Description
The term Big Data is intuitively appealing and increasingly well accepted in academics as well as practices. Firms readily see the possibility of new business value from big data and future business opportunities. Although they are good understanding what Big Data captures that conventional data do not, the journey for Big Data is difficult and deeply frustrating, as widely known, because of its volume, variety, and velocity. They also get stuck how to collect and analyze Big Data because how-to advice is scarce on this subject and mostly aimed at experts. As a result, Big Data Analytics are considered difficult to implement. The paper discusses that big data have business value and develop a model for measuring its value. We also attempt to design an implementing framework for big data collection as the first step for analytics. This paper can contribute to provide a guideline for studying big data analytics.
Recommended Citation
Kim, Hak, "Big Data: The Structure and Value of Big Data Analytics" (2015). AMCIS 2015 Proceedings. 38.
https://aisel.aisnet.org/amcis2015/BizAnalytics/GeneralPresentations/38
Big Data: The Structure and Value of Big Data Analytics
The term Big Data is intuitively appealing and increasingly well accepted in academics as well as practices. Firms readily see the possibility of new business value from big data and future business opportunities. Although they are good understanding what Big Data captures that conventional data do not, the journey for Big Data is difficult and deeply frustrating, as widely known, because of its volume, variety, and velocity. They also get stuck how to collect and analyze Big Data because how-to advice is scarce on this subject and mostly aimed at experts. As a result, Big Data Analytics are considered difficult to implement. The paper discusses that big data have business value and develop a model for measuring its value. We also attempt to design an implementing framework for big data collection as the first step for analytics. This paper can contribute to provide a guideline for studying big data analytics.