Application Analytics & AI
Engineering Intelligence into Your Application Portfolio
Transform your application estate from a static cost center into a dynamic, self-optimizing asset. Actin’s Application Analytics & AI services embed intelligence directly into your application lifecycle, enabling data-driven modernization decisions, automated optimization, and proactive operational excellence. We move beyond traditional monitoring to deliver predictive insights that drive continuous improvement and measurable business value.
Our Core Application Analytics & AI Services
Gain empirical visibility into how applications are actually used to guide modernization priorities and maximize ROI.
Technical Capabilities:
• Application Dependency Mapping: Automated discovery of application interdependencies using VMware vRealize Network Insight, Azure Migrate, and custom discovery scripts to build a complete application topology.
• User Behaviour Analytics: Implementing Google Analytics, Azure Application Insights, and custom telemetry to track feature adoption, user pathways, and engagement metrics.
• Performance Baseline Correlation: Correlating usage data with performance metrics (Apdex scores, response times) from Dynatrace and AppDynamics to identify pain points impacting user productivity.
• Cost-Per-Transaction Analysis: Integrating usage data with cloud cost management tools (Azure Cost Management, AWS Cost Explorer) to calculate true operational cost metrics.
ROI & Benefits
• Data-Driven Modernization Priorities: Objectively identify candidates for retirement, refactoring, or retention based on actual usage patterns.
• 15-25% Reduction in Licensing Costs by identifying and decommissioning underutilized software assets.
• Improved User Experience by prioritizing modernization efforts on the most critical and poorly performing application components.
Systematically reduce technical debt and improve code quality through automated static and dynamic analysis.
Technical Capabilities
• Static Code Analysis: Automated code quality assessment using SonarQube, Checkmarx, and Fortify to identify security vulnerabilities, code smells, and complexity hotspots.
• Technical Debt Quantification: Measuring and tracking technical debt using CAST Imaging and custom metrics to provide a clear business case for refactoring.
• Automated Code Remediation: Implementing GitHub Copilot, Amazon CodeWhisperer, and Codota to suggest and generate optimized code during development.
• Architecture Conformance Validation: Using Structure101 and NDepend to enforce architectural boundaries and prevent erosion during modernization.
ROI & Benefits
• 20-30% Reduction in Technical Debt through prioritized, automated refactoring recommendations.
• 40-50% Faster Code Reviews with AI-assisted analysis of pull requests.
• Enhanced Maintainability through consistent code quality enforcement and architectural governance.
Shift from reactive firefighting to proactive prevention through AI-powered operational intelligence.
Technical Capabilities
• Anomaly Detection & Forecasting: Implementing machine learning models in Azure Machine Learning and AWS SageMaker to detect deviations from normal performance baselines.
• Automated Root Cause Analysis: Using Moogsoft, BigPanda, and Splunk ITSI to correlate events across the application stack and identify probable causes.
• Intelligent Alert Correlation: Reducing alert fatigue by 70-80% through AI-driven noise reduction and smart grouping in PagerDuty and OpsGenie.
• Capacity Forecasting: Building predictive models using historical performance data to forecast resource requirements and prevent performance degradation.
ROI & Benefits
• 30-40% Reduction in MTTR through automated incident triage and root cause identification.
• 20-30% Fewer Production Incidents through early detection of performance anomalies.
• Improved Resource Utilization through predictive capacity planning and right-sizing.
Connect application performance to business outcomes through process mining and value stream mapping.
Technical Capabilities
• Process Mining Implementation: Deploying Celonis, Minit, or Disco to analyze application logs and discover actual business process flows.
• Value Stream Mapping: Creating digital value stream maps that connect application performance to business process cycle times.
• Bottleneck Analysis: Identifying process constraints and their underlying technical causes through integrated analysis of business and performance data.
• ROI Tracking: Establishing metrics to measure the business impact of modernization initiatives on key process indicators.
ROI & Benefits
• Process Efficiency Improvements of 15-25% through identification and elimination of process bottlenecks.
• Clear Business Justification for modernization investments through quantifiable process impact.
• Continuous Improvement Culture enabled by data-driven process optimization.
Our Methodology: The Intelligence-Driven Modernization Framework
A Structured Approach to Smarter Application Transformation
1.
Assessment & Baselining
Comprehensive application portfolio analysis, dependency mapping, and establishment of key performance and usage baselines.
2.
Intelligence Implementation
Deployment of monitoring, analytics, and AI tools tailored to your technology stack and business objectives.
3.
Insight Generation & Prioritization
Analysis of collected data to generate actionable insights and create a prioritized modernization roadmap.
4.
Execution & Validation
Implementation of modernization initiatives with continuous measurement of outcomes against established baselines.
5.
Continuous Optimization
Ongoing monitoring and refinement of both applications and analytics models for sustained improvement.
We leverage industry-leading technologies to deliver comprehensive application intelligence:
Monitoring & APM
Dynatrace, AppDynamics, Azure Application Insights, New Relic
AI/ML Platforms
Azure Machine Learning, AWS SageMaker, Google AI Platform
Code Analysis
SonarQube, Checkmarx, Fortify, CAST Imaging
Process Intelligence
Celonis, Minit, IBM Process Mining
ITSM & AIOps
ServiceNow, PagerDuty, Moogsoft, Splunk ITSI
Quantifiable Outcomes
For Application Owners
o 20-30% Reduction in Application Portfolio TCO through optimized licensing and infrastructure
o 30-40% Improvement in Development Efficiency through reduced technical debt
o 60-70% Faster Incident Resolution through AI-powered root cause analysis
For Business Stakeholders
o 15-25% Faster Process Cycle Times through bottleneck elimination
o 30-40% Improvement in User Satisfaction Scores through performance optimization
o Clear ROI Measurement for modernization investments
For IT Leadership
o Data-Driven Investment Decisions based on actual usage and performance data
o Proactive Risk Management through predictive analytics and early warning systems
o Continuous Improvement Culture enabled by measurable insights and outcomes
Client Success Story
Challenge
A diversified manufacturer with 200+ custom applications lacked visibility into application usage patterns, leading to inefficient resource allocation and inability to prioritize modernization efforts.
Our Solution
• Implemented application dependency mapping using VMware vRealize
• Deployed user behavior analytics with Azure Application Insights
• Established technical debt quantification process using SonarQube
• Built predictive incident management with Azure Machine Learning
Impact
• Identified and retired 35 underutilized applications, saving $1.2M annually
• Reduced critical incidents by 45% through proactive anomaly detection
• Accelerated modernization planning by 60% with data-driven prioritization
• Improved developer productivity by 30% through technical debt reduction
FAQs
Your Integration Questions, Answered
Q: How do you ensure data privacy when collecting user behavior analytics?
We implement privacy-by-design principles, including data anonymization, role-based access controls, and compliance with GDPR/CCPA regulations through proper data governance frameworks.
Q: Can these analytics be applied to legacy mainframe applications?
Yes, we use mainframe performance monitors (SMF data), transaction tracking, and user session analysis to extend intelligence to legacy systems.
Q: How long does it typically take to see actionable insights?
Initial dependency mapping and baseline establishment can be completed in 2-4 weeks, with progressively deeper insights emerging over 8-12 weeks as data accumulates.
Q: What's the difference between traditional monitoring and AI-driven analytics?
Traditional monitoring tells you what is happening now; AI-driven analytics predicts what will happen next and why it's happening, enabling proactive rather than reactive management.
The Actin Advantage in Application Analytics
Manufacturing-Focused Metrics
We understand and measure the metrics that matter most in manufacturing environments - OEE, cycle times, quality yield.
Full-Stack Visibility
From user interface to database performance, we provide comprehensive observability across your entire application estate.
Practical AI Implementation
We focus on AI solutions that deliver immediate, measurable value rather than theoretical capabilities.
Integration Expertise
Seamless integration with existing ERP, MES, and supply chain systems for holistic business intelligence.
Proven Methodologies
Battle-tested frameworks for implementing and sustaining application intelligence programs.
Stop making modernization decisions in the dark. Partner with Actin to implement a data-driven approach that delivers measurable improvements in performance, cost, and business value.
