Compromise between Small Data and Big Data

Purpose: The main purpose of this work was to present the problems of big data and to present how small data can be used as a complement to big data and its effects step by step in the big data through the concept of value chain. Recently, the big data market is growing rapidly around the world because data is processed on the basis of size, diversity, speed, accuracy and value. Specifically, this study focuses on its application to Jeju tourism. Research design, data, and methodology: The problems of big data were clearly presented using various real-world examples, and the verification of the compromise with small data and its effectiveness were conducted based on research papers discussing Jeju tourism using big data. The results of the study suggested the use of small data to solve and verify the representation of big data at the collection stage of the big data value chain, and the analysis stage suggested combining the identification of behavior patterns, the strengths of small data, and the exploration of causal relationships. Results: Among various stage, in the curation phase, a cause-and-effect analysis using small data suggested a solution to the problem of mistaking the correlation of big data as causality, and in the storage phase, a policy plan using small data was proposed to solve the problem of privacy infringement. The most important part of this study was the application stage, which discussed the specific problems of big data and the possibility of compromise between small data. As well, in the utilization phase, a combination of correlation and causality, as in the curation phase, was proposed. Conclusions: As an implication of this study, we used specific examples to present synergies when big data is compromised with small data. It also showed that the compromised use of big data and small data can contribute not only in terms of research but also in terms of practicality in tourism industry. Finally, we expanded the foundation of research in terms of academic research, discussing the trade-off utilization of big and small data.