I wanted to share our successful implementation of a real-time process analytics dashboard for insurance claims operations using Pega 8.6. Our business challenge was lack of visibility into claim processing bottlenecks and SLA compliance issues - by the time weekly reports were generated, it was too late to take corrective action.
We built a comprehensive dashboard that gives claims managers live visibility into operations with role-based access for different user levels. The solution uses Pega’s data flows to aggregate case metrics in near real-time and presents them through configurable analytics widgets. Key metrics tracked include claims in process, average handling time, SLA compliance by claim type, adjuster workload distribution, and bottleneck identification.
The implementation took about 6 weeks with a team of 2 developers. We’re now seeing 40% faster identification of process bottlenecks and 25% improvement in SLA compliance since managers can intervene proactively. The live analytics widgets update every 5 minutes, which has been a game-changer for our operations team.
This sounds exactly like what we need! Can you share more details about the data flow configuration? We’ve struggled with performance when trying to aggregate metrics across thousands of active claims. How did you handle the data volume without impacting system performance?
Impressive results! I’m particularly interested in the role-based dashboard access you mentioned. Did you create different dashboard layouts for different roles, or is it a single dashboard with conditional visibility? Also, how did you handle the requirement for managers to see team metrics while individual adjusters should only see their own workload?
Great question on performance. We use incremental data flows that process only changed cases since the last run, rather than recalculating everything. The data flows run every 5 minutes and update summary tables that the dashboard queries. We also implemented data aging to archive metrics older than 90 days. This keeps the active dataset manageable - we’re processing about 15,000 active claims and the data flows complete in under 2 minutes.
We used mostly standard Pega analytics widgets - bar charts for workload distribution, line charts for SLA trends over time, and KPI widgets for headline metrics. The out-of-box widgets were sufficient for our needs and kept development time down. For drill-down, we configured each widget to link to filtered report definitions. Clicking a bar in the workload chart, for example, opens a report showing all claims assigned to that adjuster. This uses Pega’s standard navigation rather than custom development.