Operational and Quality of Service Improvements Through Advanced Analytics

Global Mobile Carrier

Main Objectives

    • Find non-trivial mobile network fault patterns, allowing higher efficiency and effectiveness on handling those faults;
    • Identify time to resolve/SLA key offenders to address corrective actions;
    • Find and assess operational process bottlenecks allowing the required network growth with current tech teams.

Solution
Icaro Tech Dash Analytics and IBM Pure Data for Analytics solutions were used to gather data from relevant operational data sources – such as faults, tickets, inventory and complaints – and correlate data by using advanced data mining and machine learning algorithms.

Results
A pattern of short duration recurring faults was identified. These faults were not detected by operations (due to their short duration they were not shown by the tools), but were affecting end customers.

Improvements on internal processes (opening and handling of network incidents), providing better understanding/measurement of network quality.
A blacklisted alarm, which usually happens prior to site unavailability (1h30 in advance), was identified:

      • 39% of all unavailability events followed this pattern;
      • Every time the blacklisted alarm happened, an unavailability event followed it.

MTTR and Ticket Volume Seasonality

      • Team allocation optimization (there are more events during raining seasons and in the afternoon)

MTTR and Ticket Volume Seasonality

      • Evolution: the number of faults grows faster than network capacity

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