This section provides the motivation for this project, the relevance of the topic addressed by the book
and a synopsis of the main themes covered by each chapter.
Chapter 1.
Big data analytics professionals: emerging trends and job profiles
Organizations have been deeply changing because of the digital transformation. New jobs are
becoming central to the success of any company, regardless the sector and the industry (Vidgen et al.
2017; Davenport and Harris 2017, 2007; Lorenz et al. 2015). One of the most pertinent definition
about professionals working in Big Data field is from American Marketing Association website:
Big Data professionals [are] individuals who can apply sophisticated quantitative skills to
data transcribing actions, interactions, or other behaviors of people to derive insights and
prescribe actions. Big Data professionals are further distinguished due to their ability to
work with extremely large datasets that may be problematic for standard tools. Data
Scientists are another subset of Big Data professional, and typically work with
continuously streaming, unstructured data that may come from social media, audio, or
video files (Keller, Ahern, & Works).
Scientific literature is still struggling to find a common definition of professionals working with Big
Data. More importantly, there is still confusion about the boundaries of different groups of jobs.
Indeed, job profiles, duties, tasks, and responsibilities are often overlapping. A first macro distinction
was recognized by Harris and Mehrotra (2014) between data scientists and analysts in general. They
distinguished the two categories according to five dimensions: types of data, preferred tools, nature of
work, typical educational background, and mind-set. However, nowadays organizations have different
profiles and all of them contribute in maintaining and providing solutions leveraging on a large
volume of data, for instance system architects, data analysts, data engineers, business analysts, and
data scientists. System architects are responsible to build and maintain the full technology
infrastructure for data ecosystems. Therefore, they manage the company’s server platform; they
support processes to load and manage the analytical data store; they integrate new data sources. Data
analysts on the other hand are responsible to support other IT functions regarding data processing in a
specific domain. Data engineers are quantitative analysts (such as programmers, software engineers)
and they support the data governance. They collect, cleanse, blend, form, organize data in the general
data warehouse. They solve more conventional quantitative analysis problems and their main
responsibility is to ensure data quality so that it can be properly analyzed. Business analysts analyze
data and communicate results through reports and dashboards to facilitate (and possibly give advice
to) business decision making. Lastly, data scientists are statisticians with a strong scientific
background. They acquire and bring structure to large quantities of formless data (or Big Data) to
generate value to the company. Despite the current literature acknowledges about these four macro
categories of job profiles, there is still a confusion about their impact within organizations and how
they contribute in decision making process. Therefore, this chapter will contribute to the extant
literature by addressing the following questions: which is the macro trend in the labor market of Big
Data professionals? What is the role and the main responsibilities of these emerging profiles within
the organizations? The chapter will discuss the following topics: i) digital transformation and its
impact on the labour market; ii) big data and emerging professions; iii) analysis and classification of
the job profiles that operate in the business analytics field.
Keywords: Big Data, business analytics professions, job profiles
Main References
Davenport, T. and J. Harris. 2017. Competing on analytics. Boston, Massachusetts: Harvard Business
Review Press.
Kelleher, J. D., Tierney, B. 2018. Data Science. The MIT Press Essential Knowledge series. The. MIT
Press, Cambridge, Massachusetts.
Lorenz, M., M. Rüßmann, R. Strack, K.L. Lueth and M. Bolle. 2015. Man and Machine in Industry
4.0. How will Technology Transform the Industrial Workforce Through 2025? The Boston
Consulting Group. https://www.bcgperspectives.com/content/articles/technology-businesstransformation-
engineered-products-infrastructure-man-machine-industry-4/. Accessed 1 May
2017.
Michelman, P. 2018. What the Digital Future Holds. 20 Groundbreaking Essays on How. The MIT
Press, Cambridge, Massachusetts.
Michelman, P. 2018. How to Go Digital Practical Wisdom to Help Drive Your Organization's Digital
Transformation. The MIT Press, Cambridge, Massachusetts. MIT Sloan Management Review.
The MIT Press, Cambridge, Massachusetts.
Vidgen, R., S. Shaw and D.B. Grant. 2017. Management challenges in creating value from business
analytics. European Journal of Operational Research 261(2): 626–639.
Chapter 2.
When hard skills are not enough: The role of behavioural competencies in business analytics
professions
Since David McClelland (1973) claimed that competencies are critical differentiator of performance,
‘every organization with more than 300 people uses some form of competency-based human resource
management’ (Boyatzis, 2009: 750). Behavioural competencies are central to achieve superior
performance, both at individual and at firm level (Koman and Wolff, 2008; Zhang and Fan, 2013).
The concept of competency comprehends both action (how an individual behaves according to a
specific situation) and intent (how much effort an individual has towards something) (Boyatzis, 2009).
Thereby, a competence is the underlying characteristics of a person that lead to or cause effective and
outstanding performance (Boyatzis, 2008: 93). It is a capability (Boyatzis, 1982, 2008; McClelland,
1973), and specifically it consists of a ‘set of related but different sets of behaviour organized around
an underlying construct called the “intent.” The behaviours are alternate manifestations of the intent,
as appropriate in various situations or times’ (Boyatzis, 2009: 750). Thereby, these behavioural
capabilities comprise Emotional, Social, and Cognitive competences (ESCs) that refer to the ability to
recognize, understand and manage one’s own (emotional competencies) and others’ emotions (social
competences), as well as to the ability to analyse information and situations (cognitive competencies)
(Boyatzis and Sala, 2004; Boyatzis, 2009). According to this model, Emotional, Social, and Cognitive
competences are:
- Emotional competencies: self-awareness and self-management;
- Social competencies: social awareness and relationship management;
- Cognitive competencies: the capacity to think, analyse, and organize information and
different situations.
As suggested by Boyatzis (1982; 2009), an outstanding performance occurs when the person’s
capability or talent is consistent with the needs of the job demands and the organizational
environment. Thereby, the analysis of outstanding perfor
