Towards Auto-remediation in Services Delivery: Context-Based Classification of Noisy and Unstructured Tickets

Service interactions account for major source of revenue and employment in many modern economies, and yet the service operations management process remains extremely complex. Ticket is the fundamental management entity in this process and resolution of tickets remains largely human intensive. A large portion of these human executed resolution tasks are repetitive in nature and can be automated. Ticket description analytics can be used to automatically identify the true category of the problem. This when combined with automated remediation actions considerably reduces the human effort. We look at monitoring data in a big provider’s domain and abstract out the repeatable tasks from the noisy and unstructured human-readable text in tickets. We present a novel approach for automatic problem determination from this noisy and unstructured text. The approach uses two distinct levels of analysis, (a) correlating different data sources to obtain a richer text followed by (b) context based classification of the correlated data. We report on accuracy and efficiency of our approach using real customer data.