We’re evaluating whether to invest in Process Mining capabilities versus enhancing our existing Performance Analytics dashboards for incident management workflow analysis. Our current setup uses custom PA dashboards that track metrics like average resolution time, reassignment counts, and SLA compliance by category and team.
Process Mining promises to automatically discover actual process flows and identify bottlenecks we might not know exist. However, the setup complexity and licensing costs are significant considerations. From what I understand, Process Mining provides workflow traceability that shows the actual path each incident takes, while our analytics dashboards show aggregate metrics.
For those who’ve used both approaches, what’s the real-world value difference? Does Process Mining reveal insights that traditional analytics dashboards miss, or is it mainly a visualization upgrade? Particularly interested in experiences with initial Process Mining setup effort and how the analytics dashboard comparison played out in practice.
From a cost-benefit perspective, start with your current PA capabilities and add targeted enhancements before jumping to Process Mining. Build dashboards that track sequential activities, not just aggregate metrics. Create flow-based visualizations showing incident progression through assignment groups. If you still can’t identify bottlenecks or explain performance variations after enhancing PA, then Process Mining becomes justified. For many organizations, improved PA dashboards with proper flow tracking is sufficient.
Having implemented both approaches across multiple ServiceNow instances, here’s my perspective on the three key areas:
Process Mining Setup Complexity:
The initial setup requires careful planning but is manageable with proper preparation. You’ll need to ensure your incident table has comprehensive activity logging - every state change, reassignment, and update must be captured with accurate timestamps and user information. The Tokyo release has improved Process Mining connectors, making data extraction more straightforward. Expect 2-3 weeks for initial configuration: one week for data mapping and validation, one week for process model refinement, and another week for establishing baseline metrics and training your team. The key success factor is having clean, consistent data. If your incident workflow has inconsistent activity naming or missing timestamps, address that first before attempting Process Mining deployment.
Analytics Dashboard Comparison:
Traditional Performance Analytics dashboards excel at tracking predefined metrics and trends over time. They’re perfect for monitoring SLA compliance, team performance, and volume trends. However, they have a fundamental limitation: they only show what you think to measure. Process Mining operates differently - it reconstructs the actual end-to-end process flow from event logs, revealing the real process execution patterns. In practice, Process Mining consistently uncovers 3-5 significant bottlenecks that weren’t visible in standard dashboards because they involve sequential patterns or conditional paths rather than simple metric thresholds. For instance, you might discover that incidents assigned to Team A, then reassigned to Team B, then back to Team A have 3x longer resolution times - a pattern that aggregate reassignment metrics would miss entirely.
Workflow Traceability Value:
This is where Process Mining truly differentiates itself. The ability to trace individual incident journeys and aggregate them into process variants provides insights impossible to achieve with traditional analytics. You can identify that your incident process has 47 different execution variants when you designed for 5 standard paths. You can see exactly where delays accumulate, which transitions cause the most waiting time, and which process paths correlate with SLA violations. This granular traceability enables root cause analysis at the process level rather than just symptom monitoring.
For your situation with incident management, I’d recommend starting with Process Mining if you have budget flexibility. The discovery capabilities will likely reveal optimization opportunities that justify the investment within 6-12 months through reduced resolution times and improved resource allocation. Keep your PA dashboards for operational monitoring, but use Process Mining for strategic process improvement initiatives.
I’d argue that Process Mining and PA dashboards serve different purposes and complement each other. PA is great for monitoring known metrics and tracking KPIs over time. Process Mining excels at discovery - finding problems you didn’t know to look for. We use Process Mining quarterly to identify new bottlenecks and process variants, then build PA dashboards to monitor those specific issues ongoing. The combination is more powerful than either alone.