What this book covers
Chapter 1, Fundamentals for an Operating Model, looks at the challenges that the journey to the cloud has thrown at organizations and why. It goes through a rich set of examples to look at different ways to define key components of an operating model: the operating model dimensions. It also introduces key terms from engineering and operations and distinguishes between platform and product engineering and SRE. This chapter closes with thoughts on how to construct metrics, teaming, and how Conway’s law affects your architecture.
Chapter 2, Enterprise Technology Landscape Overview, gives an overview of common enterprise technology landscape components. It distinguishes between systems of innovation, differentiation, and systems of record, and examines the associated change cadence and challenges related to these cadences by expanding the classification from applications to infrastructure. It completes the discussion by looking into the difficulties around the adoption of a standard operating model because of the distinguished traits across that classification.
Chapter 3, Learnings From Bimodal IT’s Failure, examines closely why the Gartner Bimodal IT approach never yielded the results expected and extracts the learnings out of it in order to apply them to the distributed technology operating model. It looks at the change cadence differences between mode 1 and mode 2. It closes by highlighting that there is no endorsed bimodal architecture that combines a working approach between mode 1 and mode 2 estates.
Chapter 4, Approaching Your Distributed Future, focuses on the imminent distributed future. It looks at the reasons why the future is distributed and revisits hybrid and multi-cloud definitions while shedding light on specific business and technology reasons why the public or single cloud cannot be a target state for organizations. It spends time on different edge classifications to get a better handle on different viewpoints in light of worthwhile use cases. It closes by looking at emerging trends and external factors such as compliance, mergers, and acquisitions.
Chapter 5, Building Your Distributed Technology Operating Model, explains in detail the building blocks for a distributed operating model across the cloud and edge. It starts off by showing the steps toward the desired outcome in the Starting at the end section and introduces an operating model dashboard to track outcomes, work in process (WIP), and dependencies. It presents workshop-leading practices to help lead teams along the forming, storming, norming, and performing life cycle. The chapter also walks through more than 30 dimensions to consider and choose from for the operating model. It also provided numerous suggestions for further reading if you want to dive deeper into any of the research underpinning our recommendations.
Chapter 6, Your Distributed Technology Operating Model in Action, introduces an anonymized real-life use case and walks through how this organization built its distributed technology operating model in a hybrid multi-cloud and edge context.
It walks through the step-by-step process of utilizing already introduced templates and new assets that can be reused for the operating model development process.
Chapter 7, Implementing Distributed Cloud and Edge Platforms with Enterprise Open Source Technologies, walks through an operating model-based platform implementation example. It connects real work architecture, design, and implementation with the previously developed operating model. It shows how requirements and principles from the operating model flow into technology selection and how they map to capabilities.
Chapter 8, Into the Beyond, wraps the book up. It introduces additional aspects such as antifragility, geographically disparate (non-)autonomous operating models; different ways to measure progress; how tech debt, undifferentiated heavy lifting, and open source are connected; gap analysis; and roadmap development. It revisits prioritization and decision-making and introduces a quick way to make the best possible decision with the often limited information available.