• DocumentCode
    1667045
  • Title

    Optigrow: People Analytics for Job Transfers

  • Author

    Wei, Dennis ; Varshney, Kush R. ; Wagman, Marcy

  • Author_Institution
    Math. Sci. & Analytics, IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA
  • fYear
    2015
  • Firstpage
    535
  • Lastpage
    542
  • Abstract
    The information technology (IT) services industry is undergoing a rapid change with the growth of market interest in cloud, analytics, mobile, social, and security technologies. For service providers to match this pace, they must rapidly transform their workforce in terms of job roles, and do so without incurring excessive cost while continuing to deliver core services. In this paper, we describe a big data approach to enable such a transformation through internal job transfers of suitable employees from legacy areas to growth areas. Toward this end, we use data on employee expertise to mathematically profile skill sets required for growth area jobs and develop a statistical scoring algorithm to prioritize internal candidates to be transferred to those growth area jobs. We describe how we have enacted this analytics procedure within the IT services division of the IBM Corporation and provide empirical results. We also discuss the lessons learned during the deployment, focusing mostly on organizational reasons preventing wide uptake.
  • Keywords
    Big Data; information services; organisational aspects; personnel; IBM Corporation; IT services division; IT services industry; Optigrow; analytics procedure; big data approach; core service; employee expertise; employees; information technology services industry; internal candidate; internal job transfer; organizational reason; people analytics; security technology; service provider; statistical scoring algorithm; Algorithm design and analysis; Business; Industries; Security; Sociology; Statistics; Taxonomy; enterprise transformation; expertise analytics; human capital management; total variation distance; workforce analytics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2015 IEEE International Congress on
  • Conference_Location
    New York, NY
  • Print_ISBN
    978-1-4673-7277-0
  • Type

    conf

  • DOI
    10.1109/BigDataCongress.2015.84
  • Filename
    7207268