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An Examination of The Path to Prescriptive Analytics

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The difficulty of demonstrating a significant return on investment from the use of advanced data analytics has led to a lack of utilization of this tool. The most likely explanation

The difficulty of demonstrating a significant return on investment from the use of advanced data analytics has led to a lack of utilization of this tool. The most likely explanation for this phenomenon is the difficulty of incorporating non-financial metrics in the higher levels of analysis that are fully salient and derived in a manner that can be understood and trusted by organizational leaders. Another challenge that has confounded the use of advanced analytics by the leadership of organizations is the widely accepted belief that models are oftentimes developed with an insufficient number of variables that are expected to have an impact, which inhibits extrapolation of results for use in real-world decision making. This research identifies factors that contribute to the underutilization of analytics models in managerial decisions by leadership of the produce industry, and explores a variety of potential tools including descriptive analytics and dashboards that are able to provide predictive, prescriptive, and more advanced cognitive methods of decision making for use by organizational leadership. By understanding the disconnect between availability of the advanced data analysis tools and use of such tools by organizational leadership, this research assists in identifying the programs and resources that should be developed and presented as opportunities for support in the industrial decision-making process. This dissertation explores why managers within the produce industry underutilize higher levels of data analytics and whether it is possible to increase their levels of cognitive comfort. It shows that by providing leadership with digestible and rudimentary business experiments, they become more comfortable with more complex data analytics and then are better able to utilize dashboards and other tools within their decision-making models. As experiments are explained to managers, they become as comfortable with conducting experiments as they are with dashboards, thus becoming comfortable with evaluating their benefits.

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  • 2020