The emotional and social side of analytics professionals: an exploratory study of the behavioral profile of data scientists and data analysts

Abstract

PurposeAnalytics technologies are profoundly changing the way in which organizations generate economic and social value from data. Consequently, the professional roles of data scientists and data analysts are in high demand in the labor market. Although the technical competencies expected for these roles are well known, their behavioral competencies have not been thoroughly investigated. Drawing on the competency-based theoretical framework, this study aims to address this gap, providing evidence of the emotional, social and cognitive competencies that data scientists and data analysts most frequently demonstrate when they effectively perform their jobs, and identifying those competencies that distinguish them.Design/methodology/approachThis study is exploratory in nature and adopts the competency-based methodology through the analysis of in-depth behavioral event interviews collected from a sample of 24 Italian data scientists and data analysts.FindingsThe findings empirically enrich the extant literature on the intangible dimensions of human capital that are relevant in analytics roles. Specifically, the results show that, in comparison to data analysts, data scientists more frequently use certain competencies related to self-awareness, teamwork, networking, flexibility, system thinking and lateral thinking.Research limitations/implicationsThe study was conducted in a small sample and in a specific geographical area, and this may reduce the analytic generalizability of the findings.Practical implicationsThe skills shortages that characterize these roles need to be addressed in a way that also considers the intangible dimensions of human capital. Educational institutions can design better curricula for entry-level data scientists and analysts who encompass the development of behavioral competencies. Organizations can effectively orient the recruitment and the training processes toward the most relevant competencies for those analytics roles.Originality/valueThis exploratory study advances our understanding of the competencies required by professionals who mostly contribute to the performance of data science teams. This article proposes a competency framework that can be adopted to assess a broader portfolio of the behaviors of big data professionals.

The data-driven leader: developing a big data analytics leadership competency framework

“…The importance of both task-related competencies and relations-oriented competencies is in line with the findings by Mikalef et al. (2018b) and Bonesso et al. (2022).…”

“…The importance of both task-related competencies and relations-oriented competencies is in line with the findings by Mikalef et al (2018b) and Bonesso et al (2022). However, both research groups explored the requirements for data scientists and data analysts while our study examined…”

“…(2016) conceptual framework, talent capability is composed of BDA technology management know-how, BDA technical know-how, BDA business acumen and BDA relational know-how. Bonesso et al. (2022) contributed to BDA studies by identifying a difference between data scientists' and data analysts' use of competencies.…”

“…In Akter et al (2016) conceptual framework, talent capability is composed of BDA technology management know-how, BDA technical know-how, BDA business acumen and BDA relational know-how. Bonesso et al (2022) contributed to BDA studies by identifying a difference between data scientists' and data analysts' use of competencies. Mikalef et al (2018b) contributed to BDA research by examining the skills BDA leaders were looking for in graduates aspiring to become data scientists.…”

Dealing with Data Challenges when Delivering Data-Intensive Software Solutions

“…Personality impacts have been extensively studied in software teams [61], including impact on requirements engineering [62], coding and pair programming [63], and testing [64]. The impact of personality on multi-disciplinary teams, especially those involved in DI solution development, is little explored to date [65].…”