Nowadays, because of rapid data increase is brought by SNS (Social Networking Service) and the various sensor, effective utilization of big data is needed in many organizations. The purposes of this paper are to develop the extract framework on best practices and to discuss the current issues of big data utilization in Japan. Komatsu and T Point Japan were selected as best practices. The current issues are as follows: (i) the utilization of personal data, (ii) the effective use of open data, (iii) the development of human resources on big data utilization, and (iv) the difficulty of decision making based on analyzed results of big data.
In this paper, we give an introduction of big data from view point of statistics including definition of big data, features of big data. The cause of Big data trend is in the fall of prices for data collection, storage and communication. The remaining problem is the issue of data analysis. Statistics is a leading tool for analyzing big data. There are two approaches in statistics: estimation and descriptive statistics. Both approaches are useful for analyze big data.
This article introduces and reviews recent analyses methods on big data. We first introduce several kinds of definitions of big data based on Volume，Variety，Velocity, and Value extracted from data. Next, we describe that big data can be classified into three types – 1. various kinds of data with very large volume or producing very fast, 2. sparse data and 3. universal data –. Then we derive three important factors for utilizing big data – big data technologies, visualization and techniques for analyzing big data – and introduce the details of each factor corresponding to that three types of big data.
There are a lot of data all over the world. They are usually gathered by using the Internet. But the Internet is a best-effort type network. The data like a sensor data from lots of points have to be gather to one computer to analyze. This many-to-one traffic causes network congestion and makes the efficiency of the system decrease. We propose two methods of the big data acquisition method by using dynamic configuration of network. Two methods, i.e., GroupTable method and OFC method, are developed with using SDN, software defined networking. As the result of performance test, we concluded the system worked well as we expected and the GroupTable method is efficient for the big data acquisition system.
Keio Business School (KBS) is executing researches on business management based on HR (Human Resources) / education big data analytics funded by private corporations. This paper describes the research results and the important points to apply HR / education big data analytics to business management were extracted from the research results. Data collection / processing, data analytics and analytics results must be co-related to each other to create meaningful output for business management and the corporate organization, in which HR experts and technology experts can be collaborated from the viewpoint of business management must be developed to execute meaningful big data analytics.
In Japan, a lot of attention was paid to “big data” as the keyword from around 2012.Development of business models which livers obtain results from is featured in national strategies for utilizing ICT. In this paper, I proposed the idea of breaking big data utilization system down into patterns depend on liver’s perspective, and revealed each type’s signature with particular cases. And I pointed “smart type system” is not an ultimate aim and it is important to understand livers have a variety of needs.
The cases of analyzing big data can be roughly classified into five groups. These have come true due to the development of ICT. But, to realize customer’s needs or to fill up a complexity gap, interacting by information with customers and observing their behavior technically are significant. Also, to find out some useful data from big data and utilize it to develop new merchandises, skills to edit data and to frame hypotheses by abductive approach may be effective. It means the function of human-based information system, including information sharing and organizational learning, is significant.
There are two ways of entrepreneurship education at universities; bring entrepreneurs up and educating entrepreneurship. Both classes are based on lecture styles at universities in Japan. The second factor has been paid attention these days in the Japanese society as the importance of human resources with entrepreneurship is recognized. In this paper, we try to classify entrepreneurship education including an educational program named “Virtual Company” from a viewpoint of the learning model. In addition to this, it is suggested that Legitimate Peripheral Participation is useful in order to educate entrepreneurship