Discovering Experts, Experienced Persons and Specialists for IT Infrastructure Support

Girish Keshav Palshikar (), Harrick M. Vin (), V. Vijaya Saradhi () and Mohammed Mudassar ()
Additional contact information
Girish Keshav Palshikar: Tata Research Development and Design Centre (TRDDC), Tata Consultancy Services Limited, 54B Hadapsar Industrial Estate, Pune 411014, India
Harrick M. Vin: Tata Research Development and Design Centre (TRDDC), Tata Consultancy Services Limited, 54B Hadapsar Industrial Estate, Pune 411014, India
V. Vijaya Saradhi: Department of Computer Science, Indian Institute of Technology, Guwahati 781039, India
Mohammed Mudassar: Tata Consultancy Services Limited, 54B Hadapsar Industrial Estate, Pune 411014, India

Service Science, 2011, vol. 3, issue 1, 1-21

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.]

Keywords: expert discovery; workforce analytics; human resource management; support services; IT infrastructure support; data mining (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/serv.3.1.1 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:3:y:2011:i:1:p:1-21

Access Statistics for this article

More articles in Service Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().