Book Image

DevOps Culture and Practice with OpenShift

By : Tim Beattie, Mike Hepburn, Noel O'Connor, Donal Spring, Ilaria Doria
Book Image

DevOps Culture and Practice with OpenShift

By: Tim Beattie, Mike Hepburn, Noel O'Connor, Donal Spring, Ilaria Doria

Overview of this book

DevOps Culture and Practice with OpenShift features many different real-world practices - some people-related, some process-related, some technology-related - to facilitate successful DevOps, and in turn OpenShift, adoption within your organization. It introduces many DevOps concepts and tools to connect culture and practice through a continuous loop of discovery, pivots, and delivery underpinned by a foundation of collaboration and software engineering. Containers and container-centric application lifecycle management are now an industry standard, and OpenShift has a leading position in a flourishing market of enterprise Kubernetes-based product offerings. DevOps Culture and Practice with OpenShift provides a roadmap for building empowered product teams within your organization. This guide brings together lean, agile, design thinking, DevOps, culture, facilitation, and hands-on technical enablement all in one book. Through a combination of real-world stories, a practical case study, facilitation guides, and technical implementation details, DevOps Culture and Practice with OpenShift provides tools and techniques to build a DevOps culture within your organization on Red Hat's OpenShift Container Platform.
Table of Contents (30 chapters)
Free Chapter
2
Section 1: Practices Make Perfect
6
Section 2: Establishing the Foundation
11
Section 3: Discover It
15
Section 4: Prioritize It
17
Section 5: Deliver It
20
Section 6: Build It, Run It, Own It
24
Section 7: Improve It, Sustain It
27
Index
Appendix B – Additional Learning Resources

Metrics-Driven Transformation

Metrics-driven transformation focuses on using value-based business metrics to understand how technology-related investments impact organizational performance and provide specific tools and guidance to help improve those metrics.

In the previous chapter, we looked at different approaches to doing delivery, whether that be Waterfall, or using an Agile framework such as Scrum or Kanban. As we complete loops of delivery, we want to take value-based metrics to validate hypotheses, confirm the results of experiments, clarify the impact of our feature deliveries, determine whether we have moved toward the Target Outcomes we set out, and make decisions around what to do next.

There are many different levels of measurements we can take in our delivery ecosystem and a growing number of sources we can collect them from. In this chapter, we will explore metrics we can collect automatically from our software and our platform as well as practices we can use to...