Workflow Data Fabric & Workflow Data Network: Connecting Your Silos in ServiceNow

Vaishnavi Lathk · Thursday

In today’s enterprise environments, data remains one of the biggest barriers to digital transformation. While organizations invest heavily in automation, AI, and advanced analytics, their efforts are often limited by fragmented data scattered across multiple systems and departments. According to the trend article “Top 7 Tech Trends for ServiceNow Users in 2025,” the need for connected, high-quality data is one of the most critical success factors for ServiceNow users.

Two powerful solutions from ServiceNow address this challenge: Workflow Data Fabric and Workflow Data Network. Together, they enable organizations to connect silos, standardize data, and empower intelligent workflows across the enterprise.

This article provides a comprehensive overview of both concepts, practical guidance for implementation, and best practices for leveraging them to their full potential.

What is Workflow Data Fabric?

Workflow Data Fabric is ServiceNow’s framework for connecting data across multiple internal systems and workflows. It provides a centralized, unified layer of data that supports enterprise-wide automation, reporting, and AI initiatives.

Key features of Workflow Data Fabric include:

Unified Data Access: Access data from ERP, ITSM, HR, CRM, and other systems without manual extraction or duplication.
Data Standardization & Normalization: Transform and normalize data from different sources to create a single source of truth.
Real-Time Integration: Feed accurate, up-to-date data into workflows, automation, and AI models.
Governance & Compliance: Track data lineage, ensure accuracy, and maintain audit-ready records for compliance purposes.
Automation Enablement: Provide reliable data inputs for workflow orchestration, AI reasoning, and decision-making processes.

In essence, the Data Fabric acts as a digital “weaving layer,” connecting disparate enterprise data sources to power smarter, faster, and more consistent operations.

What is a workflow data network?

While the Data Fabric focuses on internal data integration, the Workflow Data Network expands these capabilities to external systems, partners, and distributed environments. It enables organizations to:

Connect multiple ServiceNow instances and external systems seamlessly.
Share and consume data securely across organizational boundaries.
Maintain data governance and enforce security, privacy, and compliance rules.
Enable collaborative workflows across departments, regions, or partner ecosystems.
Scale AI and automation initiatives beyond internal silos, creating an enterprise-wide data mesh.

The Workflow Data Network effectively acts as a “networked fabric of data across enterprises,” breaking down traditional barriers that limit workflow intelligence, AI adoption, and operational efficiency.

Why These Capabilities Matter

1. Eliminate Data Silos

Disconnected systems create inefficiencies, duplicate work, and incomplete insights. Workflow Data Fabric and Network connect systems and data points, ensuring all teams operate from a single, reliable source of truth.

2. Enable Smarter Workflows

Accurate, real-time data allows workflows to operate autonomously and intelligently, improving speed, quality, and consistency.

3. Drive Compliance & Governance

With growing regulatory pressures, organizations need audit trails, data lineage, and governance controls. The Fabric and Network ensure policies are applied consistently across all data and workflows.

4. Unlock AI & Analytics Potential

AI models require high-quality, consolidated data to deliver actionable insights. By feeding AI with normalized, trusted data, organizations can improve predictions, automate complex tasks, and generate measurable ROI.

5. Scale Enterprise Collaboration

By enabling data sharing across departments and external partners, the network fosters collaborative processes, improving responsiveness and operational efficiency.

Step-by-Step Guide to Implementing Workflow Data Fabric & Network

Step 1: Assess Your Data Landscape

Identify all systems containing critical data: ERP, ITSM, HR, CRM, finance, and analytics platforms.
Map existing workflows that depend on data from multiple sources.
Prioritize high-value data for initial integration.

Step 2: Configure Workflow Data Fabric

Use IntegrationHub to connect systems to the Data Fabric.
Define data normalization rules, standardizing formats, units, and naming conventions.
Enable real-time data flows for workflows that require immediate updates.
Ensure data governance policies are applied at the fabric level.

Step 3: Extend Integration via Workflow Data Network

Identify external systems or partners requiring secure access to your data.
Configure API endpoints and connectors to integrate distributed data sources.
Apply security policies and role-based access to ensure compliance and privacy.
Enable cross-system workflows, allowing automation to act on shared data.

Step 4: Feed Data Into Workflows and AI

Connect workflows to Fabric/Network sources, enabling intelligent automation.
Provide AI models with clean, normalized, and contextual data for better insights.
Implement data validation checks to ensure workflow integrity.

Step 5: Monitor, Audit, and Archive

Continuously monitor data quality, workflow efficiency, and system performance.
Archive historical or low-value data to optimize storage and compliance.
Maintain audit-ready documentation of all data connections, transformations, and access events.
Periodically review and refine integrations to remove redundancies and improve efficiency.

Best Practices for Data Integration & Archiving

Prioritize Critical Data – Focus on data that powers high-value workflows and business decisions.
Standardize Early—Define common formats, naming conventions, and data structures before scaling.
Enforce Governance Policies—Include security, compliance, privacy, and quality rules across all systems.
Automate Monitoring—Use dashboards, alerts, and AI-driven checks to maintain data quality.
Archive Strategically—Retire historical or redundant data while ensuring compliance and accessibility.
Document Everything—Maintain clear maps of integrations, dependencies, and transformations.
Iterate Continuously—Treat data integration as an evolving process to accommodate new systems and requirements.

Practical Example: Connecting HR, IT, and Finance Workflows

Consider an organization using separate systems for HR onboarding, IT provisioning, and finance approval:

Without Data Fabric/Network: HR enters employee info manually in each system, IT may provision accounts late, and finance may approve resources slowly.
With Data Fabric: Employee information from HR automatically flows into IT and finance systems, ensuring accurate, timely, and consistent onboarding.
With Data Network: If the organization uses a partner payroll system externally, the network allows secure sharing of employee data for payroll processing while maintaining compliance and traceability.

This combination reduces manual effort, speeds workflows, and ensures compliance, demonstrating the tangible benefits of connecting data silos.

Business Value of Workflow Data Fabric & Network

Benefit	Impact
Unified Data Access	Eliminate silos and provide a single source of truth.
Workflow Efficiency	Automate processes with reliable, real-time data.
Compliance & Governance	Maintain audit-ready data and enforce policies across systems.
AI & Analytics Enablement	Feed high-quality, normalized data to AI and reporting workflows.
Enterprise Collaboration	Share data across departments and external partners securely.
Scalable Architecture	Extend data connectivity as enterprise systems and partnerships grow.

Conclusion

Workflow Data Fabric and Workflow Data Network are critical enablers for any enterprise looking to connect silos, standardize data, and unlock the full potential of workflows and AI on the Now Platform.

By combining internal data integration (Fabric) with distributed data collaboration (Network), organizations can achieve:

Enterprise-wide visibility and governance
Smarter, automated workflows
Reliable AI and analytics insights
Improved operational efficiency and ROI
Scalable, secure collaboration across departments and partners

Implementing these capabilities is not just a technical exercise—it’s a strategic imperative for organizations striving to become data-driven, agile, and future-ready.

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