What Do Employers Look for in "Business Analytics" Roles? - A Skill Mining Analysis

S. Umamaheswaran, Semila Fernandes, V.G. Venkatesh, Nivyasree Avula and Y. Shi
Additional contact information
S. Umamaheswaran: Symbiosis International (Deemed University)
Semila Fernandes: Symbiosis International (Deemed University)
V.G. Venkatesh: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
Nivyasree Avula: Bangalore University
Y. Shi: Macquarie University [Sydney]

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Abstract: Businesses constantly strive to build organizational capacity to use data strategically. As a result, there is a growing demand for business analytics professionals. While higher education systems worldwide have been adapting to build competencies, they must meet employees' expectations. Curriculum design for delivering business analytics competencies remains a challenge due to the rapidly evolving nature of business analytics as a discipline. The paper aims to decode the industry expectations for the Business Analytics profile. This study investigates the skills employers value by analyzing job descriptions. We use a text-mining approach to understand the weightage of different skills and mine skill clusters within business analytics roles. The core skill clusters are hard skills related to Big data, Business Intelligence, and analytical techniques. Results also suggest that traditional machine learning (ML) skills, typically expected in a data science profile, are also being sought after in a business analytics role. Surprisingly soft communication and stakeholder management skills are also emerging as essential skills for business analytics roles. This study provides a better understanding by investigating the interplay between the demand for skills in the job market and curriculum development. \textcopyright 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: Business analytics; Data Analytics; Skill development; Skill mining; Information society (search for similar items in EconPapers)
Date: 2023-11-30
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Citations: View citations in EconPapers (1)

Published in Information Systems Frontiers, 2023, ⟨10.1007/s10796-023-10437-y⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04433060

DOI: 10.1007/s10796-023-10437-y

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