IIoT, SCADA & Edge Connectivity calculator

OT Data Completeness Calculator

Estimate complete OT tag samples per period. Enter tags published per polling cycle, planned cycles in the period, the share of polls arriving in the historian (delivery rate), and the share of arriving polls within quality bounds (in-spec rate). The calculator returns the gross sample count, complete sample count, and the loss buckets.

What this calculator does

  • Estimate the count of OT tags landing complete in the historian per period from tags published per polling cycle, planned cycles in the period, the share of polls that arrive (delivery rate), and the share of arriving polls that are within quality bounds.
  • Use it when an analytics lead, MES owner, or historian admin needs a clean count of usable tag samples in a period before signing off on a model rebuild or KPI dashboard.
  • It returns the count of tag samples that landed in the historian complete and in-spec in the period.

Formula used

  • Gross tag samples = tags per cycle × planned cycles
  • Complete tag samples = gross samples × delivery rate × in-spec quality rate

Inputs explained

  • Tags published per polling cycle: Use the count of tags configured to publish per polling cycle in the historian polling group.
  • Planned polling cycles in the period: Use planned cycles for the period (60 cycles per minute at 1 second polling).
  • Tag delivery rate: Use the share of polls that actually arrived in the historian on time (typical 99 to 99.99 percent on a healthy system; lower if MQTT sessions drop or store-and-forward is delayed).
  • In-spec quality rate: Use the share of arriving polls that passed validation (not BAD, not stuck, within engineering range), from the historian validation counter.

How to use the result

  • Use it before a model rebuild, before publishing a KPI dashboard, or any time a downstream consumer (MES, analytics, finance) is debating whether the OT data is trustworthy.
  • Completeness does not equal correctness. A tag can deliver complete and in-spec samples but be calibrated wrong; pair with sensor calibration workload for a calibration view.

Common questions

  • What is delivery rate vs quality rate? Delivery rate is the share of polls that arrived (network and broker did their job). Quality rate is the share of arriving polls that passed validation (within range, not stuck, not BAD).
  • What targets are realistic? Mature plants run 99.5 to 99.9 percent delivery and 97 to 99 percent in-spec. Below 95 percent on either is usually a project trigger.
  • How is this different from machine data capture rate? This is a tag-level view. Machine data capture rate is a machine-level view (did the machine report at all). Use both.
  • Should I include manual entry tags? No. Scope to automated polled tags. Manual entry has a separate quality model.

Last reviewed 2026-05-12.