Siloed Data

Tensor9 for Siloed Data Scenarios

Enterprise customers often operate with data scattered across multiple systems, departments, or geographies, creating "data silos" that prevent seamless access and analysis. These silos can result from organizational constraints, legacy systems, regulatory requirements, or technical limitations, making it difficult to centralize data for insights, machine learning, or automation.

Tensor9’s Any-Prem platform enables software vendors to bring their applications directly to siloed data—no matter where it resides—by running the software in the customer's environment rather than requiring data consolidation in the vendor's cloud.

Key Challenges with Siloed Data

1. Data Fragmentation Across Systems
   Different teams and departments may store data in incompatible formats or separate databases, preventing easy integration and analysis.

2. Geographically Distributed Data
   Enterprise branches or data centers across multiple regions may store data locally due to latency, security, or compliance concerns.

3. Regulatory Restrictions on Data Movement
   Sensitive datasets (e.g., personally identifiable information or proprietary information) are often subject to data residency or privacy regulations that prevent their transfer across systems or jurisdictions.

4. Legacy and On-Prem Systems
   Many enterprises still use legacy systems or on-prem databases that are incompatible with modern cloud-based solutions, making integration cumbersome.

How Tensor9 Helps

1. Localized Data Access
   Tensor9 allows software vendors to deliver their applications directly into customer environments where the siloed data resides (e.g., on-prem systems, private cloud instances). By running software next to the data, Tensor9 avoids the need for costly and slow data transfers to a central location.

2. Multi-Environment Compatibility
   Tensor9 supports hybrid environments, allowing vendors to deliver their software across cloud, on-prem, and air-gapped networks. This ensures access to siloed data wherever it lives, without requiring changes to the customer’s infrastructure.

3. Data Residency and Security Compliance
   Tensor9 ensures that sensitive datasets remain within the boundaries of the customer’s environment, making it easier to comply with regulations such as GDPR, HIPAA, and national data sovereignty laws.

4. Modern Integration with Legacy Systems
   Tensor9 integrates with legacy systems and databases, enabling vendors to bridge the gap between siloed data in older infrastructure and modern AI/ML applications.

Example Scenario: Global Bank Data Consolidation

Consider a multinational bank with customer transaction data spread across multiple regional offices, each using separate local databases. The bank needs to perform fraud detection and customer analytics across all its data but cannot move the data to a central cloud location due to privacy regulations and security policies.

With Tensor9, the bank’s AI vendor can deploy its software within each regional office’s infrastructure, performing analytics locally while maintaining compliance and security. The results can then be aggregated without moving raw sensitive data.

Benefits

Example Scenarios

1. Cross-Department Analytics
   An enterprise AI vendor uses Tensor9 to deliver analytics software that can pull insights from multiple internal databases in different departments without requiring a data migration.

2. Healthcare Data Integration
   A healthcare provider with multiple clinics uses Tensor9 to deploy patient management software locally in each clinic, enabling data synchronization without exposing sensitive medical records.

3. Global Supply Chain Monitoring
   A logistics company deploys Tensor9 to access inventory data from multiple warehouses and factories across different countries, maintaining local compliance while providing a global operational view.

Summary

Tensor9 empowers vendors to overcome the challenges of siloed data by enabling local software deployment within customer environments. By bringing the code to the data instead of the data to the cloud, Tensor9 simplifies data integration, improves security, and ensures compliance across distributed and legacy systems.