Assessing expertise awareness in resolution networks

Problem resolution is a key issue in the IT service industry. A large service provider handles, on daily basis, thousands of tickets that report various types of problems from its customers. The efficiency of this process highly depends on the effective interactions among various expert groups, in search of the resolver to the reported problem. In fact, ticket transfer decisions reflect the expertise awareness between groups, thus encoding a sophisticated resolution social network. In this paper, we propose a computational frame-work to quantitatively assess expertise awareness, i.e., how well a group knows the expertise of others. An accurate assessment of expertise awareness could identify the weakest components in a resolution system. The framework, built on our previously developed resolution engine, is able to calculate the performance difference caused by excluding a node from the network. The difference exposes the awareness of this node to other nodes in the network. To our best knowledge, this is the first study on this problem from a computational perspective. We tested the proposed framework on a large set of real-world problem tickets and validated our discovery by carefully analyzing the tickets that are incorrectly transferred. Experimental results show that our framework can successfully capture groups that do not know others' expertise very well.