Strategy typically starts with an evaluation of organizational direction, organizational dynamics (team alignment, team boundaries), existing team capabilities, and appetite for continuous improvement across applicable cohorts. It is important to establish focus (Services Observability is very different from other forms of observability such as Business Observability, Model Observability, and Data Quality Observability) Evaluate current ad-hoc capabilities which demands analysis in runtime, providing prompt reaction to erroneous behavior in near real-time, making this a requirement to meet complex failure modes Understand current maturity with respect to central tendency metrics such as Mean Time to ‘X’ (MTTx) Understand TCO expectations as a dimension of planning against a cost baseline, how those costs should be captured, and where those costs should be allocated

Establishing an Observability Operating Model (OOM) is easily the most fundamental and critical element to success. As Observability is a journey, establishing clear operational motives and common goals will allow many teams to self-identify and co-create value. Establish clear boundary of responsibilities between Product Teams, Operations/Global Command Center (OCC), Strategy & Architecture, Site Reliability Engineering (SRE), and Platform Engineering/Shared Services. Here we consider the role of centralized and decentralized behaviors as it relates to Observability Establish prioritization regarding Product Team operationalization, including alignment with Developer and/or Platform Engineering advocacy and adoption programs, architectural goals, integration to operational capabilities around notification, ticketing systems, dashboard creation, KPI onboarding/gates, etc. Determine guidance around Operational Support Patterns that provide select Product Teams greater autonomy to introduce/manage observability tooling and processes, and define what contracts must be established with the organization to support such patterns Develop maturity and discipline regarding observability practice capability (introduction and deprecation) through introduction and refinement of architecture patterns, framework(s), tools, and ready-to-use code libraries Develop a formal and consistent commitment to measuring lost engineering cycles across Product Teams (e.g., partnering with Dev/Platform advocacy) for continuous process improvement/refinement

An Observability Pipeline establishes guardrails (compliant facility that aligns with IT control narratives) and advanced data controls (e.g., obfuscation, removal of superfluous data, enrichment), while promoting vendor neutrality for observability platforms. This is often a starting point in terms of implementation, and like all other domains this should align with the Observability Operating Model (OOM) where careful consideration was weighed when contemplating a buy vs build approach and centralized vs decentralized deployment patterns. The benefits of creating this beach head are fundamental if building toward vendor independence as this domain not only prevents vendor lock-in, but allows for safe & rapid tools experimentation while potentially reducing costs of existing tooling. Note: Establish and manage this Observability Pipeline as a Product, and build a robust set of advocacy programs to ensure community engagement. Building this agnostic facility to route MELT data with guardrails opens the proverbial spigot to expanded tools usage. As per the Observability Operating Model (OOM), clear definitions of 'acceptable' have to be established. Will you allow any Product Team to instantiate their own tools, or will this be owned by Platform Engineering? Establish cohorts based on maturity/capability/needs and just like a preventative vs detect/correct path for security, either allow for tools through a measurable discipline and program, or provide a measure of control via a gated process. The better you know your constituents, the more prescriptive you can be with your tools alignment. If you allow for bespoke tooling - the Observability Pipeline allows you to audit usage per cohort/Product Team. Pay close attention to the 'why' behind usage decisions. With groups that are less mature, Platform Engineering tends to provide more tools leadership and ownership. With groups that are more mature, the opposite is true. The more advanced groups will provide drag for the teams just starting out and should be considered as you develop your advocacy programs.

Evaluate and establish architectural tenets that commoditize telemetry collection and propagate context to backend systems. This can be a long pole to attack, and you will need to think carefully about the developer experience as you focus on signal emission capture.

Focus on aggregation and correlation as a Common Data Interpretation layer and move teams to the common interface (Global Command Centers, SREs, Developers, etc.). Aggregation of disparate observability tools (COTS/custom) creates a common interpretation layer for all teams to operate from, and correlation improves overall central tendency metrics (e.g., MTTD, MTTR) while simplifying tools rationalization. Without this approach, unwinding platform usage can become a protracted effort.

Maturing an observability discipline promotes value extraction beyond baseline central tendency metrics (e.g., Agile metrics, regression/performance management, resilience engineering, chaos engineering, etc.)