Behavioral Competencies of Digital Professionals: Understanding the Role of Emotional Intelligence

ISBN-13: 9783030335809 / Angielski / Miękka / 2021 / 110 str.

Sara Bonesso; Elena Bruni; Fabrizio Gerli

Spis treści

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 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 soft

skills 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 roles

under 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.

O autorze

Sara Bonesso is associate professor of Business Organization and Human Resources Management at the Ca’ Foscari University of Venice, Italy. She is also one of the founders and the Vice-Director of the Ca' Foscari Competency Centre.

Elena Bruni is post-doc researcher at the Department of Management, Ca’ Foscari University of Venice, Italy.

Fabrizio Gerli is Associate Professor of Business Organization and Human Resources Management at Ca’ Foscari University of Venice, Italy. He is also one of the founders and the Director of the Ca' Foscari Competency Centre.

Blurb

‘I encounter many data scientists and analysts whose sole focus is solving analytical problems and developing accurate models. They all need to read this excellent book and adopt its recommendations!’.

 — Thomas H. Davenport, Distinguished Professor, Babson College, Research Fellow, MIT Initiative on the Digital Economy

 ‘This book provides a compelling, credible and sound narrative to de-code complexity by developing a set of competencies (action, social, awareness, cognitive, exploration and organizational) supported by emotional intelligence. A must read for Leaders and HR practitioners, for the intellectual curious eager to understand that Human Beings will have to remain central to Human Development’.

 —Paolo Gallo, Former CHRO at World Economic Forum, World Bank and European Bank

Shedding new light on the human side of big data through the lenses of emotional and social intelligence competencies, this book advances the understanding of the requirements of the different professions that deal with big data. It also illustrates the empirical evidence collected through the application of the competency-based methodology to a sample of data scientists and data analysts, the two most in-demand big data jobs in the labor market. The book provides recommendations for the higher education system to offer better designed curricula for entry-level big data professions. It also offers managerial insights in describing how organizations and specifically HR practitioners can benefit from the competency-based approach to overcome the skill shortage that characterizes the demand for big data professional roles and to increase the effectiveness of the selection and recruiting processes. 

Sara Bonesso is associate professor of Business Organization and Human Resources Management at the Ca’ Foscari University of Venice. She is also one of the founders and the Vice-Director of the Ca' Foscari Competency Centre.

Elena Bruni is a post-doc researcher at the Department of Management, Ca’ Foscari University of Venice.

Fabrizio Gerli is Associate Professor of Business Organization and Human Resources Management at Ca’ Foscari University of Venice. He is also one of the founders and the Director of the Ca' Foscari Competency Centre.