Making observability actionable and scalable for the IT Department
1. Understand Context and Topology
Understand how applications and infrastructure are connected by identifying relationships and dependencies among all components across potentially billions of interconnected parts. Gather rich contextual data that allows real-time topology maps showing dependencies across stacks, services, processes, and hosts.
2. Implement Continuous Automation
Automatically discover, instrument, and baseline every system component continuously. This shifts IT effort away from manual setup to value-adding innovation projects focused on understanding what matters. Observability becomes “always-on” and scalable, allowing constrained teams to do more with less.
3. Establish True AIOps
AI-driven fault analysis combined with code-level visibility enables teams to automatically pinpoint the root cause of issues without time-consuming manual efforts. AI can also automatically detect unusual changes to discover unknown problems teams are unaware of. These actionable insights drive faster, more accurate responses.
4. Foster an Open Ecosystem
Observability considers external open-source data sources like OpenTelemetry guided by vendors. Automated discovery, instrumentation, and topology mapping support platforms seeking scalable observability solutions.
5. Utilize AI
An AI-driven solution makes observability truly actionable by addressing cloud complexity challenges. It helps interpret vast data streams from multiple sources at increasing velocities. With a single source of truth, teams can quickly and accurately pinpoint root causes before performance degrades or accelerate recovery if a failure occurs.
What is Observability?
As technology systems become more complicated, the teams that manage them face growing challenges in keeping track of and addressing problems across different cloud environments. Due to this, the teams responsible for operations, development, and system reliability seek better visibility and understanding of these diverse and intricate computing setups. They need simpler ways to monitor and identify the issues within these complex systems.