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● ORGANIZATION ASSESSMENT

Assess Your Organization's Observability Maturity

Use one organization, team, or service as the scope for each assessment. Select the practices you can verify in current operations to see maturity across six dimensions and the first priorities for the next 90 days.

Answer from current operating evidence, not intentions or plans. The result is a starting point for agreeing on what to improve next—not a scorecard or certification.

Published: Jul 18, 2026
Version: 1.0.0
Publisher: 801 PLANET
Technical review: Jeen Lee
Public sources: 3

Criteria met0 of 89 criteria met

We use your selections to calculate current maturity across six dimensions and the first priorities for the next 90 days. The assessment response is saved when results are shown; the email report is optional.

Select only practices your team can repeatedly verify in current operations. A dimension advances only after every criterion in all earlier transitions is met.

01Assessment dimensionData collection and visualizationThe ability to collect data across the system and visualize and analyze it in real time.Current levelLevel 1

Level 1 → 2Criteria for reaching Level 2

Basic infrastructure metrics such as CPU, memory, and disk are explicitly defined.
Basic metrics are collected regularly through an agreed standard tool.
A simple dashboard shows key metrics in real time.
An owner is assigned to monitor the dashboard and respond.

Level 2 → 3Criteria for reaching Level 3

Key infrastructure, application, network, and database components are all monitored.
Key data is collected at intervals of about one minute or less.
Collection settings and the actual presence and visualization of expected metrics are reviewed regularly.
Monitoring gaps caused by missing configuration or tags are identified and corrected.

Level 3 → 4Criteria for reaching Level 4

Dynamic-baseline anomaly detection is applied to key signals.
Graphs and alerts make normal and abnormal states comparable.
Anomaly indicators are immediately visible on the dashboard.

Level 4 → 5Criteria for reaching Level 5

Telemetry detects post-release performance changes and affected scope quickly.
Predictive analysis forecasts anomaly signals automatically.
The likely impact scope is estimated automatically when anomalies arise.
Prioritized response options are proposed automatically and fed into the improvement cycle.

02Assessment dimensionSystem reliability managementThe ability to improve availability, performance, and risk continuously through incident and recovery processes grounded in observed reliability signals.Current levelLevel 1

Level 1 → 2Criteria for reaching Level 2

The first-response, escalation, and recovery-closure flow is documented.
Notification rules and logging policies needed for initial response are defined.
Basic reliability indicators such as availability and response time are defined.
The defined basic reliability indicators are being measured.

Level 2 → 3Criteria for reaching Level 3

A standard incident process and templates cover detection, recording, analysis, and recurrence prevention.
Targets and a recurring review cadence are defined for indicators such as availability and error rate.
MTTR, MTBF, and availability are measured and shared with the team.
A system-risk register and assessment criteria exist, and risks are prioritized by severity.

Level 3 → 4Criteria for reaching Level 4

Availability, incident count, and recovery time are monitored statistically.
The effects of recurrence-prevention and response-time improvements are analyzed regularly.
Metrics, logs, and traces to capture during an incident are standardized.
Required telemetry is collected automatically as soon as an incident starts.

Level 4 → 5Criteria for reaching Level 5

Log and metric analysis identifies failure signals and likely impact automatically.
Recovery procedures execute automatically according to incident type.
Recovery history and knowledge are captured automatically and shared across the organization.
Automated analysis, recovery, and learning form a continuous reliability-improvement loop.

03Assessment dimensionDevelopment and operations process optimizationThe ability to deliver changes predictably and reliably while maintaining code quality.Current levelLevel 1

Level 1 → 2Criteria for reaching Level 2

Release and operations roles and decision flows are defined.
Minimum procedures and accountable owners are documented visibly.
Static analysis automatically detects basic code-quality issues.
A repeatable lightweight code-review practice exists.

Level 2 → 3Criteria for reaching Level 3

Standard templates and procedures exist for key processes such as design and release.
Standard procedures maintain consistent quality across different operators.
A CI/CD pipeline is established or an existing pipeline has been improved.
Release procedures are prioritized for standardization and automation.

Level 3 → 4Criteria for reaching Level 4

Key artifacts to evaluate are defined for each development and operations phase.
Each key artifact has explicit review and verification checkpoints.
Objective quality criteria exist for documents and work outcomes.

Level 4 → 5Criteria for reaching Level 5

Continuous-improvement work is part of the official development and operations cadence.
Retrospectives run repeatedly on an agreed cadence and format.
AI is introduced incrementally for code or change-impact analysis.
Automation is expanding incrementally from review through deployment.

04Assessment dimensionAlert optimization and incident responseThe ability to detect system anomalies accurately, suppress noise, and respond quickly.Current levelLevel 1

Level 1 → 2Criteria for reaching Level 2

All alert rules currently in operation have been inventoried.
Noisy or unnecessary alerts are explicitly identified.
Every alert is assigned a level using a common severity classification.
Response priority is explicit for each alert severity.

Level 2 → 3Criteria for reaching Level 3

Key alert signals are organized for service, infrastructure, and network layers.
Dashboards and logs to inspect first when an alert fires are standardized.
Dynamic thresholds or anomaly detection are applied to key alerts.
Reduction in unnecessary notifications is verified after adaptive detection is introduced.

Level 3 → 4Criteria for reaching Level 4

Incident-response flows are documented for each severity level.
Escalation owners and paths are explicit for each severity level.
The highest-severity incidents can be escalated and handled immediately.

Level 4 → 5Criteria for reaching Level 5

Signals for real-time detection before incidents, such as latency and error rate, are defined.
Baseline-anomaly or machine-learning alerting is in production use.
Predictive alert settings and models are reviewed and improved continuously.

05Assessment dimensionUser behavior understanding and optimizationThe ability to understand user activity and needs and apply them to system and product improvement. API clients may be included when they represent human behavior or are valid analysis subjects.Current levelLevel 1

Level 1 → 2Criteria for reaching Level 2

Basic user metrics such as daily active users and key-feature usage frequency are defined.
The defined basic user metrics are collected continuously.
Basic user metrics are visualized for at-a-glance inspection.

Level 2 → 3Criteria for reaching Level 3

Behavior-log coverage is broad enough to analyze critical user journeys.
Movement through each step of critical user journeys is visualized.
A dashboard exposes drop-off points for each journey.
Behavior bottlenecks are identified and used to improve retention or adoption.

Level 3 → 4Criteria for reaching Level 4

A performance KPI is defined for each key user-behavior pattern.
Behavior KPIs are monitored regularly on a dashboard.
AI analysis detects trends and anomalies in behavior data automatically.
Automated findings are used to investigate KPI changes more quickly.

Level 4 → 5Criteria for reaching Level 5

Behavior findings are translated into concrete value-delivery actions by user attributes and tendencies.
Executable optimization measures such as recommendation strategy or UI policy are defined.
The defined measures are being applied to optimize the actual user experience.

06Assessment dimensionContinuous improvement and optimizationThe ability for the whole team to repeat improvements using monitoring and development-process data.Current levelLevel 1

Level 1 → 2Criteria for reaching Level 2

A simple retrospective is held after an incident or release.
Issues and improvement opportunities found in the retrospective are recorded.
Retrospective records are shared where team members can access them.
Recorded findings are used for subsequent operational improvement.

Level 2 → 3Criteria for reaching Level 3

Basic operational indicators such as detection time, recovery time, and release success rate are defined.
The defined operational indicators are visualized continuously.
Team members can inspect the same operational indicators together.

Level 3 → 4Criteria for reaching Level 4

Qualitative information such as incident-response records and release problems is collected.
Quantitative indicators and qualitative information are shared with the team.
Improvement cases accumulate as searchable, reusable knowledge.
The team repeatedly learns together from accumulated cases.

Level 4 → 5Criteria for reaching Level 5

Recurring reviews visualize and share KPI attainment and improvement outcomes.
Continuous improvement is recurring organizational work rather than an individual responsibility.
AI is used to identify improvement targets and propose possible actions.
Execution of validated improvement actions is becoming automated and autonomous incrementally.

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Source and license

An unofficial 801 PLANET translation and extension of the DMM.com LLC source, with checklists, vendor-neutral solution categories, and related-resource links for web self-assessment. It is neither an official translation nor endorsed by DMM.com LLC.

Original repository: DMM.com LLC ↗CC BY 4.0 ↗