Closing the skills gap: Finding skilled analytics professionals for a dynamically changing data-driven environment

Angela D’Auria Stanton and Wilbur W. Stanton
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Angela D’Auria Stanton: Professor of Marketing, Department of Marketing, Radford University, USA
Wilbur W. Stanton: Professor of Marketing, Department of Marketing, Radford University, Radford, VA, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2019, vol. 5, issue 2, 170-184

Abstract: This paper presents a comprehensive assessment of the challenges companies face when attempting to fill an analytics position. It begins with an overview of the data proliferation context in which most companies find themselves when seeking analytics professionals. The study then analyses a sample of 191,276 analytics positions advertised on LinkedIn. The authors describe the demand for various types of analytics professionals and compare four of the most highly sought-after roles, namely, data scientist, marketing analytics, business analytics and data science professionals. Content analysis of position descriptions and job requirements is used to determine any similarities in competencies within each of the three categories of skill set (ie hard skills, soft skills and credentials) specified by employers seeking analytics professionals. Among the similarities is the growing acknowledgment that data analytics applied to large and complex datasets requires a new breed of employee — an employee who is fully grounded in a business domain, but who also has a breadth of analytical and personal skills. The paper then assesses the challenges that organisations face when recruiting employees with the desired skills. Finally, the paper presents emerging and creative methods for recruiting analytics talent in a very complex and dynamically changing environment.

Keywords: data science; business analytics; job requirements; position descriptions; marketing analytics; closing the skills gap (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2019
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