Service Requests Insights and Optimisation
Description
This recipe offers clear visibility into service request patterns and operational performance. It tracks monthly request volumes, request type distribution, approval times, and rejection trends, automatically generating concise, actionable recommendations. Users can quickly identify workload spikes, dominant request categories, slow approvers, and periods with increased rejection rates. The recipe also highlights optimisation opportunities such as automation candidates, SLA adjustments, and improvements to request templates or triage steps. Insights are presented through simple charts and a consolidated suggestions table, enabling faster, data-driven decisions.
Details
Service request data is typically large and scattered across multiple systems, creating blind spots in volume trends, approval performance, and rejection patterns; this recipe consolidates logs and computes lifecycle metrics so teams can identify bottlenecks, measure approver efficiency, and prioritize operational improvements.
Business value
- Faster identification of bottlenecks and high-rejection workflows
- Data-driven resource and capacity planning based on request trends
- Systematic measurement of approver performance and SLA adherence
Who benefits
- Service Operations Managers
- Workflow Owners / Process Governance Teams
- Business Unit Owners monitoring request volumes
What you receive
How the recipe works (guided flow)
Step 1 — Read source data
Ingest and consolidate ticket tables (total tickets created, service request types, approver timestamps, and rejection flags) to provide a single source of truth for downstream computations.
Step 2 — Compute metrics
Aggregate monthly/weekly counts, compute distributions by request type and priority, calculate average per-approver resolution time, and derive monthly rejection counts.
Step 3 — Output visualizations & tables
Produce summary tables and charts (pie for types, bar charts for monthly volumes, rejections, and approver timings) to surface trends and actionable issues.
Sample insights (illustrative)
| Insight Category | What the recipe discovered | Business Impact |
|---|---|---|
| Monthly volume peak | A 20% spike in requests occurred in the last quarter concentrated in two request types. | Reallocate resources during peak months to avoid SLA breaches. |
| High rejection cluster | One workflow shows a sustained high rejection rate linked to inconsistent template usage. | Standardize templates and retrain approvers to reduce repeat work. |
| Approver delay hotspot | Average approval time for a subset of approvers is double the organizational average. | Introduce SLAs and monitor individual approver performance to improve throughput. |
Make sure the following ingredients are available in your workspace:
- Connected datasets are crawled and profiled
- Columns have distinct counts and top values generated