• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Journal of Smart Tourism
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• v.1 no.3
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• pp.3-5
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• 2021

The advancement of Information Communication Technology has provided tourism researchers with a golden opportunity to access big data, which plays a critical role in smart tourism. Recognizing the current issue, this paper discusses the evolution of the literature on tourism big data focusing on conceptual understanding of and types of big data, and insights from big data analytics. Indeed, this article provides important research agenda for future tourism researchers who would like to conduct academic research about big data and smart tourism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.567-582
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• 2014

The purpose of this study is to qualitatively identify the typologies and characteristics of the big data marketing strategy in major companies that are taking advantage of the big data business in Korea. Big data means piles accumulated from converging platforms such as computing infrastructures, smart devices, social networking and new media, and big data is also an analytic technique itself. Numerous enterprises have grown conscious that big data can be a most significant resource or capability since the issue of big data recently surfaced abruptly in Korea. Companies will be obliged to design their own implementing plans for big data marketing and to customize their own analytic skills in the new era of big data, which will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees. This research employed a Q-study, which is a methodology, model, and theory used in 'subjectivity' research to interpret professional panels' perceptions or opinions through in-depth interviews. This method includes a series of q-sorting analysis processes, proposing 40 stimuli statements (q-sample) compressed out of about 60 (q-population) and explaining the big data marketing model derived from in-depth interviews with 20 marketing managers who belong to major companies(q-sorters). As a result, this study makes fundamental contributions to proposing new findings and insights for small and medium-size enterprises (SMEs) and policy makers that need guidelines or direction for future big data business.

• Journal of Advanced Navigation Technology
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• v.17 no.1
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• pp.9-15
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• 2013

Big data has a key engine of the new value creation and troubleshooting are becoming more data-centric era begins in earnest. This paper takes advantage of the big data, big data in order to secure the quality of the quality elements for ensuring the quality of Justice and quality per-element strategy argue against. To achieve this, big data, case studies, resources of the big data plan and the elements of knowledge, analytical skills and big data processing technology, and more. This defines the quality of big data and quality, quality strategy. The quality of the data is secured by big companies from the large amounts of data through the data reinterpreted in big corporate competitiveness and to extract data for various strategies.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.89-96
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• 2016

By reviewing preceding studies of big data and spatial big data, spatial big data was defined as one part of big data, which spatialize location information and systematize time series data. Spatial big data, as one part of big data, should not be separated with big data and application methods within the system is to be examined. Therefore in this study, services that spatial big data is required to provide were suggested. Spatial big data must be available of various spatial analysis and is in need of services that considers present and future spatial information. Not only should spatial big data be able to detect time series changes in location, but also analyze various type of big data using attribute information of spatial data. To successfully provide the requirements of spatial big data and link various type of big data with spatial big data, methods of forming sample points and extracting attribute information were proposed in this study. The increasing application of spatial information related to big data is expected to attribute to the development of spatial data industry and technological advancement.

• Spatial Information Research
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• v.21 no.5
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• pp.43-51
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• 2013

In this study, the concept of spatial big data and effective ways to build a spatial big data system are presented. Big Data is defined as 3V(volume, variety, velocity). Spatial big data is the basis for evolution from 3V's big data to 6V's big data(volume, variety, velocity, value, veracity, visualization). In order to build an effective spatial big data, spatial big data system building should be promoted. In addition, spatial big data system should be performed a national spatial information base, convergence platform, service providers, and providers as a factor of production. The spatial big data system is made up of infrastructure(hardware), technology (software), spatial big data(data), human resources, law etc. The goals for the spatial big data system build are spatial-based policy support, spatial big data platform based industries enable, spatial big data fusion-based composition, spatial active in social issues. Strategies for achieving the objectives are build the government-wide cooperation, new industry creation and activation, and spatial big data platform built, technologies competitiveness of spatial big data.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.959-974
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• 2013

We investigate the roles of statistics and statisticians in the big data era. Definition and application areas of big data are reviewed and statistical characteristics of big data and their meanings are discussed. Various statistical methodologies applicable to big data analysis are illustrated, and two real big data projects are explained.

• Journal of The Korean Association of Information Education
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• v.24 no.2
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• pp.201-214
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• 2020

Big data is gradually expanding in diverse fields, with changing the data-related legislation. Moreover it would be interest in big data education. However, it requires a high level of knowledge and skills in order to utilize Big Data and it takes a long time for education spends a lot of money for training. We study that in order to define Universal Big Data used to the industrial field in a wide range. As a result, we make the paradigm for Big Data education for college students. We survey to the professional the Big Data definition and the Big Data perception. According to the survey, the Big Data related-professional recognize that is a wider definition than Computer Science Big Data is. Also they recognize that the Big Data Processing dose not be required Big Data Processing Frameworks or High Performance Computers. This means that in order to educate Big Data, it is necessary to focus on the analysis methods and application methods of Universal Big Data rather than computer science (Engineering) knowledge and skills. Based on the our research, we propose the Universal Big Data education on the new paradigm.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.229-238
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• 2012

Within last 10 years, internet has become a daily activity, and humankind had to face the Data Deluge, a dramatic increase of digital data (Economist 2012). Due to exponential increase in amount of digital data, large scale data has become a big issue and hence the term 'big data' appeared. There is no official agreement in quantitative and detailed definition of the 'big data', but the meaning is expanding to its value and efficacy. Big data not only has the standardized personal information (internal) like customer information, but also has complex data of external, atypical, social, and real time data. Big data's technology has the concept that covers wide range technology, including 'data achievement, save/manage, analysis, and application'. To define the connected technology of 'big data', there are Big Table, Cassandra, Hadoop, MapReduce, Hbase, and NoSQL, and for the sub-techniques, Text Mining, Opinion Mining, Social Network Analysis, Cluster Analysis are gaining attention. The three features that 'bid data' needs to have is about creating large amounts of individual elements (high-resolution) to variety of high-frequency data. Big data has three defining features of volume, variety, and velocity, which is called the '3V'. There is increase in complexity as the 4th feature, and as all 4features are satisfied, it becomes more suitable to a 'big data'. In this study, we have looked at various reasons why companies need to impose 'big data', ways of application, and advanced cases of domestic and foreign applications. To correspond effectively to 'big data' revolution, paradigm shift in areas of data production, distribution, and consumption is needed, and insight of unfolding and preparing future business by considering the unpredictable market of technology, industry environment, and flow of social demand is desperately needed.