Phase 1: Build a unified observability foundation
Break down data silos by integrating heterogeneous sources across open-source tools, public cloud platforms, and databases. With non-intrusive instrumentation, enterprises can capture full-stack traces across core systems such as ERP and MES, while automatically mapping service topology. This creates full visibility across both applications and infrastructure.

Phase 2: Extend visibility from cloud to edge
Observability should not stop at the data centre. By introducing Digital Experience Monitoring at the edge, organisations can capture real user latency from physical terminals. Combined with global synthetic testing, this fills blind spots across public networks. A unified data layer then connects IT, OT, and edge data into a single model, enabling complete visibility across cloud, edge, and endpoints.

Phase 3: Enable AIOps-driven operations
With unified data in place, AIOps can consolidate alerts from multiple sources into a single intelligent system. Automated root cause analysis and full data snapshots allow faster troubleshooting and replay of incidents. With the support of large models, organisations can move towards predictive capacity planning, intelligent diagnostics, and decision support.