Own Data
Own Data is a data infrastructure and privacy platform that enables enterprises to run analytics and Artificial Intelligence (AI) on their own data within their own environments rather than in third-party clouds.
- Privacy-preserving data infrastructure for running analytics and AI in the customer’s own environment (data privacy / data infrastructure)
- Tooling for connecting to enterprise data sources without centralizing or extracting raw data (data virtualization / secure data access)
- Support for deploying analytics and AI workloads close to sensitive datasets, including on-premises (on-prem) and private cloud (AI infrastructure / data security)
- Controls to enforce data governance, access policies, and regulatory compliance around analytical and AI use cases (data governance / compliance)
- Enterprise-focused architecture for retaining data ownership while enabling collaboration with models, applications, and partners (data collaboration / secure AI)
More About Own Data
Own Data focuses on enterprises and institutions that need to apply analytics and AI to confidential, regulated, or high-sensitivity datasets while retaining full control over where data resides. Its platform is designed so that data remains inside the customer’s own infrastructure perimeter, whether that is an on-prem data center, a private cloud deployment, or a tightly controlled Virtual Private Cloud (VPC) in a public cloud provider. Instead of centralizing data in a vendor-managed environment, Own Data supports architectures in which computation travels to the data, aligning with data residency, sovereignty, and internal security requirements.
The core approach aligns with concepts such as data virtualization and secure data access, in which analytic and AI workloads are executed against connected data sources without bulk replication of raw datasets. This architecture is relevant for enterprises that operate relational databases, data warehouses, data lakes, or operational systems that cannot be exported to external environments due to contractual or regulatory constraints. Own Data’s platform is positioned for use cases in sectors such as financial services, healthcare, and public sector, where regulatory compliance and internal security policies often require that data stay within controlled infrastructure.
From a technology perspective, Own Data typically integrates with existing enterprise identity and access management, uses policy-based access control for query and model execution, and supports deployment models that fit into Kubernetes, Virtual Machine (VM), or container-based environments. By enabling analytics and AI workloads to run close to the data, the platform can support scenarios such as model evaluation on first-party data, internal analytics on customer records, or collaborative projects with external partners where sharing raw data is not permitted but controlled query or model access is acceptable.
In the broader enterprise IT landscape, Own Data maps to solution categories including data privacy platforms, secure AI infrastructure, and data governance tooling. It provides a runtime and control layer rather than replacing core systems of record like data warehouses or transactional databases. Organizations can position Own Data alongside existing data catalogs, observability tools, and security platforms as part of a wider data and AI architecture. For directories and marketplaces, Own Data can be categorized under data privacy and security (governance and policy enforcement), AI infrastructure (privacy-preserving model execution), and data access and virtualization (querying distributed data sources without centralization).