Navigating the DevOps infinity loop involves more than coding and deployment; it extends to effective collaboration with IT Service Management (ITSM) and closing the loop from operations to product discovery.
Understanding Operations in DevOps:
Operations in DevOps involves managing deployed code, ensuring its availability, capacity, and security. Collaboration between Dev and Ops is crucial for effective decision-making, irrespective of the tools used or the team’s structure.
Observing and Monitoring for Improvement:
Observation and monitoring aim to answer questions about the results obtained. Metrics from a DORA report, covering deployment frequency, change lead time, change failure rate, and time to restore service, are integral. The integration of CI/CD pipelines with tools like Jira Service Management automates aspects of change control and incident management.
Observation Beyond Metrics:
Observation extends beyond DORA metrics to user support interaction, feature usage, and overall product user experience. Monitoring different dimensions, including organizational, technological, partner-related, and process-related aspects, is essential for a comprehensive root cause analysis.
Continuous Feedback and Collaboration:
Continuous feedback requires collaboration forums, facilitating regular meetings between customer support, development, and operations teams. Collaboration fosters a culture of transparency, essential for sharing insights and building cross-functional relationships.
Integrating Feedback into Product Discovery:
Closing the loop involves transforming raw operational data into structured information, prioritizing and evaluating observations, and integrating them into product discovery. The goal is to utilize insights gained from operations during the evaluation and prioritization of new features or improvements.
Key Actions to Consider:
1. Collaboration and Dialogue:
– Bring different teams together for regular sharing sessions.
– Cultivate a collaborative and transparent culture based on ITSM, Agile, Lean, and DevOps principles.
2. Structured Data Transformation:
– Transform raw operational data into structured, contextualized information.
– Create knowledge as a foundation for decision-making.
3. Prioritization and Evaluation:
– Assess, evaluate, and prioritize observations from operations.
– Ensure that only crucial feedback enters the product discovery phase.
4. Integrate Feedback into Product Discovery:
– Establish mechanisms to use information from observation and continuous feedback in product discovery.
– Evaluate, prioritize, and make decisions based on insights gained during operational phases.
5. Continuous Observation and Monitoring:
– Use observation tools providing relevant information to all stakeholders in the DevOps loop.
– Empower teams to act on identified deviations and improvements.
6. Root Cause Analysis:
– Conduct a comprehensive root cause analysis, considering organizational, technological, partner-related, and process-related dimensions.
7. Continuous Improvement in DNA:
– Instill a continuous improvement mindset in the organizational culture.
– Encourage proactive feedback to enable ongoing product enhancement.
8. Integrate and Build Analysis Capabilities:
– Ensure tools are integrated to automate measures and metrics.
– Integrate CI/CD pipelines with ITSM tools for efficient change control.
9. Close the Feedback Loop:
– Facilitate communication to prevent information silos.
– Ensure seamless flow of information from operations to product discovery, closing the DevOps infinity loop.
Implementing these actions will not only enhance operational efficiency but also contribute to a holistic DevOps approach tightly integrated with ITSM practices. For organizations aiming for a complete DevOps way of working, these steps provide a strategic roadmap for assessment and improvement.