A major telecom operator needed to optimize its network performance monitoring system due to inefficiencies in real-time data analysis and predictive maintenance. Sparity stepped into streamline the workflows with Power BI Solution.
Client: Telecom
Services: Power BI
Year: 2024
Client Challenges:
High Data Volume: Processing over 50 million network performance data points daily.
Integration Issues: Data scattered across 10 different systems, including SNMP, Syslog, and proprietary databases.
Real-Time Analysis: Required dashboards to update every 5 minutes to reflect current network status.
Predictive Maintenance: No existing predictive analytics to anticipate network failures.
Slow Reporting: Manual reports took up to 24 hours to compile.
User Access: Over 200 field engineers and managers needed access to actionable insights.
Scalability: Need to support an anticipated 30% data volume increase over the next 2 years.
Sparity Solutions:
Data Integration: Employed Power BI Dataflows to consolidate data from SNMP, Syslog, and other sources into a single dataset.
Real-Time Dashboards: Built real-time dashboards using DirectQuery to ensure updates every 5 minutes.
Predictive Models: Integrated Power BI with Azure Machine Learning to develop predictive models for network failure, analyzing historical data patterns.
Automated Reporting: Created automated reports using Power BI’s subscription feature, reducing report generation time from 24 hours to 2 hours.
Custom Visualizations: Designed custom visualizations using Power BI’s advanced charting features to simplify complex data.
Alerts and Notifications: Set up Power BI Alerts to notify engineers when performance metrics exceed predefined thresholds.
Scalability Planning: Configured the Power BI environment to handle a 30% increase in data volume through scalable Azure resources.
Benefits:
Efficiency Improvement: Reduced network issue detection time by 50% due to real-time dashboards.
Predictive Accuracy: Increased predictive accuracy for network failures by 40%, preventing potential outages.
Reporting Speed: Reduced manual report generation time by 92%, from 24 hours to 2 hours.
User Satisfaction: Enhanced access to insights for 200+ users, improving decision-making efficiency.
Scalability: Supported a 30% data volume increase with no performance degradation.
