Data Analytics Dashboard for IT Operations
Project Overview
Developed a comprehensive data analytics dashboard for IT operations, providing real-time visibility into infrastructure health, performance metrics, and user behavior across 300+ endpoints and 81 unified websites.
Business Need
- Lack of centralized visibility into IT infrastructure
- Reactive approach to problem resolution
- Difficulty in capacity planning
- No data-driven decision making
- Manual reporting consuming significant time
Solution
Dashboard Features
Real-Time Monitoring
- System health status
- Network performance metrics
- Service availability
- Active incidents and alerts
Historical Analytics
- Trend analysis for capacity planning
- Performance over time
- Incident patterns and root causes
- User behavior analytics
Predictive Insights
- Resource usage forecasting
- Potential issue prediction
- Capacity planning recommendations
Technology Stack
- Data Collection: Python scripts, SNMP, APIs
- Data Storage: PostgreSQL, InfluxDB (time-series)
- Processing: pandas, NumPy
- Visualization: Grafana, Plotly
- Backend: Python, Flask
- Automation: Scheduled data collection and analysis
Implementation
Data Pipeline
Data Sources → Collection Scripts → Database → Processing → Visualization
Key Metrics Tracked
- Infrastructure
- Server CPU, memory, disk usage
- Network bandwidth utilization
- Service uptime and availability
- Applications
- Response times
- Error rates
- User sessions
- Support
- Ticket volume and trends
- Resolution times
- First-call resolution rate
Results
| Metric | Before | After | Impact |
|---|---|---|---|
| Incident Response Time | 4 hours | 2.4 hours | -40% |
| Proactive Issue Detection | 20% | 75% | +275% |
| Reporting Time | 8 hours/week | 30 min/week | -94% |
| Capacity Planning Accuracy | 60% | 90% | +50% |
Business Benefits
- Proactive Management: Issues detected before user impact
- Data-Driven Decisions: Insights for infrastructure investments
- Time Savings: Automated reporting freed up staff time
- Improved SLAs: Better service level achievement
- Cost Optimization: Right-sized resources based on data
Technical Highlights
Data Collection
- Automated scripts collecting data every 5 minutes
- API integrations with monitoring tools
- Log file parsing and analysis
- Custom metrics for business KPIs
Visualization
- Interactive dashboards with drill-down capability
- Customizable views for different stakeholders
- Mobile-responsive design
- Automated report generation
Alerts and Notifications
- Threshold-based alerting
- Anomaly detection using ML
- Multi-channel notifications (email, SMS)
- Escalation workflows
Skills Demonstrated
- Data Engineering
- Data Visualization
- Python Programming
- Database Management
- Statistical Analysis
- Dashboard Design
- Stakeholder Communication
Technologies
| Python | pandas | Grafana | PostgreSQL | InfluxDB | Flask | Plotly | APIs |
Future Enhancements
- Machine learning for predictive analytics
- Natural language queries
- Advanced anomaly detection
- Integration with more data sources
Contact
- LinkedIn: shuvo-kumar-shill
- Medium: Data Analytics Articles
#DataAnalytics #Dashboard #Python #Grafana #ITOperations #DataVisualization