What is data fabric?
A data fabric is an architectural approach that unifies data across disparate systems—integrating, governing, and making data instantly accessible for real-time operations and analytics. It provides a consistent, connected, and intelligent data foundation. This foundation allows organizations to seamlessly collect, enrich, and act on data across multiple sources, vendors, and domains.
In the context of telecommunications, a data fabric connects siloed operations support systems / business support systems (OSS/BSS), enabling federated access to network, service, and customer data. These data are a critical prerequisite for AI, automation, and autonomous network operations.
Unified, trusted data foundation: Consolidates fragmented OSS data into a single, federated source of truth, enabling accurate visibility across physical, virtual, and cloud assets.
Real-time intelligence and automation: Provides live data streams that fuel AI, analytics, and closed-loop automation—turning static data into actionable intelligence for proactive operations.
Accelerated transformation and reduced OPEX: Simplifies integration across legacy and cloud systems through open APIs and standards, reducing operational complexity and integration costs while increasing agility.
Data fabric versus data mesh: What’s the difference?
While both concepts focus on modern data management, they serve distinct but complementary purposes.
| Data fabric | Data mesh |
Primary focus | Technology architecture and integration | Organizational model and data ownership |
Goal | Provide a seamless layer for discovery, access, and governance of data across systems | Decentralize data responsibility to domain teams while enforcing global standards |
Origin | Evolved from enterprise data integration and metadata management | Evolved from DevOps and domain-driven design principles |
Control model | Centralized policy and orchestration | Federated governance with domain autonomy |
Data flow | Automated data movement and virtualization | Peer-to-peer data sharing across data products |
Core concept | Intelligent connectivity layer across sources | Domain data ownership and data as a product |
Primary consumers | Data engineers, analysts, and AI platforms | Domain teams exposing/consuming governed data products |
Key enabler | Metadata management, automation, and semantic modeling | Product thinking, self-service data platform, and a federated governance council |
In essence: the data fabric is the connective tissue, while the data mesh defines who manages each part of it. Blue Planet participates in both—providing the governed OSS data fabric that powers a broader, cross-domain data mesh for the entire telco ecosystem.
Challenges solved by data fabric
A modern data fabric tackles the overwhelming complexity and fragmentation across OSS/BSS environments, clearing the path for service providers to transform operations and unlock new business models.
- Siloed and inconsistent OSS data limits automation and AI adoption
- Incomplete or outdated inventory data result in manual, error-prone processes
- Slow integration between OSS and BSS systems constrains agility and innovation
- Limited observability across domains hinders real-time service assurance
- Custom integration and data reconciliation creates high operational costs
Use cases for data fabric
AI-driven network planning: Harness unified, reconciled inventory to design optimized 5G, transport, or SD-WAN networks in hours instead of weeks.
Closed-loop service assurance: Correlate real-time telemetry with inventory and topology data to automatically detect and resolve issues.
Automated service orchestration: Enable intent-based provisioning by providing orchestrators with live, accurate resource data.
Cross-domain analytics: Combine OSS, IT, and customer data for predictive insights into performance, demand, and SLA compliance.
Data mesh enablement: Deliver curated, governed network data to BSS—such as billing, customer relationship management (CRM), and order management—through open, standards-based APIs.
How to build a data fabric
Creating an effective data fabric involves several key steps.
- Consolidate data sources
Integrate data from legacy OSS, network management systems, and cloud platforms. - Establish a common data model
Normalize and federate multi-vendor, multi-domain data into a unified schema. - Ensure data quality and reconciliation
Continually validate and correct discrepancies between planned and live network views. - Enable AI and automation
Expose trusted data to AI models, orchestration, and assurance systems for closed-loop operations. - Adopt open standards
Use TM Forum APIs, YANG models, and industry protocols to ensure interoperability and scalability.
How Blue Planet helps you create a data fabric
A data fabric is not just about data integration—it’s about empowering intelligence and automation through visibility and trust. With Blue Planet, you gain the unified data foundation, AI readiness, and open architecture needed to move from manual operations to autonomous, agentic networks.
- Blue Planet Inventory: The heart of the OSS data fabric
- Blue Planet Inventory federates and reconciles data across physical, virtual, and cloud domains
- Gain a single source of truth for network and service assets, and power AI, planning, and automation with accurate, trusted data
- Blue Planet Unified Assurance and Analytics: Build on the data fabric with real-time visibility and AI-driven insights
- Blue Planet Unified Assurance and Analytics correlates multi-domain telemetry with inventory data for unified assurance
- Harness predictive analytics and closed-loop automation
- Blue Planet Orchestration: Automate service design, fulfillment, and activation using one federated data model
- Blue Planet Orchestration integrates with Blue Planet Inventory and Unified Assurance and Analytics for intent-based orchestration across domains
- Blue Planet AI Studio: Extend the data fabric into an agentic AI framework, where intelligent agents reason, coordinate, and act on unified OSS data
- With Blue Planet AI Studio, build and manage AI agents that use federated data to achieve autonomous operations