The next evolution of observability starts with telemetry

The evolving landscape of technology has led to increased complexity and scale in modern environments, making it challenging for technology teams to manage telemetry effectively. Traditionally, the assumption was that more telemetry would lead to better visibility and quicker problem resolution. However, fragmented telemetry and unclear signals have resulted in longer problem resolution times and rising costs associated with storing and processing large volumes of data.

To address these issues, organizations are reconsidering the placement of telemetry processing within the technology stack. By collecting, processing, and routing relevant telemetry upstream, teams can filter out irrelevant data, enrich important information, and send only meaningful telemetry downstream. This approach not only reduces the volume of telemetry to handle but also decreases expenses and ensures cleaner telemetry for analysis.

Open standards like OpenTelemetry facilitate this upstream processing, allowing teams to gather telemetry consistently across diverse environments without relying on specific vendors or tools. As technology continues to advance, the ability to adapt to evolving environments becomes increasingly valuable.

Moreover, the importance of high-quality telemetry is amplified with the integration of AI-driven capabilities. AI systems rely on accurate and contextualized telemetry to generate insights and automate decisions. Poor-quality telemetry can hinder AI performance and compromise trust in automated decisions, affecting both technical teams and senior management.

Investing in a strong telemetry and data foundation early in the lifecycle is crucial for faster innovation and better customer outcomes. By ensuring the relevance, trustworthiness, and scalability of telemetry, organizations can support actionable insights and drive successful AI-powered initiatives.