docs/content/copilot/reference/copilot-usage-metrics/reconciling-usage-metrics.md

title, shortTitle, intro, permissions, versions, topics, contentType, allowTitleToDifferFromFilename, redirect_from

title	shortTitle	intro	permissions	versions	topics	contentType	allowTitleToDifferFromFilename	redirect_from
Reconciling Copilot usage metrics across dashboards, APIs, and reports	Reconciling Copilot usage metrics	Understand how {% data variables.product.prodname_copilot_short %} usage metrics differ between dashboards, APIs, and exported reports.	Enterprise owners and billing managers	feature copilot	Copilot	reference	true	/early-access/copilot-metrics/dashboards/best-practices-for-correlating-usage-metrics /copilot/reference/reconciling-usage-metrics

{% data reusables.copilot.usage-metrics-preview %}

The {% data variables.product.prodname_copilot_short %} usage metrics dashboard, APIs, and export files all use the same underlying telemetry data, but they aggregate and present it differently. Understanding these differences helps you reconcile numbers across sources and trust your analysis when preparing internal reports.

Prerequisite

{% data variables.product.prodname_copilot_short %} usage metrics depend on telemetry from users’ IDEs. If a developer has disabled telemetry in their IDE, their {% data variables.product.prodname_copilot_short %} activity will not appear in the dashboard, API reports, or exported data.

If you notice missing users or unexpectedly low adoption numbers, verify IDE telemetry settings before troubleshooting other causes.

Metric alignment

The dashboard and APIs use shared definitions for key metrics:

Concept	Dashboard metric	API or export field	Notes
Active users	Daily/weekly/total active users	user_initiated_interaction_count > 0	A user is considered active if they interacted with {% data variables.product.prodname_copilot_short %} in their IDE on that day.
Acceptance rate	Code completion acceptance rate	code_acceptance_activity_count ÷ code_generation_activity_count	Both sources calculate acceptance rate the same way, though rounding may differ.
Agent adoption	Agent adoption chart	totals_by_feature where feature = “agent”	Reflects users who interacted with the {% data variables.copilot.copilot_agent_short %}.
Language usage	Language usage charts	totals_by_language_feature or totals_by_language_model	The dashboard visualizes these aggregated fields.

For complete field descriptions, see AUTOTITLE.

Discrepancies between reports

Small differences between dashboard data, API reports, and exports are expected. These variations are usually caused by differences in time windows, scope, or data freshness.

Time windows

Each data source aggregates data differently.

Source	Time window	Aggregation method
Dashboard	28-day rolling window	Metrics are aggregated continuously over the past 28 days to smooth fluctuations.
APIs	Daily	Each record represents a single day per user, enabling daily trend analysis.
NDJSON exports	Daily	Mirrors API output for BI tools and long-term reporting.

Aligning your reporting period with the dashboard’s 28-day window ensures consistent comparisons.

Delayed telemetry

Because IDE telemetry is processed asynchronously, data for recent days may appear incomplete or missing. Data typically finalizes within three full UTC days. Apparent drops in recent daily metrics often resolve once telemetry is fully processed.

Export timing

NDJSON files reflect data available at the time of export. If a file is downloaded before new telemetry is processed, the data may lag behind the dashboard or API. Re-exporting the file after the three-day window provides the most accurate view.

Unknown values

The value Unknown appears in some API or export breakdowns when telemetry from the IDE client lacks sufficient detail to categorize the activity. This is expected behavior and does not indicate missing data.

Breakdown	Explanation
Language	Shown as Unknown when the IDE cannot identify the programming language of the active file.
Feature	Appears when an older client sends a generic event without specifying a chat mode (for example, chat_panel_unknown_mode).
Model	Appears when the event lacks information identifying the model used. Some internal models (for example, gpt-4o-mini) may appear alongside Unknown when used for non-user-facing operations such as summarization or intent detection.

Unknown values are excluded from dashboard visualizations but appear in API and NDJSON data for completeness. The amount of Unknown data decreases as users upgrade to newer IDE and extension versions that send richer telemetry.