While modern software tools continue to advance the performance and sophistication of fixed-access networks, wet or corroded cables present a significant challenge for customer support and outside plant organizations. For instance a single problematic cable can negatively impact several DSLs, generating multiple calls to the contact center. If the contact center is not able to diagnose the issue as cable-related, then the contact center agent often dispatches a field service technician who is not trained or qualified to handle cable-level defects. After multiple calls from multiple customers, the incident eventually escalates to an outside plant technician who identifies the location of the problem using specialized (and frequently costly) test equipment. This series of events can quickly add up to a very expensive problem to diagnose and resolve.
Now, however, DSL service providers have a new and dramatically more cost-effective approach to identify problematic cables in the outside plant. This approach uses automated big data analytics to identify wet or corroded cables that degrade speed and reliability across multiple DSLs. The analytic results improve service quality by helping providers proactively resolve cable-related issues. The results also enhance outside plant operations by prioritizing repairs that will have the greatest impact on quality of service across multiple subscribers.
Cable diagnostic algorithms applied against terabytes of performance data collected from the DSL network provide an alternate, more cost-effective way to diagnose wet or corroded cables. Using big data analytics such as those included in ASSIA’s Expresse Solutions Outside Plant Maintenance, providers can continually (and automatically) monitor performance parameters for each DSL in the network at a binder level. The algorithms detect subtle changes in network performance that correlate to moisture or corrosion and then deliver detailed reports to help the customer care and outside plant departments in diagnosis and repair.
Using big data analytics to identify wet and corroded cables requires tuning the analytic algorithms for each network to provide the most accurate results. For example the analytics engine must be able to accommodate subtle (or not so subtle) changes resulting from scheduled and unscheduled events such as maintenance activities, installation of new hardware or software, service upgrades, and weather patterns.
With a properly tuned analytics engine, providers achieve a high degree of sensitivity for accurately detecting problematic cables even before most customers may notice a problem. Expresse Solutions include the professional services and support to ensure the highest degree of performance and accuracy from the Outside Plant Maintenance analytics engine.