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Top1. Introduction
In the era of Big Data, firms in every sector are required to deal with a huge amount of data (Côrte-real, Oliveira, & Ruivo, 2017) that come from both the physical world (sensors, RFIDs, GPS) and the human society (social networks, Internet) (Jin, Wah, Cheng, & Wang, 2015). Such data need to be processed, analyzed, interpreted and used to make business decisions. The analysis of these large volumes of data is called in general terms Big Data Analytics (BDA). Analytics, in broad terms, does not refer to a particular technology or method. Rather, it is a combination of multiple IT-enabled resources in order to gain information, answer questions, predict outcomes of problem solutions and support decision-making, consequently creating competitive advantage (Barbosa, Ladeira, & Vicente, 2017; Davenport & Jeanne, 2007; Davenport, Morison, & Harris, 2010; Trkman, McCormack, de Oliveira, & Ladeira, 2010).
The professional responsible for dealing with these enormous amounts of data is frequently called a Data Scientist, although other denominations might be used, such as Data Analyst or Data Engineer. Data scientists are among the most wanted and well-paid profiles on the job market (Carillo, 2017).
Despite being so trendy, working as a data scientist requires diverse competencies such as the ones related to programming, databases, statistics or even business. Although different competencies are required from these professionals, a first conceptualization in regard to what type of competencies are required from a corporate perspective seems a suitable starting point to address people management issues (Kache & Seuring, 2017). Due to the novelty of the data scientist concept, companies struggle to come up with explicit job descriptions required to the recruitment of this new type of employee. The reason for this may be found in a lack of knowledge on what may be required for such employees (Kache & Seuring, 2017). Hiring companies often do not understand exactly what employees need to be successful in analytics jobs (Cegielski & Jones-farmer, 2016). If this shortage continues, firms will have to offer highly competitive salaries to qualified data scientists and may need to develop data analytics training programs so their internal employees can meet such demand (Lee, 2017).
Grover et al. (2018) state that the most critical element is the human talent because specific competencies are needed to design and implement BDA strategies. According to the authors, it is impossible to develop and carry out a BDA strategy without the right group of knowledgeable big data experts. Professionals need to develop competencies related to technology as well as to specific business domains (Bose, 2009; H. Chen, Chiang, & Storey, 2012; Richey, Morgan, Lindsey-Hall, & Adams, 2016; Schoenherr & Speier-Pero, 2015). While the computing technologies required to facilitate data management are keeping pace, the human competencies business leaders require to leverage BDA are lagging behind (Sivarajah, Kamal, Irani, & Weerakkody, 2017). In fact, it has been shown that inadequate staffing is among the leading barriers to BDA (Debortoli, Müller, & Vom Brocke, 2014).
One way of better comprehending human resources demands is by analyzing job offers, which are often used as a channel for collecting relevant information about the required competencies in rapid-changing industries (Pejic-bach, Bertoncel, Meško, & Krstić, 2019). Understanding what is required to be a data scientist is mandatory both to comprehend market demands as well as to design more focused university programs. The analysis of job advertisements, or job posts, can be carried out with the aid of content analysis. However, the description of competencies of data specialists jobs is often nebulous (Mauro, Greco, Grimaldi, & Ritala, 2017).