• Informatization Policy
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• v.28 no.1
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• pp.43-63
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• 2021

The purpose of this research is to examine the relationships among data literacy, organizational culture, and data analytics maturity and the moderating effects of organizational culture. Analysis of the relationship between data literacy and data analytics maturity shows that the higher the data literacy competency of employees, the higher the organization's data analytics maturity. In examining the relationship between organizational culture and data analytics maturity, it is found that relationship culture and innovation culture are positively related to data analytics maturity. In addition, relationship culture and hierarchy culture show significant moderating effects. Relationship culture shows a synergistic effect, whereas hierarchy culture has a buffer effect between data literacy and data analytics maturity.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.213-231
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• 2023

The purpose of this study is to develop and present a model that can evaluate the data maturity of library. To achieve this goal, library data maturity model can be applied to library was designed by analyzing previous studies related to data maturity. As a result of this study, proposed data maturity model consisting of 19 evaluation factors in 5 areas was designed, and the maturity level was set to 5 levels. In the future, it will be possible to measure the data maturity of libraries participating in the library big data project using the data maturity evaluation model, and it can be expected that in the long term, it will be possible to present a direction for data-based library operation and data utilization development.

• The Journal of Bigdata
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• v.6 no.1
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• pp.13-22
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• 2021

The purpose of this study is to develop and demonstrate a framework for evaluating the maturity of big data for effective data strategy establishment and efficient investment of companies. By supplementing the shortcomings of the evaluation developed so far, a framework was developed to evaluate the maturity of a company's big data in an integrated process. As a result, four evaluation areas of 'Vision and Strategy', 'Management', 'Analysis' and 'Utilization', assessment items for each area, detailed content, and criteria for each stage were derived. This was verified through a survey of entrepreneurs, and the maturity level of big data of domestic companies was confirmed. As a future research direction, it is proposed to develop detailed assessment factors according to the characteristics of each industry, to develop a data utilization framework according to the assessment results, and to improve validity and reliability through adjustment of verification targets.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.153-154
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• 2022

본 논문에서는 역연령으로 구분되었던 기존 생물학적 성숙도에 기반한 체력 지표가 아닌 골성숙도를 활용한 생물학적 성숙도에 기반하여 유소년의 신체에 맞는 체계적인 운동을 추천하는 기법을 제안한다. 이를 통해 유소년의 성장기에 개인화된 운동능력 발달을 성취하게 함으로써 국민 체력 증진에 기여하고 체육 공교육 활성화 및 유소년 피트니스 관리 산업 발전에 도움이 될 것으로 기대한다.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.219-227
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• 2020

Smart Factory is one of the fastest developing and changing fourth industrial revolution fields. In particular, the degree of introduction and maturity level in the smart factory is an important part. In this paper, a cluster analysis of companies introduced smart factory was performed based on a new maturity assessment model. The 68% of 193 companies surveyed were at the basic level, with only 21% being the middle one. Most SMEs cited lack of funds as the main reason for not entering the middle one. As a result of the cluster analysis, it was found that all clusters had similar patterns but grouped into one of three levels of high, middle, and low depending on maturity level of smart factory operation, and process domain had the highest maturity and data domain was lowest among the 6 domains. Through this, analysis of more specific and quantified maturity levels can be performed using 6-domain smart factory maturity evaluation model.

• Informatization Policy
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• v.24 no.1
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• pp.30-47
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• 2017

The Korean government developed an organizational maturity model for public data quality management based on international standards to evaluate the data quality management level of public organizations, However, as the model has too many indicators to apply on the site, a new model with reduced number of indicators is proposed in this paper. First, the number of processes is reduced by integrating and modifying the processes of the previous model. Second, a new maturity evaluation method is proposed based on capability levels focused on the activity, not on the process. Third, the maturity level of public data quality management is represented by five discrete levels or real values of 1 through 5. Finally, characteristics of the proposed model are compared with those of the previous model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1117-1132
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• 2019

Recently, IT technology such as internet, mobile, and IOT has rapidly developed, making it easy to collect data necessary for business, and the collected data is analyzed as a new method of big data analysis and used appropriately for business. In this way, data collection and analysis becomes easy. In such data, personal information including an identifier such as a sensor id, a device number, IP address, or the like may be collected. However, if systematic management is not accompanied by collecting and disposing of large-scale data, violation of relevant laws such as "Personal Data Protection Act". Furthermore, data quality problems can also occur and make incorrect decisions. In this paper, we propose a new data governance maturity model(DGMM) that can identify the personal data contained in the data collected by companies, use it appropriately for the business, protect it, and secure quality. And we also propose a over all implementation process for DG Program.

• The Journal of Bigdata
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• v.2 no.2
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• pp.87-93
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• 2017

The research on data quality has been performed for a long time. However, the research focused on structured data. With the recent digital revolution or the fourth industrial revolution, quality control of big data is becoming more important. In this paper, we analyze and classify big data quality types through previous research. The types of big data quality can be classified into value, data structure, process, value chain, and maturity model. Based on these comparative studies, this paper proposes a new standard, service quality of big data.

• Journal of Internet Computing and Services
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• v.8 no.2
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• pp.15-22
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• 2007

As we detect defects and eliminate them in early stages, we can make better quality software. For doing this task, we need to use a defect tracking system which con effectively track and manage defects that give severe effects on software quality. Those existing defect tracking systems have some weaknesses as we apply them to organizations that use CMMI for process improvements. Major problems of those systems are that they require the organizations to collect many types of defect data at a time without providing the proper explanation and even without the support of defect management process. The organizations at CMMI maturity level 2 and 3 have problems for analyzing those defects because there is no specific process area at CMMI maturity level 2 and 3 which directly handles defect managing activites. This paper resolves those problems by developing a defect tracking system which offers methods of managing defects. And the system provides guidelines of which defects should be gathered for each CMMI mathurity levels. The system also has functions to generate various status and statistic information on defects, and to assign defect data to the person in charge so that he or she track the defect to the closure

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.373-375
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• 2016

Analyze the Big Data become a hot issue because of Smart environment, the amount of data in the world has been exploding. Result of application makes a good use of Analysis and applicate of the big data, is play an important part in application area (finance, circulation, manufacturing, disaster etc.) This paper presents an influence element for data analysis and its practical use based in result of maturity in Business process of Big Data in Electrical Commercial system.