The main goal of this project is to investigate how companies that have embraced analytics differ from traditional firms in terms of decision-making quality, agility, innovation, and competitive performance. The students will address the key problem of understanding whether and how the use of data and analytics leads to superior business outcomes beyond financial metrics, such as improvements in customer satisfaction, operational efficiency, product innovation, and market responsiveness. By the end of the project, students are expected to deliver a well-supported comparative study highlighting the advantages, challenges, and limitations of analytics-driven decision-making, providing practical insights that can guide companies considering a transition toward a more data-driven approach.
To successfully complete this project, students will need to carry out several important tasks. They will conduct a comprehensive literature review on decision-making theories, analytics adoption, and business performance across industries. Students will identify and select case study firms that represent both traditional and analytics-driven approaches. They will engage in interviews, surveys, or secondary research to collect qualitative and quantitative data on decision-making practices, cultural differences, leadership styles, and performance outcomes. Students will then perform a comparative analysis to identify patterns, success factors, and pain points experienced by both types of firms. Additional tasks will include developing a framework or set of recommendations for how traditional firms can begin transitioning toward analytics-driven decision-making and outlining potential organizational and leadership interventions required to support this change. Finally, they will prepare a comprehensive report and an executive summary for presentation to academic or industry audiences.
