We’re evaluating whether to invest in Pega Process Mining or enhance our existing reporting infrastructure for process optimization. Our goal is identifying bottlenecks in our order-to-cash process which spans multiple systems and involves 20+ handoffs.
Currently, we use standard Pega reports and dashboards showing case duration, stage timing, and SLA compliance. These give us aggregate metrics but don’t reveal the actual paths cases take or where delays really occur. Process mining promises to visualize actual process flows and automatically detect variants and bottlenecks.
I’m trying to understand the practical trade-offs around process visualization capabilities, setup effort and ongoing maintenance, and depth of insights each approach provides. Has anyone implemented both approaches and can share real-world experiences? Is process mining worth the investment for bottleneck analysis, or can enhanced traditional reporting achieve similar results with less complexity?
Tom, how do you identify unexpected process variants with traditional reporting? That’s one area where process mining seems to have a clear advantage - discovering paths through the process that weren’t part of the designed flow. Can standard reports surface that kind of insight?
One aspect people overlook: ongoing maintenance. Process mining requires continuous data quality management. If your event logs are inconsistent or incomplete, the discovered processes will be misleading. Traditional reports are more forgiving - you define the metrics once and they’re stable. We spent 20% of our process mining effort just cleaning up data and ensuring consistent activity logging across systems.
We implemented process mining last year. The value isn’t just technical - it’s organizational. When you show executives a visual process flow with bottlenecks highlighted in red, they immediately understand the problem. Traditional reports require explanation and interpretation. Process mining democratizes process analysis. Non-technical stakeholders can explore the data themselves. That engagement drove process improvement initiatives that had stalled for years. The ROI came from organizational adoption, not just technical capability.