The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age

Costa, Carlos; Santos, Maribel Yasmina

doi:10.1016/j.ijinfomgt.2017.07.010

Cited by 48 publications

(46 citation statements)

References 5 publications

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“…Specifically, concerning technical skills, Davenport and Patil (2012) emphasise on the importance that the emerging job of the data scientist will have in the next few years throughout a number of industries. Such technical skills are important when it comes to understanding what data is of value, and how different data types can be cleansed, processed, and analysed to derive insights that are actionable (Costa & Santos, 2017). Nevertheless, while technical skills are important, one of the most critical aspects of data science is the ability of data-analytic thinking and strategic planning based on data-driven insight (Mikalef et al, 2018a;Prescott, 2014).…”

Section: Big Data Analytics Resourcesmentioning

confidence: 99%

Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities

Mikalef

Krogstie

2020

European Journal of Information Systems

178

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The potential of big data analytics in enabling improvements in business processes has urged researchers and practitioners to understand if, and under what combination of conditions, such novel technologies can support the enactment and management of business processes. While there is much discussion around how big data analytics can impact a firm's incremental and radical process innovation capabilities, we still know very little about what big data analytics resources firms must invest in to drive such outcomes. To explore this topic, we ground this study on a theory-driven conceptualisation of big data analytics based on the resource-based view (RBV) of the firm. Based on this conceptualisation, we examine the fit between the big data analytics resources that underpin the notion, and their interplay with organisational contextual factors in driving a firm's incremental and radical process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms are analysed by means of fuzzy set qualitative comparative analysis (fsQCA). Results show that under different combinations of contextual factors the significance of big data analytics resources varies, with specific configurations leading to high levels of incremental and radical process innovation capabilities.

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Section: Big Data Analytics Resourcesmentioning

confidence: 99%

Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities

Mikalef

Krogstie

2020

European Journal of Information Systems

178

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“…Evidence shows that, in some cases, data scientist teams are labeled separately, distinctively from IT and business teams. While these teams usually have well-defined roles, teams of data scientists aim to reduce the uncertainty that companies have about a broader range of problems (Costa & Santos, 2017), which, traditionally, are challenging to discover (Verma, 2017). Therefore, as data scientists are transversal across the structure, it appears to be creating a favorable environment for problem-solving through analytics.…”

Section: Results and Analysismentioning

confidence: 99%

Absorptive Capacity in Analytics Implementations: A Research Model

Ferreira

Gomes

Carvalho

2020

RAM, Rev. Adm. Mackenzie

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Purpose: Propose a research model that involves the conceptual relationships between the theoretical support of the absorptive capacity and analytics, emphasizing the way in which the tool contributes to the formation of absorptive capacity within the companies. Originality/value: It was possible to delineate a model with the theoretical associations between absorptive capacity and analytics from the voices of experts and decision makers of Portuguese companies, contributing as a tool to guide strategies that help companies for a better understanding of how the absorption of knowledge occurs in analytics implementations to better target efforts at incremental value generation. Design/methodology/approach: A qualitative study was carried out with the adoption of Grounded Theory as a research strategy. In this way, techniques and procedures of the method were followed that allowed the constant collection and analysis of the data, supported with the use of Atlas.ti software. Findings: The main theoretical contribution is the proposition of a conceptual model that considers emerging categories, subcategories, properties, and dimensions, that qualify the conceptual alignment between absorptive capacity and analytics and explain the existence of the construct over implementation. As entrepreneurial contributions, the configuration of knowledge absorption according to two analytic use patterns is emphasized: as oriented towards solving specific problems and as a guideline of institutional planning.

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“…We also extracted machine learning as a relevant competency for data analytics. With a rising industry of artificial intelligence, machine learning competencies offer new ways for data analytics (Costa et al, 2017;Debortoli et al, 2014;Prifti et al, 2017). As a summary, technical and technological data competencies can be seen as an enabler for many steps in the process of data analytics on the one hand and as a starting point for a future-oriented alignment of organizational data analytics on the other hand.…”

Section: Data Analytics Competencies As a Prerequisite For Business Vmentioning

confidence: 99%

“…Thus, we consider employees in organizations as a starting point for daily and operative analytical work, which is the foundation for using data and generating business value. While several authors have already researched business values with the help of data in general (Günther et al, 2017;Loebbecke & Picot, 2015;Shollo & Galliers, 2016) and also many authors have analyzed data analytics competencies from a business and education perspective (Cech et al, 2018;Cegielski & Jones-Farmer, 2016;Costa et al, 2017), we want to close a research gap by combining data analytics competency and business value by presenting it in a practical and comprehensive way for organizations. This leads us to the following research question (RQ) for our paper:…”

Section: Introductionmentioning

confidence: 99%

How Data Analytics Competencies Can Foster Business Value– A Systematic Review and Way Forward

Klee

Janson

Leimeister

2021

Information Systems Management

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Several authors have illustrated the benefits of data in organizations. For realizing benefits, we see two major challenges for organizations. First, there are necessary investments, which have to be justified. Second, adequate data analytics competencies can be seen as enablers for realizing benefits. We aim to support organizations by showing relevant competencies and achievable business values. We present theoretical propositions and a research agenda on how to move the field of data analytics competencies forward.

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The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age

Cited by 48 publications

References 5 publications

Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities

Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities

Absorptive Capacity in Analytics Implementations: A Research Model

How Data Analytics Competencies Can Foster Business Value– A Systematic Review and Way Forward

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