Looking for advice on resolving a persistent data alignment issue within the Performance dashboard, specifically concerning Digital/Messaging channel metrics. The environment is running Genesys Cloud version 2024.2, and the operational requirement is to track real-time queue activity for WhatsApp and Web Chat channels with sub-minute granularity. However, the aggregated conversation counts displayed in the Queue Performance view diverge significantly from the individual Conversation Detail records retrieved via the standard UI. This discrepancy creates a compliance risk for our SLA reporting, as the dashboard shows a 15% higher volume than the actual processed transcripts in the detailed logs. The issue appears isolated to digital channels, while voice queues remain consistent, suggesting a potential latency or aggregation logic error in the digital data pipeline rather than a general system outage.
The configuration utilizes a standard Omnichannel Flow with no custom API integrations or third-party middleware interfering with the data stream. All agents are assigned to the same skill group, and the routing logic is identical across channels, utilizing the standard “Find Agent” block. Despite this uniformity, the dashboard’s “Handled Conversations” metric for the digital queue frequently spikes during peak hours (09:00-11:00 CET), whereas the agent-level performance reports show stable, lower volumes. This inconsistency forces manual reconciliation of reports, which is unsustainable for daily operations. The expectation is that the high-level dashboard metrics should reflect the sum of individual agent activities without deviation, ensuring accurate business intelligence for resource planning.
Has the community encountered similar synchronization delays between the real-time dashboard aggregates and the historical conversation records for digital channels? The objective is to determine if this is a known limitation of the Performance dashboard’s data refresh rate for messaging protocols or if a specific configuration adjustment is required to align the metrics. Any insights into the underlying data aggregation methodology for digital interactions would be appreciated, as the current variance undermines confidence in the automated reporting tools provided by the platform.