Why Automation and AIOps Need a New Data Management Architecture

Automation and AIOps play a pivotal role in modern IT infrastructure by enhancing efficiency, accuracy, and speed, but they face challenges, particularly in managing data across hybrid IT environments. AIOps can suffer from trust issues when AI makes decisions based on outdated or incomplete data, highlighting that the problem lies more with data management than with AI itself. Many organizations prioritize execution over data governance, leading to fragile automation systems and unpredictable AI behavior. Infrastructure intent data, akin to application source code, requires rigorous management practices such as object inheritance, idempotency, and comprehensive version control, yet these are often neglected. Weak data management practices contribute to technical debt, with some enterprises spending over 70% of their time on maintenance. To address this, infrastructure intent data should be treated as a strategic enterprise dataset, managed as a knowledge graph to capture the complexity of hybrid infrastructures. This approach, coupled with robust data governance including validation pipelines and provenance tracking, can transform intent data into a reliable control plane, enabling scalable and trustworthy automation and AIOps solutions.