This is the seventh and final post in a series of posts alphabetically listing and defining important Observability terms. If you have just jumped in, then please consider starting from the beginning to get a better understanding of my selection.
We must become a Yogi when it comes to Observability and beyond (Controllability, Monitoring, and Management) in following the personal guidelines such as cleanliness, contentment, discipline, introspection, and surrender. Data must be cleaned. Sensors and measurement filtered of impurities (noise). The organization and design of information fall under hygiene. A tidy and minimal information surface space aids decision making and increases the embracement of change. With contentment, there is a greater appreciation and understanding of the system as a whole, along with a recognition that much of the system is undergoing a process of change that needs to be steered in a positive direction – warts and all. More data, of the same type or from a single source, is not always better. Discipline is needed in instrumenting and data collection; otherwise, the system and the agents (human and machine) enclosed within will be overloaded and operational impaired. Observability enables understanding and, more importantly, guides action. This is achieved by continuously reflecting on what has happened, what has been done, and what were the consequences. Introspection leads to greater awareness and improved attention giving us a more holistic view of the system beyond machines and mechanics. We must in some way and part surrender to complexity; control in totality is an illusion that does not serve well in light of modern changing complex systems.
The zenith is an imaginary point directly above a particular location in the vertical direction opposite to the gravitational force at that location. The gravitational force being measurement data, lots of it, and growing daily. All too often, Observability teams become bogged down and prematurely rooted to a lower level of service and operation in giving too much consideration to the production and consumption of low-level data by way of the creation and maintenance of “databoards” as opposed to situational screens and predictive projections. It is imperative that teams try to keep some degree of detachment from data, types, and sources, recognizing the transient nature of code, coupling, and containment. Organizations should never lose sight of the big and strategic picture – the process of monitoring and management for the primary purpose of accelerated adaptation and agility while maintaining satisfactory levels of quality, reliability, stability, and predictability. Time to leave the data race.
Below is a visual recap over the seven posts. I hope along the way some small nuggets of wisdom were imparted.