Automatically monitor KPIs with Anomaly Detection
Surface relevant changes and digested updates to make quick, informed decisions.
Understand the usual performance of each KPI.
Automatically identify unusual behavior and root cause.
Send automated reports to the right people.
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Anomaly Detection for large telecom provider.
Challenge
The company had developed and purchased a myriad of systems to meet their reporting needs.
Multiple teams had to sort through several different systems and interpret the information to find the relevant anomalies.
They then manually compiled reports to email to the management team.
Multiple teams had to sort through several different systems and interpret the information to find the relevant anomalies.
They then manually compiled reports to email to the management team.
Solution
We built a Machine Learning system that understands the usual behavior of each KPI at each dimension (country, province, city, hourly, daily, etc) and the correlation with other KPIs.
Internal datasets like promotions, price changes, new system versions were used as contextual information to explain the anomalies.
Automated reports with summaries were set up through email, chat, and other channels to alert the relevant people and give additional in-depth information through custom dashboards.
Internal datasets like promotions, price changes, new system versions were used as contextual information to explain the anomalies.
Automated reports with summaries were set up through email, chat, and other channels to alert the relevant people and give additional in-depth information through custom dashboards.
Results
Results
The system successfully removed the manual effort of sorting through the data to identify root causes and reduced the time it took to identify relevant issues from days to mere minutes.
The 8X faster anomaly detection enabled faster issue resolution, enhanced the service reliability, and allowed an overall better customer experience.
The 8X faster anomaly detection enabled faster issue resolution, enhanced the service reliability, and allowed an overall better customer experience.
See other use cases
See other use cases
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