern analytics. Bilateral data sharing agreements, anonymisation techniques, and data trusts impose high transaction costs and long lead times. Each new collaboration requires a bespoke legal review, as well as privacy impact assessments and audit processes that can span several months. In regulated sectors, the fragmentation of privacy regimes across jurisdictions compounds this friction. 

Even when sharing is permitted, utility degradation remains a persistent issue. Traditional anonymisation methods often remove or generalise precisely the features required for advanced modelling, particularly in fraud detection, clinical analytics, and network optimisation. The result is a paradox. Data can be shared safely or be useful, but rarely both at scale. This structural limitation has become a binding constraint on cross-organisation innovation. 

Synthetic Data Exchanges as a Market Infrastructure Layer 

Synthetic data exchanges represent a shift from transactional data sharing to platform-mediated collaboration. At their core, they decouple analytical utility from raw data custody. Instead of moving sensitive datasets across organisational boundaries, exchanges allow data owners to generate high-fidelity synthetic replicas and distribute them under standardised governance controls. 

This model realigns incentives. Data owners retain control and reduce exposure, while data consumers gain access to statistically representative datasets suitable for modelling, testing, and benchmarking. Importantly, exchanges introduce repeatability and scale. Once governance, validation, and access controls are established, additional collaborations can be enabled with marginal effort rather than restarting the entire compliance process. 

Architecture of a Synthetic Data Exchange 

Three tightly integrated layers define a production-grade synthetic data exchange. 

Generation and Fidelity Engineering 

The generation layer relies on advanced generative modelling techniques, including generative adversarial networks, variational autoencoders, and hybrid probabilistic approaches. Vendors such as Mostly AI, headquartered in Austria, and Hazy, based in the United Kingdom, have built platforms optimised for enterprise-grade tabular and relational data. Their systems focus on preserving multivariate correlations, temporal dynamics, and rare-event behaviour, which are critical for financial crime, clinical risk, and network analytics. The objective is not visual similarity, but task-level equivalence under defined analytical workloads. 

Privacy Assurance and Risk Quantification 

For expert users, privacy claims must be measurable. Leading exchanges embed quantitative privacy metrics, including differential privacy parameters and resistance to membership inference attacks. These metrics allow risk teams to assess exposure in objective terms rather than relying on qualitative assurances. In several large enterprises, synthetic datasets are now reviewed through the same risk committees that oversee model governance and data access, signalling institutional maturity. 

Access, Governance, and Commercial Controls 

The exchange layer governs who can access which datasets, under what conditions, and for what purposes. Role-based access control, licensing constraints, and audit trails are standard. Advanced platforms integrate directly with enterprise data catalogues and MLOps pipelines, allowing synthetic datasets to be discovered and consumed using existing workflows. This integration reduces friction and accelerates adoption across analytics teams. 

Where Synthetic Data Exchanges Are Operating at Scale 

The value of synthetic data exchanges is already evident across multiple risk environments. 

In regulated healthcare ecosystems, large European hospital networks and pharmaceutical companies have already operationalised synthetic data exchanges for multicenter analytics on electronic health records. NHS England, working through NHS Digital, has partnered with Syntegra, the synthetic data business spun out of Novartis, to generate synthetic replicas of national-scale EHR datasets for external research and AI development. Patient-level data remains within NHS-controlled environments, while synthetic datasets are shared centrally with approved academic and commercial partners, enabling collaborative model development without transferring identifiable health records and while remaining compliant with GDPR requirements.

Independent evaluations showed that predictive models trained on synthetic data retained more than 90% of the performance of models trained on real data for key outcome prediction tasks. 

In financial services, ING Group, the Netherlands-headquartered multinational bank, has deployed synthetic data in collaboration with Mostly AI to support fraud detection and financial crime analytics. Regulatory constraints prevented sharing raw transaction data with vendors and distributed teams. Synthetic transaction datasets preserved behavioural and temporal patterns relevant for detection models, enabling external benchmarking and model development without exposing live customer data. ING has publicly stated that this approach reduced vendor onboarding friction while maintaining GDPR and internal risk compliance.

In telecommunications, Telefónica, through Telefónica Tech, has used synthetic data to support collaboration on network analytics and 5G optimisation. Synthetic replicas of call detail records and network telemetry captured traffic and congestion dynamics while eliminating subscriber identifiers. These datasets enabled technology partners to test optimisation algorithms end-to-end without access to real customer data, supporting external collaboration under telecom data protection requirements.

Economic and Strategic Implications for Enterprises 

Beyond compliance, synthetic data exchanges deliver measurable strategic benefits. Enterprises report reductions in model development cycle times, lower legal and audit overhead, and expanded partner ecosystems. Internally, synthetic exchanges enable broader data democratisation by allowing teams to experiment without accessing sensitive production data. Externally, they create a controlled mechanism to engage vendors, regulators, and research partners without compromising proprietary assets. 

Critical Limitations and Decision Filters 

Synthetic data is not a universal substitute for real data. Certain edge cases, particularly those driven by infrequent events or highly complex causal relationships, may still require access to real data. Poorly defined fidelity metrics or insufficient domain expertise can result in synthetic datasets that appear accurate but fail to perform effectively under real-world conditions. Enterprises must also invest in governance capabilities to manage privacy budgets, validate processes, and oversee lifecycle management. 

Forward Trajectory and Standardisation Pressure 

Industry consortia and standards bodies are increasingly focused on defining common metrics and governance frameworks for synthetic data. Regulatory dialogue is also evolving, with early indications that synthetic data may be formally recognised as a risk-reducing mechanism under specific conditions. Advances in generative modelling are extending synthetic approaches into unstructured domains, including medical imaging and network logs, further expanding the addressable use cases. 

Conclusion: From Data Protection to Data Leverage 

Synthetic data exchanges mark a transition from defensive data protection to proactive data leverage. By providing a scalable, governed mechanism for collaboration on sensitive use cases, they remove one of the most persistent barriers to enterprise analytics and AI adoption.  

For organisations seeking to accelerate innovation while maintaining regulatory and reputational discipline, synthetic data exchanges are no longer an experimental concept. They are becoming foundational infrastructure. The strategic risk now lies not in adoption, but in delay.