ServiceNow Analytics — Operations & Service Management

Active Backlog Queue Volume

Total Active Queue

Ranked open tickets volume with numbers inside bars

Active Incidents (INC)

Ranked active technical incidents

Active Tasks (SCTASK)

Ranked active service catalog requests

Queue Ageing & Operational Risk

Average Queue Age

Mean open duration in days (lower is fresher)

Aging Debt (> 30 Days)

Backlog tickets stranded over 30 days

On Hold / Pending Rate (%)

Share of tickets in pending/wait state

Multi-Team Backlog Evolution Trend

Multi-Team Daily Queue Trajectory

Daily active queue evolution across recorded ingestion snapshots

Team Backlog Risk & Workload Matrix

Team Operational Queue Breakdown

Complete queue comparison with Incidents vs Tasks, age breakdown, and blocker ratios

Throughput & Resolution Trajectory

Resolution Throughput Trend

Resolved ticket volume trend aggregated by selected granularity (Daily / Weekly / Monthly)

Year-over-Year (YoY) Resolution Volume

Year-over-Year Monthly Comparison (2025 vs 2026)

Monthly ticket resolution volume comparison (2025 vs 2026)

Historical Performance & MTTR Breakdown

Performance & MTTR Breakdown

Volume and Mean Time to Resolve (MTTR) broken down by selected granularity

Throughput by Team & Technician

Resolved Volume by Team

Resolved Volume by Technician

Fulfillment & Luxury Brand Distribution

Resolved by Category / Request Type

Resolved by Luxury Maison

Team Resolution Volume

Total Volume (Inc + Task)

Ranked total volume with numbers inside bars

Incidents (INC)

Ranked technical incidents volume

Tasks (SCTASK)

Ranked service catalog requests volume

Resolution Speed & MTTR

Overall MTTR

Average resolution time (all tickets) in days

Incident MTTR

Average resolution time for Incidents (INC)

Task MTTR

Average resolution time for Tasks (SCTASK)

Multi-Team Throughput Trajectory

Multi-Team Comparative Trend

Side-by-side volume comparison over selected period and granularity

Team Performance & Capacity Matrix

Quantitative Metrics & MTTR Breakdown

Full benchmarking breakdown with Incidents vs Tasks volume and distinct MTTRs per team

Contractual SLA & Reaction Targets

Resolution SLA Met %

0.0%

Contractual achievement

High Incidents (P1/P2)

0.0%

Critical SLA compliance

Standard Incidents Met

0.0%

P3 & P4 technical incidents

Catalog Tasks Met

0.0%

Service requests fulfillment

Regional Hub Team Benchmark Filter (Click chips to toggle/deselect):

Team Resolution SLA Compliance Rankings (% Met)

Overall Resolution SLA Met %

Ranked by Resolution SLA % Met across selected teams (Target: 95%)

Incident Resolution SLA Met %

Ranked by Incident SLA % Met (High + Standard Incidents)

Catalog Task SLA Met %

Ranked by Catalog Task Fulfillment SLA % Met

Team Initial Reaction KPI Performance (% Met)

Incident Reaction Met %

Ranked by Incident initial triage/response attainment % Met

Task Reaction Met %

Ranked by Catalog Task initial acknowledgment % Met

Team Volumes & SLA Performance Summary Table

Quantitative Team Performance Matrix (Volumes & SLA Compliance)

Full benchmarking breakdown with Total Volumes, Resolution SLAs, Reaction KPIs, and Breach counts per team

Additional Operational & Diagnostic Indicators

Resolution SLA Compliance Trajectory

Monthly % of SLA targets met (2026 YTD)

SLA Budget Time Consumption

Distribution of tickets by % of allowed SLA time consumed

Reaction Duration Histogram

Distribution of tickets by initial response time bracket

SLA Compliance by Luxury Maison

Resolution SLA Met % and volume per Brand

🤖 AI Operations & Service Management Analyst BETA

Query the full ServiceNow repository (Active Open Backlog, 2025–2026 Resolved Tickets, and Contractual SLAs) in natural language. Powered by real-time deterministic SQLite queries with 100% mathematical accuracy and zero numeric hallucinations.

🟢 Engine: Qwen 2.5 3B (Fast On-Premise)

⚡ Database: Indexed SQLite (135k+ records)

🛡️ Privacy: 100% Local & Confidential

⚠️

Infrastructure & Beta Notice: This engine is currently running locally on lightweight CPU hardware without dedicated neural accelerators (NPU/GPU). Consequently, model schemas and synthesis lengths are streamlined for fast latency. The core data-pipeline, deterministic SQL orchestration, and semantic schemas are fully architected to support complex multi-hop analytics, richer conversational memory, and sub-second response times on enterprise AI compute infrastructure.

Quick Query Starters (Click to execute)

🚨 SLA & Risk

📊 Throughput 2026

⏳ Backlog & Blockers

🏷️ Maisons & Hubs

AI

Hello! I am your ServiceNow AI Operations Analyst.

I can query and analyze the complete IT Helpdesk database: Active Backlog (~1,087 tickets), Historical Resolutions 2025–2026 (88,377 tickets), and Contractual SLA / Reaction KPIs 2026 (46,296 records).

Select any suggested question above or type any operational request below.

Open Backlog & Active Queue

Backlog Snapshot Trend i

Active Backlog Volume by Selected Regional Team i

Lifecycle State Breakdown i

Incidents

Tasks

Pending & Hold Reasons i

Age Stratification (<7d, 7–30d, 30–90d, >90d) i

Incident Categories i

Service Catalog Request Items i

Open Backlog by Assignment Group i

Open Backlog by Technician i

Geography by Country i

Backlog by Luxury Maison i

Total Active Queue i

Active Incidents (INC) i

Active Tasks (SCTASK) i

Average Queue Age i

Aging Debt (> 30 Days) i

On Hold / Pending Rate (%) i

Multi-Team Daily Queue Trajectory i

Team Operational Queue Breakdown i

Resolution Throughput Trend i

Year-over-Year Monthly Comparison (2025 vs 2026) i

Performance & MTTR Breakdown i

MTTR by Incident Priority Level i

Resolved Volume by Team i

Resolved Volume by Technician i

Resolved by Category / Request Type i

Resolved by Luxury Maison i

Total Volume (Inc + Task) i

Incidents (INC) i

Tasks (SCTASK) i

Overall MTTR i

Incident MTTR i

Task MTTR i

Multi-Team Comparative Trend i

Quantitative Metrics & MTTR Breakdown i

Overall Resolution SLA Met % i

Incident Resolution SLA Met % i

Catalog Task SLA Met % i

Incident Reaction Met % i

Task Reaction Met % i

Quantitative Team Performance Matrix (Volumes & SLA Compliance) i

Resolution SLA Compliance Trajectory i

SLA Budget Time Consumption i

Reaction Duration Histogram i

SLA Compliance by Luxury Maison i

Summary generated by Qwen3

Backlog Snapshot Trend

Active Backlog Volume by Selected Regional Team

Lifecycle State Breakdown

Pending & Hold Reasons

Age Stratification (<7d, 7–30d, 30–90d, >90d)

Incident Categories

Service Catalog Request Items

Open Backlog by Assignment Group

Open Backlog by Technician

Geography by Country

Backlog by Luxury Maison

Total Active Queue

Active Incidents (INC)

Active Tasks (SCTASK)

Average Queue Age

Aging Debt (> 30 Days)

On Hold / Pending Rate (%)

Multi-Team Daily Queue Trajectory

Team Operational Queue Breakdown

Resolution Throughput Trend

Year-over-Year Monthly Comparison (2025 vs 2026)

Performance & MTTR Breakdown

MTTR by Incident Priority Level

Resolved Volume by Team

Resolved Volume by Technician

Resolved by Category / Request Type

Resolved by Luxury Maison

Total Volume (Inc + Task)

Incidents (INC)

Tasks (SCTASK)

Overall MTTR

Incident MTTR

Task MTTR

Multi-Team Comparative Trend

Quantitative Metrics & MTTR Breakdown

Overall Resolution SLA Met %

Incident Resolution SLA Met %

Catalog Task SLA Met %

Incident Reaction Met %

Task Reaction Met %

Quantitative Team Performance Matrix (Volumes & SLA Compliance)

Resolution SLA Compliance Trajectory

SLA Budget Time Consumption

Reaction Duration Histogram

SLA Compliance by Luxury Maison