Visualisation of data to optimise strategic decision making - Moore, Jeanne (Department of Information Systems, 2017)
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 emotions, culture and experiences (Creswell, 2013; Lub, 2015). This supports the ontological view of the researcher, which is an interpretivist's view (Eriksson & Kovalainen, 2015; Ormston, Spencer, Barnard, & Snape, 2014). The epistemology was interpretivism, as the researcher interviewed executives and data analysts (Eriksson & Kovalainen, 2015; Ritchie, Lewis, Nicholls, & Ormston, 2013). Furthermore, literature relating to decision-making supported the researcher's interpretivist view, as people generally make decisions based on what they know at the time (Betsch & Haberstroh, 2014). Therefore, the researcher cannot separate the participant from his/her views (Dhochak & Sharma, 2016).The population for this research comprised of 13 executives tasked with strategic decision-making, as well as 4 data analysts who are either internal (permanent employees) or external (consultants) of the organisation within the private sector. 1.4 Conclusion: RQ1: What do individual organisational executives value and use in data and data visualisation for strategic decision-making purposes? Based upon the findings, to answer RQ1, organisational executives must first be clear on the value of the decision. No benefit will be derived from data visualisation if the decision lacks value. The executives also stressed the importance of understanding how data relevancy was identified, based on the premise used by the data visualisation developers. Executives also value source data accuracy and preventing a one-dimensional view by only incorporating data from one source. Hence the value of dynamism, or differing data angles, is important. In terms of the value in data visualisation, it must provide simplicity, clarity, intuitiveness, insightfulness, gap, pattern and trending capability in a collaboration enabling manner, supporting the requirements and decision objectives of the executive. However, an additional finding also identified the importance of the executive's knowledge of the topic at hand and having some familiarity of the topic. Finally, the presenter of the visualisation must also provide a guiding force to assist the executive in reaching a final decision, but not actually formulate the decision for the executive. 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? Based on the findings, to answer RQ2, themes of consumption, speed and confidence can be used. However; the final themes of use and trust overlap the initial 3 theme. Consumption is impacted by the data visualisation's ability to talk to the objective of the decision and the ability of the technology used to map the mental model and thinking processes of the decision-maker. Furthermore, data visualisations must not only identify the best decision, but also help the executive to define actionable steps to meet the goal of the decision. Executives appreciate the knowledge and skill of peers and prefer an open approach to decision-making, provided that each inclusion is to the benefit of the organisation as a whole. Benchmark statistics from similar industries also add to the consumption factor. Speed was only defined in terms of the data visualisation design, including the use of contrasting elements, such as colour, to highlight anomalies and areas of interest with greater speed. Furthermore, tolerance limits can also assist the executive in identifying where thresholds have been surpassed, or where areas of underperformance have occurred, focussing on problem areas within the organisation. Finally, confidence is not only impacted by the data visualisation itself but is also affected by the executive's knowledge of the decision and the factors affecting the decision, the ability of the data visualisation presenter to understand, guide and add value to the decision process, the accuracy and integrity of the data presented, the familiarity of the technology used to present the data visualisation and the ability of the data visualisation to enable explorative and collaborative methods for decision-making. RQ3: What elements should data analysts consider when developing data visualisations? Based on the findings, to answer RQ3, the trust theme identifies qualitative factors, relating to the presenter. The value, consumption and confidence themes all point to the relevance of having an open and collaborative organisational culture that enables the effective use of data visualisation. Collaboration brings individuals together and the power of knowledgeable individuals can enhance the final decision. In terms of the presenter, his/her organisational ranking, handling of complexity and multiple audience requirements, use of data in the data visualisation, ability to answer questions, his/her confidence and maturity, professionalism, delivery of the message when presenting, knowledge of the subject presented, understanding of the executive's objectives and data visualisation methodology, creation of a "WOW" factor and understanding the data journey are all important considerations.