Observability maturity assessment table¶
Estimated time to read: 2 minutes
Work in progress
| Logging | Monitoring & Alerting | Dashboards & Visualization | Observability Features | Observability Outcomes | |
|---|---|---|---|---|---|
| 1. Baseline | Collect and store logs from applications and infrastructure. Establish basic log management processes. Begin familiarising teams with log analysis | Data Platform Service Monitoring Begin collecting and storing logs from applications and infrastructure | Begin enhancing customer's experiences | ||
| 2. Novice | Implement monitoring tools to collect and analyse logs, metrics, and events. Set up alerting mechanisms to notify teams of potential issues. Improve log coverage across the stack. Establish monitoring and alerting best practices | Data Pipeline Performance Monitoring Implement monitoring tools to collect and analyse logs, metrics, and events | Boost software delivery speed and reduce time to market | ||
| 3. Intermediate | Develop and use dashboards to display telemetry data. Implement visualisation tools for better data analysis. Enhance data accessibility for teams. Foster data-driven decision-making | Data Quality Monitoring Enhance data accessibility for teams. Foster data-driven decision-making | Operate more efficiently | ||
| 4. Advanced | Data Lineage Automate instrumentation across the software stack. Unify telemetry data in a single pane for consumption across teams. Implement CI/CD practices for software deployment. Ingest high-cardinality data and support on-the-fly querying | Improve business performance, uptime, and reliability | |||
| 5. Expert | Data Discovery Shift developer and engineer time from reactive to proactive work. Improve collaboration across teams in decision-making related to the software stack. Mitigate service disruptions and business risk through observability. Include business context in telemetry data to quantify the business impact of events and incidents. Enhance revenue retention and create revenue-generating use cases through a deep understanding of customer behaviours | shifting developer and engineer time from reactive to proactive work, improving collaboration across teams in decision-making related to the software stack, mitigating service disruptions and business risk through observability, including the business context in telemetry data to quantify the business impact of events and incidents, and enhancing revenue retention and creating revenue-generating use cases through a deep understanding of customer behaviours. These outcomes represent the highest level of maturity in observability practices. They can help organisations enhance customer experiences, boost software delivery speed, operate more efficiently, and improve business performance. |