Build ServiceNow Data Fabric to create a governed, AI-ready data ecosystem. AI needs good data like engines need clean fuel. Yet for most organizations, data is scattered, inconsistent, and untrusted—making it nearly impossible to power accurate, explainable AI.

Will Johnson is the mind behind ServiceNow Data Platform Evolution that scales for AI enterprise evolution. ServiceNow’s RaptorDB is the solution to online transaction processes, and the next-gen database that is scaled for world class transactional processes with speed, scale and analytics.

That’s where a data fabric comes in.

A data fabric is an intelligent, connected architecture that unifies data across systems—automating discovery, governance, and quality. It ensures the right people have access to the right data, in the right format, at the right time. This seamless flow of clean, governed data is what gives AI the foundation to learn, adapt, and make better decisions.

🔍 Why does it matter?

Over 70% of business leaders say poor data quality is obstructing scalable effective decision-making. According to Gartner, 82% of companies pursuing enterprise AI initiatives cite data fabric as a critical capability. Without it, data remains siloed, incomplete, and unfit for real-time, explainable machine learning models.

By building a ServiceNow Data Fabric, organizations solve these issues, creating a reliable, scalable foundation for AI-driven insights—connecting Performance Analytics, CMDB health, governance workflows, and real-time dashboards in one ecosystem.

This guide walks you through how to do it—step-by-step—using ServiceNow as your platform for transformation.

But what is a data fabric?

Gartner introduces Data Fabric Uses, Definition & Trends. Data fabric is an emerging data management and data integration design concept of seamlessness connection of data from dispersed sources.

A data fabric is an intelligent, unified architecture that seamlessly connects data across sources, environments, and formats. It automates data discovery, governance, quality enforcement, and accessibility—making it an essential enabler of trusted AI and analytics.

This guide shows how to build a ServiceNow-powered data fabric, with Performance Analytics, CMDB, and standardized business processes—delivering visibility, reliability, and actionable insights across the data lifecycle.

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🔷 1. Scoped Application Development

Scoped applications streamline agile buildouts for targeted business use cases.

• Use Case: Define pain points the data fabric solves (e.g., asset lifecycle tracking).
• MVP: Launch core features fast, then iterate.
• Agile Dev: Enable rapid delivery with sprints and feedback loops.

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🔷 2. Data Model Definition

Laura Brandenburg offers a great getting started with Data Modeling Tutorial for Familiarity for Business.

Define your data schema, entities, and relationships to ensure consistency.

• Canonical Model: Normalize across systems.
• Data Dictionary: Create clarity with definitions and formats.
• Lineage: Document where each data element originates and how it transforms.

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🔷 3. Ingestion & Transformation

We love the content by Alex the Analyst and Analyst Builder

To unlock the full power of AI and analytics, organizations must ensure data flows efficiently from source to insight. That journey begins with a well-designed data pipeline—and ServiceNow offers the flexibility to make it happen. Here is the approach to bring your raw and refined data into alignment.

• ETL/ELT Pipelines: Use native integrations or API-based flows.
• Data Lakes & Warehouses: Store structured and unstructured data.
• Transformation Scripts: Apply ServiceNow Business Rules or PA scripts to refine data.

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🔷 4. Data Governance & Controls

Set the guardrails to protect, monitor, and manage data assets.

• Owners & Stewards: Assign responsibility.
• ACLs & Encryption: Enforce data access controls.
• Audit Trails: Enable compliance monitoring.

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🔷 5. Data Quality Management

Eye on Tech provides an introduction to Data Quality and Why it is important.

Embed quality at every stage of the lifecycle.

Dimension	Reporting Example	Performance Analytics Example
Completeness	Highlight missing fields in reports	Track % of complete records over time
Accuracy	Audit asset locations	Trend accuracy metrics monthly
Consistency	Standardize date formats	Compare resolution times by method
Timeliness	Real-time P1 reports	Delay trend monitoring
Validity	Flag invalid impact/urgency pairs	KPI for invalid CIs monthly

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🔷 6. Performance Analytics Integration

Make data actionable with KPIs, dashboards, and scorecards.

• KPI Composer: Map business goals to indicators.
• Dashboards: Use widgets and interactive filters.
• Breakdowns: Enable segmentation by assignment group, priority, etc.
• Scorecards: Drive leadership alignment.

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🔷 7. Build Embedded Analytics

Use the virtualization layer to embed analytics inside workflows:

• Widgets & Scorecards
• Dashboards
• Role-Based Workspaces
• Natural Language Queries
• Predictive Insights

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🔷 8. Organize for Success

Ensure sustainable growth with best practices:

Best Practice	Why It Matters
Prod vs. Dev Environments	Maintain integrity across lifecycle
Naming Conventions	Improve discoverability and reuse
Training & Governance Roles	Build a culture of analytics excellence
Continual Service Improvement	Iterative enhancement via CSI and KPI Reviews

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Conclusion: A New Standard for Data

By combining data governance, quality, and analytics in a unified ServiceNow Data Fabric, organizations can deliver proactive insights and measurable value. When built with clear structure and ongoing collaboration between business and IT, this model becomes a catalyst for transformation and trusted decision-making.

Other Build ServiceNow Data Fabric Resources

• 7 Best Practices for Superior Data Quality Management
• AI-Powered Xanadu & RaptorDB
• AI: ServiceNow Virtual-Agent Chatbot
• Artificial Intelligence A-Z Glossary
• Back End Data Advances with Raptor DB
• Conceptual RaptorDB Functions
• Data Management (Eye on Tech)- YouTube
• FAQs RaptorDB
• Forrester on Data Fabric
• Gartner report on data fabric and data mesh
• Introduction to Microsoft Fabric | Data Analytics for the Era of AI
• MariaDB
• MariaDB Quick Guide
• Platform: Servicenow RaptorDB Features
• Predictive Intelligence (servicenow.com)
• RaptorDB – ServiceNow
• Raptor Workflow Data Platform
• ServiceNow RaptorDB
• Virtual Agent – Tips for Implementation success – ServiceNow Community
• What you need to know to jumpstart Virtual Agent