Missed SLAs and other service disruptions cause friction and compromise your service delivery.

Tackle complex processes and drive continual service
improvements with Numerify IT Service Analtyics (ITSA).

IT Service Analytics

Numerify ITSA converts data from your service management processes into key insights. Move beyond simple process KPIs to analyze deep audit logs and complex data sets. Extract unstructured and highly granular data from your CMDB, service catalog, knowledge management system, and other relevant systems of record. Our comprehensive solution serves data-driven IT service management and operations professionals alike.

Incident Analysis

Analyze incident demand and identify peaks and patterns. Evaluate every field captured through your incident process, from CIs to incident location, to ensure you can allocate resources to meet demand. Connect incidents to change using CIs, assets, assignment groups, and business services to identify the worst offenders.
incident analytics
Our natural language processing (NLP) algorithms index and analyze incident descriptions. Text analytics uncover hidden issues beyond the standard structured fields.
top incident keywords
Our Data Orchestration Layer extracts data from audit-log tables to provide an accurate representation of incident history. Review reassignments and recurring patterns, and identify the cause of productivity-killing episodes.
incident analytics dashboard

Service Request Health

Quickly gain visibility into request delays for key services such as onboarding and server provisioning. Identify the top-requested services as well as those that most frequently experience delays. Understand how request variables impact delivery time, visualize fulfillment from end to end, and pinpoint process bottlenecks.
service request trends
Out-of-the-box metrics and dashboards reveal service catalog utilization rates and unpopulated search terms. Use this data to drive self-service and identify areas in need of knowledge management articles.
service request metrics

Change Management

Failed changes can cause delays and a surge in incidents. Slice and dice change failures by attributes such as change type, CI, rollback rate, and business service. Expose patterns in failed or rolled-back changes and isolate configuration dependencies.
unsuccessful change analysis
Increase change compliance and ensure application reliability by identifying emergency change patterns. Detect trends among changes not approved by the change advisory board (CAB). Quantify the impact of these changes by correlating outages and incidents.
successful change implementation

Configuration Analytics

Trade the traditional process view of applications for a fresh perspective from the configuration angle. Identify which CIs cause the highest number of failed changes, outages or major incidents, and missed SLAs — without requiring a fully built CMDB.
configuration item analysis
Our data lake integrates, models, and stores data in a manner optimal for in-depth analysis. Store millions of events and receive an aggregate view of all configurations leading to customer-impacting events. Measure the impact in terms of revenue loss or costs incurred.
customer impacting events

Service Level Integrity

Access real-time dashboards on SLA adherence and review its impact on customer satisfaction. Use your data to create the dialogue needed to hold your vendors accountable. Visualize service provider health, identify improper ticket handling, and quantify the financial impact on your total cost of service and SLAs.
application lifecycle management
Sift through SLA data to pinpoint the root causes of frequently missed SLA targets. Identify the key causes of escalations and recurring incidents by analyzing audit log data. View escalation patterns or use text analytics to reveal commonly escalated categories, then address relevant gaps in training.
vendor performance

Service Desk Resources

Unite data from multiple sources for a complete picture of workloads across service management and project tasks. Uncover which agents are overloaded and redistribute workloads more evenly.
employee workloads