executive-dashboard

Executive Dashboard

Overview

This skill generates executive-level ITSM KPIs and dashboard data for ServiceNow. It enables you to:

• Calculate MTTR (Mean Time to Resolve) and MTBF (Mean Time Between Failures)
• Generate incident resolution and first contact resolution rates
• Aggregate data for executive dashboards
• Compare performance across time periods
• Identify trends requiring executive attention

When to use: For preparing executive briefings, board reports, monthly IT performance reviews, or automated dashboard data generation.

Prerequisites

• Roles: report_admin, itil, or analytics_admin
• Access: Read access to incident, problem, change_request, and task tables
• Knowledge: Understanding of ITSM KPI definitions and organizational targets
• Data: Minimum 30 days of historical data for meaningful metrics

Procedure

Step 1: Calculate Mean Time to Resolve (MTTR)

MTTR measures the average time from incident creation to resolution.

Using MCP (Claude Code/Desktop):

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: state=6^resolved_atISNOTEMPTY^resolved_atONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,number,sys_created_on,resolved_at,priority,category
  limit: 1000

Using REST API:

GET /api/now/table/incident?sysparm_query=state=6^resolved_atISNOTEMPTY^resolved_atONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)&sysparm_fields=sys_id,number,sys_created_on,resolved_at,priority,category&sysparm_limit=1000

Calculate MTTR:

// For each incident:
// Resolution Time = resolved_at - sys_created_on

// MTTR = Sum of all Resolution Times / Count of Resolved Incidents

// Example calculation (in hours):
Total Resolution Time: 2,400 hours
Resolved Incidents: 150
MTTR = 2,400 / 150 = 16 hours

MTTR by Priority (Target Benchmarks):

Priority	Target MTTR	Industry Benchmark
P1 - Critical	< 4 hours	2-4 hours
P2 - High	< 8 hours	4-8 hours
P3 - Moderate	< 24 hours	8-24 hours
P4 - Low	< 72 hours	24-72 hours

Step 2: Calculate Mean Time Between Failures (MTBF)

MTBF measures reliability - average time between recurring incidents for the same CI or service.

Query CI-Related Incidents:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: cmdb_ciISNOTEMPTY^sys_created_onONLast 90 days@javascript:gs.daysAgoStart(90)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,number,cmdb_ci,sys_created_on,category
  limit: 2000
  order_by: cmdb_ci,sys_created_on

Calculate MTBF per CI:

// Group incidents by CI
// For CIs with multiple incidents:
// MTBF = Total operating time / (Number of failures - 1)

// Example for Server-001:
// Incident 1: Jan 5
// Incident 2: Jan 20
// Incident 3: Feb 10
// Time between: 15 days + 21 days = 36 days / 2 = 18 days MTBF

MTBF Target Benchmarks:

Service Tier	Target MTBF
Tier 1 (Critical)	> 30 days
Tier 2 (Important)	> 14 days
Tier 3 (Standard)	> 7 days

Step 3: Calculate Resolution Rates

First Contact Resolution (FCR) Rate:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: state=6^resolved_atONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)^reassignment_count=0
  fields: sys_id,number
  limit: 1000

Total Resolved (Last 30 Days): Query count of all resolved incidents
FCR Count: Query count with reassignment_count=0
FCR Rate = (FCR Count / Total Resolved) * 100

Reopen Rate:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: reopen_count>0^sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,number,reopen_count
  limit: 500

Reopen Rate = (Incidents with reopen_count > 0 / Total Resolved) * 100
Target: < 5%

Step 4: Incident Volume Metrics

Total Incident Volume:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,priority,category,state
  limit: 5000

Volume by Priority: Process results to count by priority field.

Backlog Analysis:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: active=true^state!=6^state!=7
  fields: sys_id,priority,sys_created_on,assigned_to
  limit: 1000

Backlog Metrics:
- Total Open: Count of active incidents
- Aging > 7 days: Filter by sys_created_on
- Unassigned: Filter by assigned_toISEMPTY

Step 5: Change Success Rate

Query Completed Changes:

Tool: SN-Query-Table
Parameters:
  table_name: change_request
  query: state=3^closed_atONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,number,close_code,type
  limit: 500

Close Code Analysis:

Close Code	Category
successful	Success
successful_issues	Success with Issues
unsuccessful	Failure
cancelled	Cancelled

Change Success Rate = (Successful + Successful with Issues) / (Total - Cancelled) * 100
Target: > 95%

Step 6: Problem Management Metrics

Problems Created vs. Resolved:

Tool: SN-Query-Table
Parameters:
  table_name: problem
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,number,state,known_error,first_reported_by_task
  limit: 200

Known Error Database (KEDB) Growth:

Tool: SN-Query-Table
Parameters:
  table_name: problem
  query: known_error=true
  fields: sys_id,number,sys_created_on
  limit: 500

Step 7: Customer Satisfaction (CSAT)

If using ServiceNow survey:

Tool: SN-Query-Table
Parameters:
  table_name: asmt_assessment_instance
  query: metric_type.nameSTARTSWITHIncident^sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,state,percent_answered
  limit: 500

Step 8: Generate Aggregated Report Data

Using GlideAggregate Pattern (via Background Script):

Tool: SN-Execute-Background-Script
Parameters:
  script: |
    var ga = new GlideAggregate('incident');
    ga.addQuery('sys_created_on', '>=', gs.daysAgo(30));
    ga.addAggregate('COUNT');
    ga.addAggregate('COUNT', 'priority');
    ga.groupBy('priority');
    ga.query();

    var results = [];
    while (ga.next()) {
      results.push({
        priority: ga.getValue('priority'),
        count: ga.getAggregate('COUNT')
      });
    }
    gs.info('Priority Distribution: ' + JSON.stringify(results));
  description: Aggregate incident counts by priority

Tool Usage

MCP Tools Reference

Tool	When to Use
SN-Query-Table	Primary tool for querying metrics data
SN-Natural-Language-Search	Natural language queries for quick insights
SN-Execute-Background-Script	Complex aggregations using GlideAggregate

REST API Reference

Endpoint	Method	Purpose
/api/now/table/incident	GET	Query incident data
/api/now/table/change_request	GET	Query change data
/api/now/table/problem	GET	Query problem data
/api/now/stats/{table}	GET	Aggregate statistics

Key Fields for Metrics

Metric	Source Table	Key Fields
MTTR	incident	sys_created_on, resolved_at
FCR	incident	reassignment_count
Reopen Rate	incident	reopen_count
Change Success	change_request	close_code, state
MTBF	incident	cmdb_ci, sys_created_on

Best Practices

• Consistent Time Windows: Always use the same time period for comparisons (30 days recommended)
• Business Calendar: Exclude weekends/holidays if measuring business time
• Baseline First: Establish baselines before setting targets
• Segment Data: Break down by priority, category, and team for actionable insights
• Automate Collection: Schedule regular data pulls for dashboard refresh
• ITIL Alignment: Align KPIs with ITIL Continual Service Improvement (CSI)

Troubleshooting

"MTTR seems too high"

Cause: Including paused time or non-business hours Solution: Use business_duration field if available, or filter by business schedule

"Resolution counts don't match reports"

Cause: Different date fields being used (resolved_at vs closed_at vs sys_updated_on) Solution: Standardize on resolved_at for resolution metrics

"MTBF calculation returning errors"

Cause: CIs with single incident cannot calculate MTBF Solution: Filter to CIs with 2+ incidents; use "N/A" for single-incident CIs

"Change success rate very low"

Cause: Cancelled changes being counted as failures Solution: Exclude state=cancelled from total; only count closed changes

Examples

Example 1: Weekly Executive Summary

Objective: Generate weekly IT performance summary

Queries to Execute:

1. Incident Volume:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 7 days@javascript:gs.daysAgoStart(7)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,priority
  limit: 500

1. Resolved Incidents:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: resolved_atONLast 7 days@javascript:gs.daysAgoStart(7)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,sys_created_on,resolved_at,priority
  limit: 500

1. Current Backlog:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: active=true
  fields: sys_id,priority,sys_created_on
  limit: 500

Output Report:

=== WEEKLY IT PERFORMANCE SUMMARY ===
Week Ending: [Date]

INCIDENT MANAGEMENT:
| Metric | This Week | Last Week | Change |
|--------|-----------|-----------|--------|
| Created | 145 | 132 | +10% |
| Resolved | 152 | 140 | +9% |
| Backlog | 48 | 55 | -13% |
| MTTR (hours) | 14.2 | 15.8 | -10% |

PRIORITY BREAKDOWN:
| Priority | Created | Resolved | Backlog |
|----------|---------|----------|---------|
| P1 | 3 | 4 | 1 |
| P2 | 22 | 25 | 8 |
| P3 | 78 | 80 | 25 |
| P4 | 42 | 43 | 14 |

KEY HIGHLIGHTS:
- MTTR improved by 10% (14.2 hours vs 15.8 hours)
- Backlog reduced by 13% (48 vs 55)
- P1 incidents: 3 created, all resolved or in progress

ITEMS REQUIRING ATTENTION:
- None - all metrics within target

Example 2: Monthly KPI Dashboard Data

Objective: Generate all KPIs for monthly dashboard

Execute All Queries in Parallel:

// Query 1: Total Incidents
Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,priority,category,state,reassignment_count,reopen_count,sys_created_on,resolved_at
  limit: 2000

// Query 2: Changes
Tool: SN-Query-Table
Parameters:
  table_name: change_request
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,type,close_code,state
  limit: 500

// Query 3: Problems
Tool: SN-Query-Table
Parameters:
  table_name: problem
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,state,known_error
  limit: 200

Calculated Dashboard Data:

{
  "period": "2024-01-01 to 2024-01-31",
  "incident_management": {
    "total_created": 612,
    "total_resolved": 598,
    "mttr_hours": 14.5,
    "mttr_by_priority": {
      "P1": 3.2,
      "P2": 7.8,
      "P3": 18.4,
      "P4": 48.2
    },
    "fcr_rate": 72.5,
    "reopen_rate": 3.2,
    "backlog": 52
  },
  "change_management": {
    "total_changes": 89,
    "success_rate": 97.8,
    "by_type": {
      "standard": 45,
      "normal": 38,
      "emergency": 6
    }
  },
  "problem_management": {
    "problems_created": 12,
    "problems_resolved": 8,
    "known_errors_added": 5
  },
  "trends": {
    "incident_volume_change": "+5%",
    "mttr_change": "-8%",
    "backlog_change": "-12%"
  }
}

Example 3: Board-Level Summary

Objective: High-level summary for board presentation

=== IT SERVICE MANAGEMENT - BOARD SUMMARY ===
Period: Q4 2024

EXECUTIVE SUMMARY:
IT Service Management delivered 97% SLA compliance in Q4,
exceeding the 95% target. Major incident count reduced by
25% compared to Q3.

KEY PERFORMANCE INDICATORS:
                          Q4 2024   Q3 2024   Target   Status
Service Availability       99.8%     99.5%    99.5%    PASS
SLA Compliance             97.0%     94.2%    95.0%    PASS
MTTR (hours)               12.4      15.2     16.0     PASS
Change Success Rate        98.2%     96.8%    95.0%    PASS
Customer Satisfaction      4.2/5     4.0/5    4.0/5    PASS
Major Incidents            3         4        <5       PASS

YEAR-OVER-YEAR COMPARISON:
| Metric | 2024 | 2023 | Improvement |
|--------|------|------|-------------|
| Total Incidents | 7,245 | 8,102 | -11% |
| MTTR | 13.2 hrs | 18.4 hrs | -28% |
| SLA Compliance | 96.5% | 92.1% | +4.4% |

INVESTMENTS & OUTCOMES:
- AI-powered triage: 15% reduction in assignment time
- Self-service portal: 22% ticket deflection
- Automation: 400+ hours saved monthly

RISKS & MITIGATIONS:
- Aging infrastructure causing 30% of P1 incidents
  Mitigation: Data center refresh approved for Q2 2025

Related Skills

• reporting/sla-analysis - Detailed SLA performance tracking
• reporting/trend-analysis - Incident trends and patterns
• itsm/incident-lifecycle - Full incident management
• itsm/problem-analysis - Problem management metrics

References

• ServiceNow Performance Analytics
• ITIL KPIs and Metrics
• IT Service Management Benchmarks