The Role of Telemetry: Feeding AI with the Right Data

• AI Is Only as Good as the Data It Sees

You can’t expect intelligent answers from incomplete inputs. AI-powered observability platforms rely on telemetry — logs, metrics, traces, events — to make sense of complex environments. Yet many teams still struggle with siloed tools and inconsistent data. Without clean, connected telemetry, AI can’t deliver meaningful insights.

• Not Just More Data — Better Data

Collecting massive volumes of telemetry isn’t the goal. What matters is coverage, quality, and structure. High-cardinality metrics, enriched logs, and distributed traces allow AI to detect anomalies, build baselines, and connect causes across layers. One enterprise improved anomaly accuracy by 42% after standardizing and enriching their telemetry pipeline.

• From Collection to Context

Effective AIOps starts with thoughtful telemetry design: What’s collected? From where? At what granularity? AI doesn’t just crunch numbers — it looks for relationships. The better the data, the smarter the output. That’s why modern NetOps teams are investing in unified pipelines and vendor-agnostic collection strategies.

Great AI starts with great data.

Observeasy helps teams unify, enrich, and structure telemetry — giving AI the foundation it needs to deliver real insight.

📌 More context. Fewer blind spots. Better decisions.

👉 Book a demo to see what your data is really capable of.