Precision and practicality

In early-stage engineering, precision often feels like the ultimate goal. It’s intuitive to design tightly, calibrate carefully, and validate against clear benchmarks. Much of my early work followed this thinking. If a device could consistently pinpoint a position within half a metre 95% of the time, it was a clear success. Complete logs, minimal errors, and precise timing seemed enough proof of stability.

Over time, this perspective evolved. Not because precision itself became less valuable, but because its practical meaning shifted. In real-world scenarios, especially unpredictable ones, precision only matters if it serves the actual operational goal. A perfectly accurate output has limited use if it’s late, unreliable under load, or assumes conditions rarely met in the field.

Measuring what matters

GNSS systems illustrate this well. Positioning accuracy is reported with a set confidence interval, making it appealing to base application logic on this figure. However, real-world deployments showed that this statistic alone didn't capture performance when signals degrade, corrections drop, or obstructions interfere. Field experience demonstrated that drift, delays, and environmental interference often overshadowed theoretical accuracy.

As a result, system-level precision required a rethink. Rather than focusing solely on ideal conditions, we began emphasising consistency and predictable performance under realistic conditions. Metrics shifted to capturing worst-case variability rather than just average accuracy. Application constraints were recalibrated based on real-world testing rather than manufacturer datasheets. Alert timings prioritised predictability over ideal theoretical values.

This was not a compromise - it was a shift toward practical optimisation.

Trade-offs in system design

Design trade-offs aren't just necessary compromises; they often enhance system robustness. In our embedded systems, loosening precision slightly enabled better resilience when conditions degraded. We streamlined event logs, prioritising critical events over comprehensive telemetry. Configuration systems were simplified to favour reliability and integrity over granular flexibility.

Similarly, in backend infrastructure, fault tracking focused not on capturing every potential error, but on reliably escalating the highest-impact issues. The defect reporting process evolved accordingly, shifting from comprehensive logging toward impact-driven telemetry.

These decisions aren't binary choices between "precise" and "practical". They're adjustments in understanding precision within context. Importantly, these refinements emerged not during initial designs but from observing long-term system behaviour in the field.

Engineering beyond tolerances

A challenging yet crucial development in professional engineering involves working beyond static tolerances. Error margins, signal strength thresholds, and retry intervals aren't absolute. They’re influenced by real-world conditions and revised based on ongoing observations.

Systems need to remain predictable under changing conditions. Precision, therefore, is determined less by sensor accuracy alone and more by how the entire system responds when inputs become marginal, invalid, or missing. True reliability emerges not just from precise data but from resilience when data uncertainty is inevitable.

This shift has reshaped how I consider design: focusing not on their proximity to ideal conditions, but on their resilience when actual conditions inevitably deviate from those ideals.

Precision remains important. Yet, increasingly, it's viewed as a final goal rather than a tool. What truly matters is consistent, observable, and contextually appropriate system behaviour.