Hello Community,
We are observing a significant variance in duration metrics between reporting views. The environment details are as follows:
- Platform Version: 23.4.0
- Region: AWS US-East-1
- Organization ID: [REDACTED]
The specific observation involves the interactionDuration metric. Real-time dashboards display a duration of 120 seconds for a specific interaction batch. Historical data via API shows 90 seconds for the same time window and filter criteria. This discrepancy causes validation failures during quarterly reporting audits.
Reference was made to Resource Center Article ID 00015678 regarding Data Consistency in Genesys Cloud Analytics. The documentation suggests Real-time metrics may lag or calculate differently than Historical aggregations.
Requesting assistance to confirm the calculation logic for interactionDuration. Specifically, does Real-time use eventTime while Historical uses endTime?
Investigating the ingestion pipeline latency could explain this variance. Network jitter often impacts how quickly interaction records reach the reporting queue. Checked the /analytics/v2/jobs/interactions/{jobId}/status endpoint recently and observed high retry counts during the affected window.
The issue usually stems from delayed delivery of event data to the analytics service rather than a calculation error. Running traceroute results between the on-premises edge and the Genesys Cloud ingestion point shows latency spikes exceeding 200ms during peak hours. This delay prevents real-time aggregation from finalizing before the historical snapshot is taken.
Reviewing the API response headers for X-Request-Id can confirm if the request hit a different shard than the source of truth. Ensure the network path between the reporting client and Genesys Cloud remains stable.
The root cause lies in how duration is calculated for these two data sources. Real-time reports calculate duration based on eventTime (interaction start). Historical reports aggregate from endTime (interaction end) minus startTime. If a wrap-up block executes after the call disconnects, this creates a gap.
To align the metrics, adjust the query body to explicitly filter by endTime. The following JSON payload ensures accurate aggregation matching historical definitions:
{
“interval”: [1704067200, 1704153600],
“metrics”: [
{
“name”: “interactionDuration”,
“aggregationType”: “SUM”
}
],
“groupBy”: [“endTime”]
}
This forces the system to account for the full lifecycle of the interaction. Verify that the wrapUp time is included in the filter parameters if using custom Architect flows.