A KPI (Key Performance Indicator) in Kudzu Canopy NOC is a computed value that describes a measurable aspect of your network’s health — device reachability, gateway uptime, RF coverage quality, airtime utilization, or any other operational signal. KPIs are produced by analysis algorithms, stored as analysis data, and consumed by dashboards, alarms, reports, maps, and AI workflows.
Raw network traffic tells you what happened, but KPIs tell you what it means. A single uplink record is just a data point; a KPI that tracks how many devices went silent in the last 24 hours is an operational signal you can act on. By continuously computing KPIs, the platform transforms high-volume telemetry into a manageable set of indicators that surface the conditions you care about most.
An analysis algorithm is a scheduled program that reads network data and emits metrics. Algorithms are written in JavaScript and run on configurable time windows — hourly, daily, weekly, or monthly. Some algorithms use rolling analysis, meaning they refresh frequently while looking back over a longer sliding window to smooth out transient fluctuations.
Each algorithm belongs to a node in the problem taxonomy, which determines where its metrics appear in the Metrics Dashboard and Alarms view.
An analysis instance is the configuration that enables one or more analysis algorithms for a specific network. When you import an algorithm through the Monitoring KPIs configuration page, the platform creates an analysis instance that controls:
A metric is the output of an analysis algorithm for a single run. It can be:
Metrics carry entity references so you can always drill from a number to the underlying evidence on a map or in inventory.
Analysis data is the immutable result frame stored after each algorithm run. It contains the computed metrics, their entity references, metric ratios, tags, alerts, and geotags. Analysis data serves as the evidence layer used by:
| Consumer | How it uses analysis data |
|---|---|
| Metrics Dashboard | Displays current and historical KPI values with charts and map focus. |
| Alarms | Compares metric values against thresholds to determine alarm state. |
| Reports | Includes metric evidence and trends in generated documents. |
| Map layers | Renders geotags as hexagonal cells, points, or zone highlights. |
| AI Insights | Uses metric history and references as context for problem identification and solution generation. |
Analysis algorithms run on defined time windows. The choice of window affects how responsive a metric is to change:
| Window | Typical use |
|---|---|
| Hourly | Fast-moving indicators like gateway connectivity drops or sudden device silence. |
| Daily | Baseline health metrics like device activity ratios or coverage scores. |
| Weekly | Trend indicators like week-over-week capacity shifts. |
| Monthly | Long-term planning metrics like deployment progress or seasonal patterns. |
Rolling analyses combine frequent execution with a longer lookback period, giving you near-real-time sensitivity without reacting to every momentary spike.
The analysis pipeline follows a clear path:
Network data → Analysis algorithm → Metric → Alarm evaluation
→ Dashboard display
→ Map layer rendering
→ Report inclusion
→ AI insight context
Once a metric is computed, it simultaneously feeds every downstream consumer. You do not need to configure separate pipelines for dashboards, alarms, and AI — they all draw from the same analysis data.
Before metrics appear on the Metrics Dashboard, you must enable reporting for the network and import at least one analysis algorithm through the Monitoring KPIs configuration page.