Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research
探讨了学术界与企业在利用大数据进行供应链研究时面临的合作障碍，提出了一个实用路线图，并通过案例展示了如何结合管理理论与大数据分析解决实际问题。
Increased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression‐based process for analyzing big data.