Data analysis: essential to success in education and employment
The demand for professionals in the rapidly growing field of data analytics keeps rising, hence identifying, evaluating, and teaching data analytics competencies is an important goal for academic institutions and organisations in every sector. What are these competencies though? And how to identify and measure them for candidates for a data analytics position is an equally important question. Literature reviews reveal there are discrepancies between our ideas of the required skills and capabilities of the role but as yet there is no specific data analytics competency framework. The goal of this research was to create a list of the most important competencies to succeed in data analytics assignments, based on a carefully confirmed consensus between experts in the field.
Abstract
Analyzing data is now essential to success in education, employment, and other areas of activity in the knowledge society. Even though several frameworks describe the competencies and skills needed to meet current and future challenges, no data analytics competency framework exists to describe the importance of specific skills to succeed in data analytics assignments. In this article, we explore which competencies are required for effective data analytics by applying the Delphi technique and exploring the opinions of data analytics experts. Our results present a list of cognitive, intrapersonal, and interpersonal competencies, voted up by a consensus panel of experts in the field. Focusing on the three categories of essential competencies will help to better prepare students and employees for existing and future roles as citizens, employees, managers, parents, and volunteers. We urge policymakers, academic institutions, and educators to establish programs and reassess existing curricula and materials to support the development of these essential competencies.
Keywords
Big data, data analytics, competencies, Delphi technique, Q methodology, CRISP-DM model, virtual lab
About the Lead Author:
Orli Weiser
Orli is a doctoral candidate at the Ben Gurion University of the Negev. She holds a B.A. in Political Science and Computer Science from Bar-Ilan University and a M.A. with summa cum laude in Learning Technologies and Networks from The Open University of Israel. Her primary research interests are Technological innovations in learning and teaching and Data Analytics Competencies. Orli works as database administrator and volunteers in the "Ma'avarim" project, a supportive environment for research students.
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