Smarter AI for Critical Operations: Why Data Matters
Having access to both deep and broad data lets you build a clear, 360-degree view of incidents and the wider technology environment. Without this, AI systems risk making flawed or biased decisions.
Garbage in, garbage out isn’t just a saying—it’s a reality for AI-powered tools. If the input data is inaccurate or incomplete, the AI’s conclusions will be unreliable. Similarly, limited data diversity causes AI to develop biases, as it fails to see the full spectrum of scenarios. This problem is especially critical in incident management.
Effectively collecting incident data lets you connect the dots between different factors and capture the subtle details behind incidents. That’s when AI moves from a simple tool to a valuable asset.
A 360-Degree View of Incidents
Several elements influence how an incident unfolds and how you should respond. For example:
- Source integration: Reveals which system or component is affected.
- Environment: Indicates the urgency—whether the incident is in a test or production setting.
- Timing: Helps determine if incidents are isolated or part of a recurring pattern.
When AI has access to this kind of detailed, varied data, it can help operations teams make faster, smarter decisions.
PagerDuty, a leader in digital operations management, offers the PagerDuty Operations Cloud. This platform combines AIOps, automation, customer service operations, and incident management to support flexible, scalable response workflows.
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