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Abstract | ||
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1. INTRODUCTION Business analytics (BA) is an emerging discipline which defines and promotes the use of techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions (Chen, Chiang, and Storey, 2012, p. 1166). BA tools, resources, as well as skills and experience in data analysis are in demand because proactive business organizations are quick in their attempts to capitalize on the benefits of big data (Manyika et al., 2011; Schroeck et al., 2012). The common understanding of big data focuses on the advantages of the sizeable volume of data available to individuals and organizations for analytics and insight generation. However, a more prevalent business view of big data extends this intuition to include the three Vs of data--its volume, variety, and velocity (Russom, 2011)--which bring into focus the potential difficulties of big data processing. With the promise of big data benefits and the challenges in its handling, it is predicted that in the next few years BA will become one of the managementu0027s top priorities (Gartner, 2012), especially in the area of data analysis in support of business decision-making and action planning. This has created an unprecedented demand for Information Systems (IS) graduates with higher degree qualifications and significant BA skills (Stubbs, 2015). Moreover, a recent study by the McKinsey Global Institute predicts that by 2018 the U.S. market alone will face a shortage of between 140,000 and 190,000 analytics professionals, in addition to 1.5 million managers and analysts to work in the big data space (Manyika et al., 2011). In order to cater for future demand of BA graduates, higher education institutions are designing new BA curricula drawing synergies from different disciplines such as Business, Statistics and Mathematics, Information Systems, and Computer Science (Stubbs, 2015). In spite of the enthusiasm of academic staff to deliver new BA programs, the effective delivery of new university BA courses faces numerous challenges. These challenges include issues such as unavailability of suitable teaching tools and resources, fast-changing technology and curriculum, and a shortage of versatile academic staff who can teach multidisciplinary content (Wixom et al., 2014). To compound these problems, BA curriculum includes complex and abstract subject matter, such as business statistics, that has been traditionally difficult to teach, especially to students with very little mathematical knowledge (Murtonen and Lehtinen, 2003; Mvududu, 2003; Prabhakar, 2008) and little exposure to business (Harmer, 2009). The project reported in this article addresses some of these challenges by adopting an innovative approach to teaching key foundational BA concepts, including data analysis in particular. We rely on studentsu0027 experiences with personal technology (such as smartphones, tablets, and laptops), their familiarity with visual interaction with computer software (such as that offered by gaming consoles), their intuitive understanding of business problems, and their general knowledge. Our approach follows current industry practices where interactive visualizations are being successfully deployed in business, science, and information management (Keim et al., 2008). In these deployments, the natural perception and cognitive abilities of humans are being utilized to visually interact with data in search of interesting features and patterns (Brodbeck, Mazza, and Lalanne, 2009). Specifically, this article reports on the design and evaluation of a learning environment which combines interactivity, information visualization, and storytelling--referred to as Interactive Visual Narratives (IVN)--to teach data analysis concepts to first-year undergraduate students in IS and Business studies. 2. RATIONALE FOR INTERACTIVE VISUAL NARRATIVES As we create BA curricula drawing on the reference disciplines, we are also confronted with a number of challenges. … |
Year | Venue | Field |
|---|---|---|
2016 | JISE | Data science,Information system,Information management,Business analytics,Business studies,Business statistics,Cultural analytics,Analytics,Big data |
DocType | Volume | Issue |
Journal | 27 | 4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dilal Saundage | 1 | 8 | 1.60 |
| Jacob L. Cybulski | 2 | 130 | 19.23 |
| Susan Keller | 3 | 9 | 3.30 |
| Lasitha Dharmasena | 4 | 0 | 0.34 |