Foundation for design – the thought process

The cloud was supposed to be simple. It was supposed to be fast. It was supposed to solve many, if not all, of the problems. As people started digging into cloud designs, they began to discover that things were not always as simple, things were not always less expensive, and things were not always performing as expected. In many cases, they experienced significant challenges with social adoption. The design did not match expectations for various reasons. Throughout this book, we talk about how perceived requirements are merely starting objectives that accelerate towards requirements with the gathering of additional insight and data. Ultimately, successful designs must simultaneously harmonize economics, strategy, technology, and risk. This balance leaves risk and economics offset at equilibrium.

Cloud computing is one of the rare things in our industry where nearly everyone is impacted or affected by the change. Adoption of the change is the big challenge. People must embrace the transition while they adapt to process and work method changes. Without mental and emotional buy-in, projects can stall, exceed budgets, and, potentially, fail. The only path to acceptance and cultural change is through data.

As an example, consider a developer who is required to utilize a different cloud provider for all projects going forward. That developer must change processes and working methods and, potentially, go through a significant learning curve for new systems, applications, and tools. This example is not about the transition to the cloud, but about transitioning between clouds. How well does this go over if the developer enjoyed working with the previous supplier? What if that developer was very efficient in using the toolsets and could quickly navigate current processes? What if the developer was with presented data showing that the new provider could provide machines at 30% lower cost? The developer then can acquire and utilize three times the number of servers for the same budget. Consequently, what if productivity then increased tenfold when more compute resources are combined with integrated toolsets and automated processes? Data helps drive adoption in all cases.

As we begin to think through design considerations, a consistent thought process is required. Consistent, methodical, process-oriented thinking will help accomplish several things, such as:

• Eliminating the noise
• Enabling quick navigation through the complexity
• Maintaining focus on constraints and objectives
• Allowing fast and accurate interpretation of relevant insight
• Accurately identifying optimal solutions satisfying constraints
• Quickly identifying opportunities to optimize strategy