Abstract
The Fourth Industrial Revolution (4IR) has brought upon disruptive trends and fundamental change in the way we live and work. 4IR and its related technologies such as the Internet of Things and big data analytics, has resulted in businesses generating large amounts of data daily. The speed, breadth and depth of this revolution are forcing organisations to rethink how to create value from what is available. With organisations now being able to generate massive amounts of complex data, decision-makers have opportunities to derive insights from the data through advanced data analytics and data visualisations.
One opportunity presented by large amounts of data for managers is to calculate a wide range of performance measures. The measurement of performance is significant in all areas of business to understand, manage and continuously improve the way of doing things. Managers need to continuously find meaning in data through these measures to be able to make decisions that will help achieve the company’s goals. For a performance measure to inform decisions effectively, it needs to be clear and well presented. Data visualisation is the presentation of data in the form of pictures and/or graphs that summarise information in a more comprehensible and clear manner with the purpose of gaining insights, drawing conclusions and enabling efficient communication. When performance data is characterised by high volumes and complexities, it may pose challenges to management in analysing and deriving meaningful insights, which may subsequently result in the risk of decisions being based on inaccurate underlying assumptions. This research aimed to determine the influence of performance data visualisation on managers’ decision-making and to investigate visualisation techniques that will enable better understanding, analysis and presentation of complex data to support decision-making.
To achieve the objectives of the study, an in-depth literature analysis of the existing body of knowledge was conducted to identify the influence of performance data visualisation on management decision-making and to identify performance data visualisation techniques. The findings from the literature analysis were validated with the use of a quantitative survey which targeted engineering managers, plant managers, operations managers and functional managers across various industries in South Africa who use data visualisations to represent their performance data. Eight influences of performance data visualisations were identified from the literature, with seven being confirmed by targeted industry practitioners. The influences that were confirmed are: improves data understanding; supports uncovering of new insights; supports performance monitoring; accelerates response speeds; enhances decision confidence; enhances communication, collaboration and data sharing; and facilitates data exploration. Practitioners also used various data visualisation techniques, with the most frequently used techniques being bar charts, line charts, tables, pie charts, histograms and heatmaps.
The findings of this research, particularly the visualisation techniques, can provide guidelines for practitioners on what visualisation techniques to use for their performance data. The data visualisation influences identified may contribute to the current awareness and knowledge of how visualising performance data can influence the decisions made by managers.