Visualisation of data to optimise strategic decision making.
1.1 Purpose of the study: The purpose of this research was to explain the principles that should be adopted when developing data visualisations for effective strategic decision making. 1.1.1 Main problem statement: Big data is produced at exponential rates and organisational executives may not possess the appropriate skill or knowledge to consume it for rigorous and timely strategic decision-making (Li, Tiwari, Alcock, & Bermell-Garcia, 2016; Marshall & De la Harpe, 2009; McNeely & Hahm, 2014). 1.1.2 Sub-problems: Organisational executives, including Chief Executive Officers (CEOs), Chief Financial Officers (CFOs) and Chief Operating Officers (COOs) possess unique and differing characteristics including education, IT skill, goals and experiences impacting on his/her strategic decision-making ability (Campbell, Chang, & Hosseinian-Far, 2015; Clayton, 2013; Krotov, 2015; Montibeller & Winterfeldt, 2015; Toker, Conati, Steichen, & Carenini, 2013; Xu, 2014). Furthermore, data visualisations are often not "fit-forpurpose", meaning they do not consistently or adequately guide executive strategic decision-making for organisational success (Nevo, Nevo, Kumar, Braasch, & Mathews, 2015). Finally, data visualisation development currently faces challenges, including resolving the interaction between data and human intuition, as well as the incorporation of big data to derive competitive advantage (Goes, 2014; Moorthy et al., 2015; Teras & Raghunathan, 2015). 1.1.3 Research Questions: Based on the challenges identified in section 1.1.1 and 1.1.2, the researcher has identified 3 research questions. RQ1: What do individual organisational executives value and use in data and data visualisation for strategic decision-making purposes? RQ2: How does data visualisation impact on an executive's ability to use and digest relevant information, including on his/her decision-making speed and confidence? RQ3: What elements should data analysts consider when developing data visualisations? 1.2 Rationale: The study will provide guidance to data analysts on how to develop and rethink their data visualisation methods, based on responses from organisational executives tasked with strategic decision-making. By performing this study, data analysts and executives will both benefit, as data analysts will gain knowledge and understanding of what executives value and use in data visualisations, while executives will have a platform to raise their requirements, improving the effectiveness of data visualisations for strategic decision-making. 1.3 Research Method: Qualitative research was the research method used in this research study. Qualitative research could be described as using words rather than precise measurements or calculations when performing data collection and analysis and uses methods of observation, human experiences and inquiry to explain the results of a study (Bryman, 2015; Myers, 2013). Its importance in social science research has increased, as there is a need to further understand the connection of the research study to people's…
Advisors/Committee Members: Johnston, Kevin.
to Zotero / EndNote / Reference
APA (6th Edition):
Moore, J. (2017). Visualisation of data to optimise strategic decision making. (Masters Thesis). University of Cape Town. Retrieved from http://hdl.handle.net/11427/25478
Chicago Manual of Style (16th Edition):
Moore, Jeanne. “Visualisation of data to optimise strategic decision making.” 2017. Masters Thesis, University of Cape Town. Accessed December 17, 2017.
MLA Handbook (7th Edition):
Moore, Jeanne. “Visualisation of data to optimise strategic decision making.” 2017. Web. 17 Dec 2017.
Moore J. Visualisation of data to optimise strategic decision making. [Internet] [Masters thesis]. University of Cape Town; 2017. [cited 2017 Dec 17].
Available from: http://hdl.handle.net/11427/25478.
Council of Science Editors:
Moore J. Visualisation of data to optimise strategic decision making. [Masters Thesis]. University of Cape Town; 2017. Available from: http://hdl.handle.net/11427/25478