Your call center is running on reporting you cannot validate.
A government services agency had an existing reporting solution for its Amazon Connect contact center. Over time, trust in the data had eroded: metrics could not be independently validated, documentation was missing, support requests went unanswered, and supervisors had no real-time queue visibility. Armely replaced the incumbent solution with a live Power BI DirectQuery wallboard connected to Snowflake, with every metric documented and independently verifiable.
Does this describe your call center reporting?
- You cannot independently validate whether your dashboard metrics match what actually happened
- Known data quality issues persist across reporting cycles with no resolution in sight
- Documentation of metric definitions, calculations, and data logic is incomplete or missing
- Supervisors have no real-time view of queue status or agent availability
- Support requests to your reporting vendor go unanswered or exceed agreed timelines
- When staff turn over, there is no reliable reference for how reporting works or what the numbers mean
What Armely built for a government agency
A government services agency operating a multi-queue Amazon Connect contact center had lost trust in its existing reporting solution across four areas: data accuracy, documentation, responsiveness, and problem resolution.
Armely built a real-time Power BI DirectQuery dashboard, the Agents Wallboard, connected to Snowflake with zero refresh lag. Three service queues are monitored independently with color-coded alert thresholds. Sixteen agent statuses are ranked by operational priority.
Every metric is documented with business definitions, annotated DAX and SQL logic, and ready-to-run validation queries. Supervisors now see live queue and agent status on every load, and staff can validate metrics against source data without developer involvement.
At a glance
| Before | After |
|---|---|
| Dashboard metrics could not be independently validated against source data | Every metric has a ready-to-run Snowflake validation query staff can execute directly |
| Batch reporting introduced refresh lag between dashboard and actual call center state | Power BI DirectQuery connects live to Snowflake on every load with zero refresh lag |
| Supervisors had no real-time queue or agent visibility | Three queues monitored independently with green, amber, and red alert thresholds on wait times |
| Metric definitions, calculation logic, and data transformations were undocumented | Full documentation includes metric definitions, annotated DAX and SQL, architecture diagrams, and a user guide |
| Data accuracy issues around call state, queue classification, and wait time persisted unresolved | Three accuracy issues corrected at the source with documented before-and-after validations |
| Support requests exceeded agreed SLA timelines with no resolution path | Every design decision shared before implementation with written trade-off analysis |