Overview

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. 

Full Product Details

Author:   Sara Bonesso ,  Elena Bruni ,  Fabrizio Gerli
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Weight:   0.454kg
ISBN:  

9783030335779

ISBN 10:   3030335771
Pages:   110
Publication Date:   07 February 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

This section provides the motivation for this project, the relevance of the topic addressed by the bookand a synopsis of the main themes covered by each chapter.Chapter 1.Big data analytics professionals: emerging trends and job profilesOrganizations have been deeply changing because of the digital transformation. New jobs arebecoming 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 definitionabout professionals working in Big Data field is from American Marketing Association website:Big Data professionals [are] individuals who can apply sophisticated quantitative skills todata transcribing actions, interactions, or other behaviors of people to derive insights andprescribe actions. Big Data professionals are further distinguished due to their ability towork with extremely large datasets that may be problematic for standard tools. DataScientists are another subset of Big Data professional, and typically work withcontinuously streaming, unstructured data that may come from social media, audio, orvideo files (Keller, Ahern, & Works).Scientific literature is still struggling to find a common definition of professionals working with BigData. 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 distinctionwas recognized by Harris and Mehrotra (2014) between data scientists and analysts in general. Theydistinguished the two categories according to five dimensions: types of data, preferred tools, nature ofwork, typical educational background, and mind-set. However, nowadays organizations have differentprofiles and all of them contribute in maintaining and providing solutions leveraging on a largevolume of data, for instance system architects, data analysts, data engineers, business analysts, anddata scientists. System architects are responsible to build and maintain the full technologyinfrastructure for data ecosystems. Therefore, they manage the company’s server platform; theysupport processes to load and manage the analytical data store; they integrate new data sources. Dataanalysts on the other hand are responsible to support other IT functions regarding data processing in aspecific 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 generaldata warehouse. They solve more conventional quantitative analysis problems and their mainresponsibility is to ensure data quality so that it can be properly analyzed. Business analysts analyzedata and communicate results through reports and dashboards to facilitate (and possibly give adviceto) business decision making. Lastly, data scientists are statisticians with a strong scientificbackground. They acquire and bring structure to large quantities of formless data (or Big Data) togenerate value to the company. Despite the current literature acknowledges about these four macrocategories of job profiles, there is still a confusion about their impact within organizations and howthey contribute in decision making process. Therefore, this chapter will contribute to the extantliterature by addressing the following questions: which is the macro trend in the labor market of BigData professionals? What is the role and the main responsibilities of these emerging profiles withinthe organizations? The chapter will discuss the following topics: i) digital transformation and itsimpact on the labour market; ii) big data and emerging professions; iii) analysis and classification ofthe job profiles that operate in the business analytics field.Keywords: Big Data, business analytics professions, job profilesMain ReferencesDavenport, T. and J. Harris. 2017. Competing on analytics. Boston, Massachusetts: Harvard BusinessReview Press.Kelleher, J. D., Tierney, B. 2018. Data Science. The MIT Press Essential Knowledge series. The. MITPress, Cambridge, Massachusetts.Lorenz, M., M. Rüßmann, R. Strack, K.L. Lueth and M. Bolle. 2015. Man and Machine in Industry4.0. How will Technology Transform the Industrial Workforce Through 2025? The BostonConsulting Group. https://www.bcgperspectives.com/content/articles/technology-businesstransformation-engineered-products-infrastructure-man-machine-industry-4/. Accessed 1 May2017.Michelman, P. 2018. What the Digital Future Holds. 20 Groundbreaking Essays on How. The MITPress, Cambridge, Massachusetts.Michelman, P. 2018. How to Go Digital Practical Wisdom to Help Drive Your Organization's DigitalTransformation. 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 businessanalytics. European Journal of Operational Research 261(2): 626–639.Chapter 2.When hard skills are not enough: The role of behavioural competencies in business analyticsprofessionsSince 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 resourcemanagement’ (Boyatzis, 2009: 750). Behavioural competencies are central to achieve superiorperformance, 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 aspecific 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 andoutstanding 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 aroundan 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 behaviouralcapabilities comprise Emotional, Social, and Cognitive competences (ESCs) that refer to the ability torecognize, understand and manage one’s own (emotional competencies) and others’ emotions (socialcompetences), 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 Cognitivecompetences 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 anddifferent situations.As suggested by Boyatzis (1982; 2009), an outstanding performance occurs when the person’scapability 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 aspecific 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 analyticsis a study conducted by Joseph and his colleagues (2010) on IT professionals. By developing adedicated instrument, called SoftSkills for IT, they attempted to provide empirical evidence thattechnical skills are not sufficient for success in IT, because these individuals work in a very dynamicand complex workplace. According to the authors, because of this complexity, these individuals needto develop a practical intelligence, which is made up of a set of skills (managerial, intrapersonal, andinterpersonal) that are used to resolve IT-related work problems (Joseph et al. 2010: 149). If on theone hand this study emphasizes that technical skills are not enough, it does not provide anyinformation about the main soft skills that should be possessed by data scientists or by other digitalroles. A recent study by Costa and Santos (2017) proposed a conceptual model that identifies, amongpersonal and social capabilities of a data scientist, the following characteristics: business acumen,communication, entrepreneurship, curiosity, and interdisciplinary orientation. Within a data analysiscluster, they consider quantitative analysis, exploratory data analysis, analytical methods, andautomated analysis as the skills pertaining to data scientists. Despite this study makes a step forwardin understanding the basic knowledge and skills of a data scientist, it classifies competencies drawingon a review of the scientific literature, academic formations, and industry-related content (Costa andSantos 2017: 733). Interpersonal and social skills have been found as among the most importantabilities for both business analysts and data scientists. These roles are asked to collaborate and workwith others (peers and team members) in contexts where each one deals with a specific step of the dataanalysis process (Shirani 2016). According to Davenport and Patil (2012: 74), what distinguishes datascientists 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 ofhypotheses that can be tested.’ In Lee and Han (2008), as well as in Kim and Lee (2016), skills such asanalytical and logical thinking, creativity and innovation, and problem solving are grouped togetherinto the cluster “problem solving skills.” Despite the relevance of these studies, extant research thathas attempted to identify the soft skills possessed by data scientists and business analysts has severalshortcomings, one of which is related to the methodology adopted. They indeed infer behaviouralcompetencies by means of questionnaires or secondary sources that do not really capture the level of apossession of a competency. Therefore, this chapter is meant to provide a clear understanding of whyit is important to analyse behavioural competencies of these professionals since they are called tounderstand data, to interpret them, and transmit to upper level of organizations. To make the entireprocess working, behavioural competencies play a fundamental role. The chapter will be structured asfollows: i) introduction of the competency framework, classification and definition of behaviouralcompetencies; ii) impact of behavioural competencies on individual performance; ii) behaviouralcompetencies in big data professions: discussion of extant research and of the major gaps.Keywords: soft skills, behavioural competencies, emotional and social competencies, competencymodel, business analytics professionsMain ReferencesBoyatzis, 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. ConsultingPsychology Journal: Practice and Research, 60(4): 298–313.Boyatzis, R. E. 2009. Competencies as a behavioral approach to emotional intelligence. Journal ofManagement Development, 28(9): 749–770.Boyatzis, R. E., and Kolb, D. 1995. From learning styles to learning skills: the executive skillsprofile. Journal of Managerial Psychology, 10(5): 3–17.Boyatzis, R. E., Sala, F. 2004. The Emotional Competence Inventory (ECI), In MeasuringEmotional 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 Europeane-Competency framework and the skills framework for the information age. InternationalJournal 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. PsychologicalScience, 9(5): 331–339.Shirani, A. 2016. Identifying Data Science and Analytics competencies based on industryDemand. 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 andbusiness analysts, since they are the two big data profiles who have a direct impact on businessfunction and decision-making processes (De Mauro et al. 2016), and therefore they are at the core oforganizational changes (Davenport and Harris 2017). In particular, the data scientist has an immediateand massive ‘impact [into] organizations (Patil 2011), understanding how to find answers to relevantbusiness questions, and exploring a voluminous and diverse set of data through a scientific way ofdoing things’ (Costas and Santos 2017: 99). The behavioural competencies of both professionals areinvestigated in order to emphasize the peculiarities of each profession. The chapter illustrates theempirical evidence collected through an in-depth qualitative exploratory study on a sample of datascientists 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 (Shirani2016; 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 doesnot rely on perceptions of the main important competencies for the professional roles underinvestigation, 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; Boyatzis2009). 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/incidentsin which each respondent felt effective in performing his/her job in the organizational context. Inparticular, the competency portfolio of data scientists and business analysts is described based on acodebook 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 ReferencesAasheim, C., and J. Shropshire. 2012. Knowledge and Skill Requirements for Entry-Level ITWorkers: 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 theEuropean 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 BusinessReview Press.Davenport, T. H. and D.J. Patil. 2012. Data Scientist: The Sexiest Job of the 21st Century. HarvardBusiness Review 90 (October 2012): 70–76.De Mauro, A., M. Greco, M. Grimaldi, and G. Nobili. 2016. Beyond Data Scientists: a Review of BigData Skills and Job Families. International Forum on Knowledge Asset Dynamics 11th,Proceedingsof IFKAD, 2016, Towards a New Architecture of Knowledge: Big Data Cultureand Creativity, June 15th-17th 2016, Dresden (Germany), 1844–1857.Shirani, A. 2016. Identifying Data Science and Analytics competencies based on industryDemand. Issues in Information Systems 17(4): 137–144.Chapter 4.Managing business analytics professions through a competency-based approach. This chaptercontributes to the current debate on how to overcome the skill shortage that characterize the demandof big data professions in the labour market. First, it offers managerial insights in describing howorganizations and specifically HR practitioners can benefit from the competency-based approach toincrease the effectiveness of the selection and recruiting processes of candidates, achieving a bettermatch between the job offer and demand. Besides the recruiting and selection process, the competencyportfolio of business analytics professionals, identified through the competency modelling, can beadopted in other human resource management practices such as training, performance and careermanagement. Second, the chapter provides recommendations for the higher education system to offerbetter designed curricula for entry-level big data professions. This is coherent with a call expressed bythe European Commission, professionals, and academics: ‘business and academia must collaborate toclearly 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 onanalytics (see Costa and Santos 2017). However, there is a need to design such programs carefully toprovide adequate preparation, both in terms of technical and soft skills.Main referencesCosta, C., and M.Y. Santos. 2017. The data scientist profile and its representativeness in the Europeane-Competency framework and the skills framework for the information age. InternationalJournal of Information Management 37(6): 726–734.Miller, S. 2014. Collaborative Approaches Needed to Close the Big Data Skills Gap. Journal ofOrganization Design 3(1): 26–30.

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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.

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