Conclusions and managerial implications
Data scientists have been put under the spotlight as the - supposedly - protagonists of the Big Data revolution in companies (Davenport & Patil, 2012). Firms need to get the right analytical skills and expertise added to their human capital but this goes well beyond acquiring data scientists alone. Managers still question themselves on which new talent they need and on how to upgrade the skills of their current human resources (Davenport, 2014; McAfee & Brynjolfsson, 2012). With the present study, we have provided structure and clarity to the multifaceted landscape of Big Data-related human resource needs, by offering a systematized nomenclature and characterization of job roles and skills. This contributes to the literature by enabling a coherent framework upon which to build future investigations, as desired my multiple researchers (Miller, 2014; Song & Zhu, 2015) We have assembled a semi-automated analytical process, based on web scraping, expert judgment, text mining and topic modelling techniques in order to systemically review the current job offers related to Big Data, using more than 2.700 job descriptions posted online. Our findings confirm the ideas of Miller (2014), who suggested that Data Scientists and their deep expertise on Analytical methods are far from being sufficient in granting companies a real competitive advantage. The evidence from our analysis suggests that there are 4 different job families related to Big Data, which are: Business Analysts, Data Scientists, Big Data Developers and Big Data Engineers. We have characterized each of them with a databased assessment of the skill sets required by each role family and the required level of proficiency. We have built a ‘Big Data Job Families vs. Skill sets matrix’ (Table 5) which can be used by business managers to structure their recruitment programs and functional career paths and also by universities for the sake of shaping their curricula and degree programs.
