A renamed dataset is not a data product. This report shows what makes one useful: a real consumer job, a clear owner, a dependable contract, visible quality, and a feedback loop that changes the roadmap.
Why it persists: When a team publishes data for "the business," requirements stay vague and prioritization becomes political. A group alias is not accountability.
What recovers it: Write the job in plain language: who needs what decision or workflow, how often, and what happens when the data is late or wrong. Define owner, interface, schema, business definitions, access model, freshness, quality, support, and change policy.
Distributed systems produce more alerts, logs, metrics, traces, and changes than an operator can correlate manually. This is a real machine learning problem.
An anomaly without service dependency, ownership, deployment, and configuration context produces weak diagnosis. AIOps needs a reliable operational graph.
Language models are useful for summarizing evidence, translating telemetry, and guiding investigation. They can still invent causes or steps.
Begin with repeated, low-value alert noise. Group events using topology, timing, and known patterns, while preserving the evidence an operator needs.
Automatically collect recent changes, owners, dependencies, dashboards, similar incidents, and runbooks. Faster orientation often creates more value than automatic action.
Rank likely causes and safe next checks. Show why each recommendation was made, which evidence supports it, and what information is missing.
Automate only tested, reversible actions with preconditions, approval rules, observability, and rollback. Feed actual outcomes back into the system.
AIOps works when it compresses the observe and orient phases of incident response, then helps execute known actions safely. It fails when leaders buy autonomy before building telemetry, topology, ownership, and runbooks. Fix the operating foundation first.
No. It can reduce repetitive triage and known remediation, but humans still own risk, architecture, and novel failure.
It can rank hypotheses. Root cause should remain evidence-backed and reviewable.
Page volume, time to acknowledge, time to orient, recovery time, toil, false positives, and repeat incident rate.
Alert deduplication and incident context assembly, because both are high-volume and reversible.
Telemetry, service ownership, dependencies, recent changes, incident history, and tested runbooks.
