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· 제목 : Behavioral Competencies of Digital Professionals: Understanding the Role of Emotional Intelligence (Paperback, 2020)
· 분류 : 외국도서 > 경제경영 > 관리
· ISBN : 9783030335809
· 쪽수 : 110쪽
· 분류 : 외국도서 > 경제경영 > 관리
· ISBN : 9783030335809
· 쪽수 : 110쪽
목차
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. Rußmann, 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 organizationalenvironment. Thereby, the analysis of outstanding performance should focus the attention on when a
specific competence occurs and on its frequency (Boyatzis, 2009; McClelland, 1998).
One of the few contributions that attempts to detect behavioural competencies in the field of analytics
is a study conducted by Joseph and his colleagues (2010) on IT professionals. By developing a
dedicated instrument, called SoftSkills for IT, they attempted to provide empirical evidence that
technical skills are not sufficient for success in IT, because these individuals work in a very dynamic
and complex workplace. According to the authors, because of this complexity, these individuals need
to develop a practical intelligence, which is made up of a set of skills (managerial, intrapersonal, and
interpersonal) that are used to resolve IT-related work problems (Joseph et al. 2010: 149). If on the
one hand this study emphasizes that technical skills are not enough, it does not provide any
information about the main soft skills that should be possessed by data scientists or by other digital
roles. A recent study by Costa and Santos (2017) proposed a conceptual model that identifies, among
personal and social capabilities of a data scientist, the following characteristics: business acumen,
communication, entrepreneurship, curiosity, and interdisciplinary orientation. Within a data analysis
cluster, they consider quantitative analysis, exploratory data analysis, analytical methods, and
automated analysis as the skills pertaining to data scientists. Despite this study makes a step forward
in understanding the basic knowledge and skills of a data scientist, it classifies competencies drawing
on a review of the scientific literature, academic formations, and industry-related content (Costa and
Santos 2017: 733). Interpersonal and social skills have been found as among the most important
abilities for both business analysts and data scientists. These roles are asked to collaborate and work
with others (peers and team members) in contexts where each one deals with a specific step of the data
analysis process (Shirani 2016). According to Davenport and Patil (2012: 74), what distinguishes data
scientists from other IT professionals is a desire, which Davenport and Patil call curiosity, to go
‘beneath the surface of a problem, find the questions at its heart, and distil them into a very clear set of
hypotheses that can be tested.’ In Lee and Han (2008), as well as in Kim and Lee (2016), skills such as
analytical and logical thinking, creativity and innovation, and problem solving are grouped together
into the cluster “problem solving skills.” Despite the relevance of these studies, extant research that
has attempted to identify the soft skills possessed by data scientists and business analysts has several
shortcomings, one of which is related to the methodology adopted. They indeed infer behavioural
competencies by means of questionnaires or secondary sources that do not really capture the level of a
possession of a competency. Therefore, this chapter is meant to provide a clear understanding of why
it is important to analyse behavioural competencies of these professionals since they are called to
understand data, to interpret them, and transmit to upper level of organizations. To make the entire
process working, behavioural competencies play a fundamental role. The chapter will be structured as
follows: i) introduction of the competency framework, classification and definition of behavioural
competencies; ii) impact of behavioural competencies on individual performance; ii) behavioural
competencies in big data professions: discussion of extant research and of the major gaps.
Keywords: soft skills, behavioural competencies, emotional and social competencies, competency
model, business analytics professions
Main References
Boyatzis, R. E. 1982. The competent manager: A model for effective performance, New York, NY:
Wiley.
Boyatzis, R. E. 2008a. Competencies in the 21st century. Journal of management development, 27
(1), 5?12.
Boyatzis, R. E. 2008b. Leadership development from a complexity perspective. Consulting
Psychology Journal: Practice and Research, 60(4): 298?313.
Boyatzis, R. E. 2009. Competencies as a behavioral approach to emotional intelligence. Journal of
Management Development, 28(9): 749?770.
Boyatzis, R. E., and Kolb, D. 1995. From learning styles to learning skills: the executive skills
profile. Journal of Managerial Psychology, 10(5): 3?17.
Boyatzis, R. E., Sala, F. 2004. The Emotional Competence Inventory (ECI), In Measuring
Emotional Intelligence, Ed. G. Geher, Hauppauge, NY: Nova Scienec Publishers: 147?180.
Costa, C., and M.Y. Santos. 2017. The data scientist profile and its representativeness in the European
e-Competency framework and the skills framework for the information age. International
Journal of Information Management 37(6): 726?734.
Joseph, D., S. Ang, R.H.L. Chang and S. Slaughter. 2010. Practical intelligence in IT: Assessing softskills of IT professionals. Communications of the ACM 53(2): 149?154.
McClelland, D.C. 1973. Testing for competence rather than intelligence. American Psychologist,
28: 1?14.
McClelland, D.C. 1998. Identifying competencies with behavioral event interviews. Psychological
Science, 9(5): 331?339.
Shirani, A. 2016. Identifying Data Science and Analytics competencies based on industry
Demand. Issues in Information Systems 17(4): 137?144.
Chapter 3.
The competency profile of data scientists and business analysts.
This chapter concentrates the attention on the analysis of two specific professionals: data scientists and
business analysts, since they are the two big data profiles who have a direct impact on business
function and decision-making processes (De Mauro et al. 2016), and therefore they are at the core of
organizational changes (Davenport and Harris 2017). In particular, the data scientist has an immediate
and massive ‘impact [into] organizations (Patil 2011), understanding how to find answers to relevant
business questions, and exploring a voluminous and diverse set of data through a scientific way of
doing things’ (Costas and Santos 2017: 99). The behavioural competencies of both professionals are
investigated in order to emphasize the peculiarities of each profession. The chapter illustrates the
empirical evidence collected through an in-depth qualitative exploratory study on a sample of data
scientists and business analysts operating in the Italian context. In contrast to previous literature,
which has drawn mainly on survey questionnaires (Aasheim et al. 2012) or content analysis (Shirani
2016; De Mauro et al. 2016), the study adopts the competency-based methodology, and specifically,
data has been collected through Behavioural Event Interview (BEI), a consolidated technique that does
not rely on perceptions of the main important competencies for the professional roles under
investigation, but it allows to detect the behaviours that are actually enacted in the work environment
(Scapolan, Montanari, Bonesso, Gerli, and Mizzau 2017; Emmerling and Boyatzis 2012; Boyatzis
2009). The chapter provides an in-depth description of the tasks and responsibilities of the two rolesunder investigation, as well as of the behavioural competencies manifested in critical events/incidents
in which each respondent felt effective in performing his/her job in the organizational context. In
particular, the competency portfolio of data scientists and business analysts is described based on a
codebook which encompasses thirty-three behavioural competencies clustered into six clusters:
awareness, action, social, cognitive, exploration, and strategic competencies.
Keywords: data scientists, business analysts, Emotional, Social, and Cognitive competencies (ESCs)
Main References
Aasheim, C., and J. Shropshire. 2012. Knowledge and Skill Requirements for Entry-Level IT
Workers: A Longitudinal Study. Journal of Information Systems Education 23(2): 193?205.
Costa, C., and M.Y. Santos. 2017. The data scientist profile and its representativeness in the
European e-Competency framework and the skills framework for the information age.
International Journal of Information Management 37(6): 726?734.
Davenport, T. and J. Harris. 2017. Competing on analytics. Boston, Massachusetts: Harvard Business
Review Press.
Davenport, T. H. and D.J. Patil. 2012. Data Scientist: The Sexiest Job of the 21st Century. Harvard
Business Review 90 (October 2012): 70?76.
De Mauro, A., M. Greco, M. Grimaldi, and G. Nobili. 2016. Beyond Data Scientists: a Review of Big
Data Skills and Job Families. International Forum on Knowledge Asset Dynamics 11th,
Proceedingsof IFKAD, 2016, Towards a New Architecture of Knowledge: Big Data Culture
and Creativity, June 15th-17th 2016, Dresden (Germany), 1844?1857.
Shirani, A. 2016. Identifying Data Science and Analytics competencies based on industry
Demand. Issues in Information Systems 17(4): 137?144.
Chapter 4.
Managing business analytics professions through a competency-based approach. This chapter
contributes to the current debate on how to overcome the skill shortage that characterize the demand
of big data professions in the labour market. First, it offers managerial insights in describing how
organizations and specifically HR practitioners can benefit from the competency-based approach to
increase the effectiveness of the selection and recruiting processes of candidates, achieving a better
match between the job offer and demand. Besides the recruiting and selection process, the competency
portfolio of business analytics professionals, identified through the competency modelling, can be
adopted in other human resource management practices such as training, performance and career
management. Second, the chapter provides recommendations for the higher education system to offer
better designed curricula for entry-level big data professions. This is coherent with a call expressed by
the European Commission, professionals, and academics: ‘business and academia must collaborate to
clearly define the big data knowledge and skill sets required across the organization’ (Miller 2014).
There is increasing attention within different institutions on developing and sponsoring programs on
analytics (see Costa and Santos 2017). However, there is a need to design such programs carefully to
provide adequate preparation, both in terms of technical and soft skills.
Main references
Costa, C., and M.Y. Santos. 2017. The data scientist profile and its representativeness in the European
e-Competency framework and the skills framework for the information age. International
Journal of Information Management 37(6): 726?734.
Miller, S. 2014. Collaborative Approaches Needed to Close the Big Data Skills Gap. Journal of
Organization Design 3(1): 26?30.
