Signals That Matter: Turning Telemetry Into Executive Decisions by Mark Hewitt
Enterprises are drowning in signals. Logs, metrics, traces, alerts, tickets, security events, data quality checks, and compliance reports generate enormous volumes of information. The problem is not that the enterprise lacks telemetry. The problem is that telemetry is rarely translated into executive decisions with speed and confidence. Engineering Intelligence must be designed as a signal-to-decision system.
The difference between data and signals
Most telemetry is data exhaust. It is recorded activity without interpretation. A signal is different. A signal is information that changes a decision. Executives should demand a signal strategy that answers:
Which signals predict failure before the business is impacted?
Which signals indicate accumulating risk and fragility?
Which signals indicate controls are failing or drifting?
Which signals indicate that AI behavior is becoming unreliable?
If telemetry does not inform a decision, it is noise.
The executive signal categories that matter
A practical signal strategy includes four categories.
Continuity signals. Indicators of service health in business terms. Performance degradation, error trends, dependency health, recovery readiness.
Change risk signals. Indicators that the enterprise is changing unsafely. Change failure rate, rollback frequency, risk-weighted change volume, rising exception rates.
Trust signals. Indicators of data and decision integrity. Data quality and drift, lineage completeness, authorization compliance, inconsistent definitions.
Control signals. Indicators of governance effectiveness. Policy enforcement coverage, audit evidence completeness, access control drift, unmanaged exceptions.
These categories translate technical telemetry into executive control.
Turning signals into decisions requires context
Signals without context create false urgency. Engineering Intelligence provides context by connecting signals to:
critical pathways
dependency maps
ownership and accountability
recent changes and deployments
policy constraints and risk tiers
historical patterns and drift trends
This is how signals become actionable and governable.
The signal-to-decision loop
Executives should expect the enterprise to run a disciplined loop.
Detect. Identify signals early, including weak signals that indicate drift and fragility.
Interpret. Correlate signals across domains, then attach business pathway context.
Decide. Apply policy, risk tiering, and oversight models to determine the appropriate response.
Act. Execute response through runbooks, automation, or supervised agent workflows.
Learn. Measure outcomes, update thresholds, and improve controls.
This loop is decision intelligence. It is also resilience engineering.
A practical starting point
Executives can begin improving signal quality with four actions.
Reduce signal volume by defining decision relevance. Every executive-level signal should map to a decision or a control boundary.
Define leading indicators. Focus on drift, dependency fragility, and change risk rather than lagging incident measures alone.
Standardize thresholds and escalation. Avoid ad hoc escalation driven by who is loudest. Escalate based on measured signals.
Build a compact executive view. Leaders should see a small set of indicators tied to critical pathways: continuity, change risk, trust, and control.
Take Aways
Enterprises do not lack telemetry. They lack a reliable method for turning telemetry into decisions.
Engineering Intelligence makes signals actionable by adding context, enforcing governance boundaries, and connecting decisions to coordinated action. That is how leaders reduce surprises and increase operational confidence.