Introduction
The data paradox
It’s fragmented across tools, duplicated across environments, and disconnected across teams — making it harder to connect the dots when something breaks.
More data should create more clarity. Instead, it often creates more friction.
Teams spend time switching between tools, reconciling inconsistent data, and searching for the context they need to understand what’s happening.
At the same time, expectations for ITOps are changing. Teams are being asked to support AI-driven operations — all while managing increasing complexity.
This is a practical, evaluation-focused guide designed to help you cut through the noise and focus on what actually matters when choosing a modern data architecture.
We’ll walk through what to look for — and how those capabilities translate into real operational impact.
Cisco Data Fabric resonates with Autodesk's approach — separating operational and analytical data, improving data quality, optimizing for cost, and leveraging large-scale internal data for AI.
Criterion #1: Unified data across domains
Criterion #2: Real-time, in-place analysis
Criterion #3: AI-ready, high-fidelity data
Criterion #4: Cost control at scale
Criterion #5: Integration and ecosystem compatibility
Criterion #6: Scalability and future-proofing your ITOps platform
Additional resources and next steps