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whitepaper

AIOps Without the Snake Oil.

AIOps can cut repetitive triage and speed investigation. It cannot replace service ownership, clean telemetry, or tested runbooks. This report separates production use cases from autonomy theater.

In depth

Autonomous Operations Is the Pitch. Assisted Operations Is the Value.

01

Why it persists: Duplicate alerts, inconsistent tags, missing ownership, and unstable baselines create noisy models.

Events that occur together are not always causally related.

In shortEvents that occur together are not always causally r…
02

What recovers it: Begin with repeated, low-value alert noise.

Automatically collect recent changes, owners, dependencies, dashboards, similar incidents, and runbooks.

In shortrunbooks
The detail

Where Data Products Lose Adoption.

Zone · 01

Ownership has moved closer to domains.

The teams that understand the business meaning are better placed to own data quality and change. Central platforms should provide the tools and guardrails, not become the semantic bottleneck.

Zone · 02

The interface is broader than the dataset.

A usable product includes definitions, access, freshness, quality, lineage, examples, support, and change policy. The table is one component.

Zone · 03

Self-service requires standardization.

Consumers need a consistent way to discover, evaluate, request, and use products. Without a common contract, every product becomes a custom integration.

By the numbers

The figures that make it a board-level conversation.

4
credible use cases lead current AIOps research: anomaly detection, cause analysis, incident reports, and assisted remediation
4
maturity areas determine adoption: purpose, ownership, contract, and repeat use
9
contract elements 98define the minimum: owner, purpose, interface, definitions, access, freshness, quality, support, and change policy
Inside the report

What you'll take away.

01

Step 1: A consumer job and success measure

Write the job in plain language: who needs what decision or workflow, how often, and what happens when the data is late or wrong. This anchors scope.

02

Step 2: A product contract

Define owner, interface, schema, business definitions, access model, freshness, quality, support, and change policy. Make the contract machine-readable where possible.

03

Step 3: Published quality and usage signals

Expose freshness, tests, incidents, adoption, repeat consumers, query patterns, and support volume. Consumers should be able to evaluate fitness without asking the owner.

04

Step 4: A feedback loop tied to the roadmap

Interview high-value consumers, observe failed discovery and access attempts, and prioritize friction that limits repeat use. Usage should shape investment.

Questions

Frequently asked.

Is every dataset a data product?
Who should own the product?
What is the minimum contract?
Do we need a marketplace?
What is the best adoption metric?
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Next step

The Product Starts With the Consumer, Not the Table.

Talk through how this applies to your roadmap with our engineering leads - a working session, not a sales pitch.

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