Abstract
Workforce analytics (i.e., statistical analysis, modeling and mining of HR data) is particularly important in service industries. Service industries are people-intensive and the knowledge and expertise of the people within an organization is a strategic resource critical for success. Performance of employees in a service organization is directly related to the customer satisfaction and creation of value. In this paper, we adopt a domain-driven data mining approach and begin by raising specific business questions in workforce the analysis with a focus on IT Infrastructure Support (ITIS) services and then propose solutions for them. We distinguish between three aspects of what makes people valuable in an ITIS organization: expertise, specialization and experience. We propose novel formalizations of these notions and discuss rigorous statistical and optimization based algorithms to discover these 3 types of people (along with their work areas). In particular, for the important problem of expert discovery, we propose two separate algorithms: one statistical and another based on data envelopment analysis technique from optimization. The approaches have been implemented and have produced satisfactory results on more than 25 real-life ITIS datasets, one of which we use for illustration. [Service Science, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]
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