Designing Scalable Data Collection Frameworks for Enterprise Network Automation and AI-driven intelligence

Introduction: Data as the Foundation of Intelligent Networks 

Enterprise network automation has evolved from scripted command execution to policy validation engines, compliance pipelines, and increasingly, AI-assisted operations. Yet as environments scale, many organizations discover that automation maturity is not limited by tools — it is limited by data architecture. In the era of AI-driven intelligence, automation is only as strong as the data ecosystem behind it. 

Modern enterprise networks generate vast volumes of configuration states, telemetry metrics, logs, and operational events. Collecting this information is straightforward. Structuring, optimizing, governing, and preparing it for analytics and AI consumption is the real engineering challenge. Scalable automation begins with disciplined data design. 

The Scaling Problem: When Data Becomes Technical Debt 

At small scale, retrieving device outputs and storing them for compliance or troubleshooting works well. As enterprises grow to thousands of devices, predictable challenges emerge: 

• Vendor-specific CLI inconsistencies 
• Software version changes altering output formats ...