DevOps and SRE, Chapter 4: Explaining It To Business Management Executives

DevOps and SRE, Chapter 4: Explaining It To Business Management Executives

Why the collection of practices that today we know as DevOps and SRE (Site Reliability Engineering) are becoming the norm for modern systems management.


Cloud-native applications are a type of complex system that depends on the continuous effort of the professionals that everyday combines the best of their expertise to keep them running. In other words, their reliability doesn't simply rely on themselves but results from the interactions of all the different actors engaged in their design, build, and operation. Over the years, the collection of those interactions has been evolving together with the systems they were designed to maintain, which have also been becoming increasingly sophisticated and complex. Once designed to maintain control and stability, the IT service management model is now fading and giving place to a model designed to improve velocity while maintaining stability. Although the combination of those things might seem contradictory at first, this series of articles tries to reveal why the collection of practices that today we know as DevOps and SRE (Site Reliability Engineering) are becoming the norm for modern systems. 

Table of Contents:

  1. Chapter 1 - When innovation becomes mainstream (released: 12/09/2019)
  2. Chapter 2 - How to cope with complexity (released:12/09/2019)
  3. Chapter 3 - Models for cultural change (released 12/16/2019)
  4. Chapter 4 - Explaining it to management (this document)
  5. Chapter 5 - Accelerate: The Science of Lean Software and DevOps (coming soon)
  6. Chapter 6 - Signals of change (coming soon)
  7. to be continued ...

Chapter 4: Explaining It To Management

DevOps has been a recurring theme in IT circles for a little more than 10 years. Although many people, and I include myself, firmly believe they understand it, the fact is that the subject is in flux, practices, processes, and tools are changing so very rapidly that to enable mainstream adoption effectively, we need to start bridging the gap between IT and "The Business". I know this is a controversial theme, and everybody seems to have a strong opinion about it, chances are we all have reasonable, valid assumptions. Still, I want to use this chapter to articulate how to develop effective communication and use it to build the bridge that can potentially close the gap between business management and IT and, as a consequence, widespread the adoption of these innovations in engineering practices in all organizations.

The basic assumptions I will use to articulate about the importance of developing a good communication strategy and how it is key to succeed in the enterprise adoption of these new engineering practices are:

  • Information Technology (IT) is the power engine of the digital economy. We're using more of it every day to ease our lives and satisfy our needs. As I described in the previous chapters, IT systems are becoming more complex, and there's no indication this characteristic will change any time soon. How to explain that the traditional ways of managing IT systems won’t work on the new cloud-based systems?;
  • "The business" and Technology have very distinct vocabularies and don't always reason about cause and consequence in the same way;
  • There is an increasing need to infuse scientific thinking across organizational boundaries as systems became more sophisticated. On multivariate analysis, which is predominant in complex systems, the norm is to look for correlation rather than causation ;
  • Enterprises are designed to operate in the most efficient way possible (most enterprises), and IT is usually perceived as a cost center rather than an investment. Justify additional work (less efficiency) with a promise, not always backed by data, that this extra work will pay off in the future isn’t easy. We need to offer good alternatives to let business executives manage the risk of failure;
  • Most people in all organizations fit into the psychologic profile found in the early and late majority distribution from Geoffrey Moore's models for diffusion of innovation. They're pragmatists and need referenceable examples of successful adoption of the practices before they can embrace the change. We need to find ways to cross the chasm from innovators and early adopters and engage with this pragmatist population.

devops cloud computing innovation lean agile and devops sre complex systems continous improvement

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