Stage 3: Business observability — when technical signals meet money
Business observability is where observability becomes a strategic CIO concern rather than an engineering concern. In this stage, organizations move beyond telemetry and ask more consequential questions: What transactions are at risk? How does latency affect conversion? What’s the revenue impact of this degradation? Which customers should receive proactive outreach? How do we prioritize incidents during peak business windows?
CIOs want to know not only what’s happening, but what it costs. Pacvue, which helps brands manage and automate campaigns across marketplaces, provides a clear demonstration of this shift. Woodside’s team analyzes how operational metrics correlate with business outcomes, especially churn. “When your MTTR [mean time to resolve] drops, your churn rate drops,” he says. Similarly, reducing bugs in production increases retention rates. Automated observability feeds the CI/CD pipeline, reducing bug counts, stabilizing features, and improving customer retention. For Woodside, this is bottom-line impact — not theoretical, but measurable.
Oracle’s Nigam, who works directly with enterprises designing cloud and observability architectures, explains the structure behind this linkage. SLIs (service-level indicators), such as latency or error rates, feed into SLOs (service level objectives), which in turn support SLAs (service level agreements). “Leadership and customers see SLAs,” she says, “but SLAs come directly from baseline telemetry.” When that telemetry isn’t collected, or worse, collected inconsistently, organizations can’t quantify business risk.
