How Can Big Data Complement Expert Analysis? A Value Chain Case Study
AbstractIn the world of big data, there is a need to investigate how data-driven approaches can support expert-based analyses during a technology planning process. To meet this goal, we examined opportunities and challenges for big data analytics in the social sciences, particularly with respect to value chain analysis. To accomplish this, we designed a value chain mapping experiment that aimed to compare the results of expert-based and data-based mappings. In the expert-based approach, we asked an industry expert to visually depict an industry value chain based on insights and collected data. We also reviewed a previously published value chain developed by a panel of industry experts during a national technology planning process. In the data-driven analysis, we used a massive number of business transaction records between companies under the assumption that the data would be useful in identifying relationships between items in a value chain. The case study results demonstrated that data-driven analysis can help researchers understand the current status of industry structures, enabling them to develop more realistic, although less flexible value chain maps. This approach is expected to provide more value when used in combination with other databases. It is important to note that significant effort is required to develop an elaborate analysis algorithm, and data preprocessing is essential for obtaining meaningful results, both of which make this approach challenging. Experts’ insights are still helpful for validating the analytic results in value chain mapping. View Full-Text
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Kim, K.; Lee, S. How Can Big Data Complement Expert Analysis? A Value Chain Case Study. Sustainability 2018, 10, 709.
Kim K, Lee S. How Can Big Data Complement Expert Analysis? A Value Chain Case Study. Sustainability. 2018; 10(3):709.Chicago/Turabian Style
Kim, Kyungtae; Lee, Sungjoo. 2018. "How Can Big Data Complement Expert Analysis? A Value Chain Case Study." Sustainability 10, no. 3: 709.
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