• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.1
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• pp.65-77
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• 2017

The purpose of this paper is to develop a model of level evaluation for data governance that can diagnose and verify level of insufficient part of operating data governance. We expanded the previous study related on attribute indices of data governance and developed a level model of evaluation and items. The model of level evaluation for data governance is the level of evaluation and has items of 400 components. We used previous studies and expert opinion analysis such as the Delphi technique, KJ method in this paper. This study contributes to literature by developing a level evaluation model for data governance at the early phase. This paper will be used for the base line data in objective evidence of performance in the companies and agencies of operating data governance.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.1
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• pp.53-63
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• 2017

Managing the quantitative measurement of the data control activities in enterprise wide is important to secure management of data governance. However, research on data governance is limited to concept definitions and components, and data governance research on evaluation models is lacking. In this study, we developed a model of quantitative assessment for data governance including the assessment area, evaluation index and evaluation matrix. We also, proposed a method of developing the model of quantitative assessment for data governance. For this purpose, we used previous studies and expert opinion analysis such as the Delphi technique, KJ method in this paper. This study contributes to literature by developing a quantitative evaluation model for data governance at the early stage of the study. This paper can be used for the base line data in objective evidence of performance in the companies and agencies of operating data governance.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.57-66
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• 2017

The academic research on data governance is still in its infancy and focused on the definition of concept and components. However, we need to study of evaluation on data governance to help make decision of establishment. The purpose of this paper is to develop of attribute index in data governance framework. Therefore, in this paper, we used RGT (repertory grid technique) and Laddering techniques for experts interview and survey for validation of disinterested third party experts and analysis statistically. We completed data governance attribute index which is composed of data compliance area including 8 components, data quality area including 16 components and data organization area including 7 components. Moreover, the evaluation attributes is prioritized and ranked using the AHP. As a result of the study, this paper can be used for the base line data in introducing and operating data governance in an IT company.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.45-58
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• 2016

Data are important in an organization because they are used in making decisions and obtaining insights. Furthermore, given the increasing importance of data in modern society, data governance should be requested to increase an organization's competitive power. However, data governance concepts have caused confusion because of the myriad of guidelines proposed by related institutions and researchers. In this study, we re-established the concept of ambiguous data governance and derived the top-level components by analyzing previous research. This study identified the components of data governance and quantitatively analyzed the relation between these components by using DEMATEL and context analysis techniques that are often used to solve complex problems. Three higher components (data compliance management, data quality management, and data organization management) and 13 lower components are derived as data governance components. Furthermore, importance analysis shows that data quality management, data compliance management, and data organization management are the top components of data governance in order of priority. This study can be used as a basis for presenting standards or establishing concepts of data governance.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.811-830
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• 2022

Purpose: In this paper, we will discuss 1) prioritizing databases to be integrated; 2) which data elements should be emphasized in federated database integration; and 3) the degree of efficiency in the integration. This paper aims to lay the groundwork for building data governance by presenting guidelines for database integration using metrics to identify and evaluate the capabilities of the UK's air quality databases. Methods: This paper intends to perform relative efficiency analysis using Data Envelope Analysis among the multi-criteria decision-making methods. In federated database integration, it is important to identify databases with high integration efficiency when prioritizing databases to be integrated. Results: The outcome of this paper aims not to present performance indicators for the implementation and evaluation of data governance, but rather to discuss what criteria should be used when performing 'federated integration'. Using Data Envelope Analysis in the process of implementing intelligent data governance, authors will establish and present practical strategies to discover databases with high integration efficiency. Conclusion: Through this study, it was possible to establish internal guidelines from an integrated point of view of data governance. The flexiblity of the federated database integration under the practice of the data governance, makes it possible to integrate databases quickly, easily, and effectively. By utilizing the guidelines presented in this study, authors anticipate that the process of integrating multiple databases, including the air quality databases, will evolve into the intelligent data governance based on the federated database integration when establishing the data governance practice in the railway industry.

• The Journal of Bigdata
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• v.3 no.2
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• pp.1-10
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• 2018

In the field of smart water management, there is an increasing demand for strengthening competitiveness through big data analysis. As a result, systematic management (Governance) of big data is becoming an important issue. Big data governance is a systematic approach to evaluating, directing and monitoring data management, such as data quality assurance, privacy protection, data lifetime management, data ownership and clarification of management rights. Failure to establish big data governance can lead to serious problems by using low quality data for critical decisions. In addition, personal privacy data can make Big Brother worry come true, and IT costs can skyrocket due to the neglect of data age management. Even if these technical problems are fixed, the big data effects will not be sustained unless there are organizations and personnel who are dedicated and responsible for data-related issues. In this paper, we propose a method of building data governance for smart water data management based on big data.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.853-866
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• 2020

The purposes of this research were: 1) to investigate the effect of participatory management on financial information asymmetry, 2) to investigate the effect of corporate governance on financial information asymmetry, 3) to examine the influences of benefits incentives on financial information asymmetry, and 4) to test the mediating effects of benefits incentive that influences the relationship between participatory management, corporate governance, and financial information asymmetry. The research sample consisted of 388 Thai-listed firms. Data were collected through a survey questionnaire. Descriptive analysis, Multiple Regression Analysis, and Structural Equation Modeling were used for the data analysis. The results revealed: 1) participatory management and participation in evaluation had a negative influence on financial information asymmetry. 2) Corporate governance and the rights of shareholders had a negative influence on financial information asymmetry. 3) Benefits incentive was negatively associated with financial information asymmetry. 4) The model's influences of participatory management, corporate governance on the reduction of financial information asymmetry through benefits incentive as mediator fit the empirical data (Chi-square = 104.459, df = 84, p = 0.065, GFI = 0.967, RMSEA = 0.025). The variables in the model explained 78.00% and 4.70 % of the variance of benefits incentive and financial information asymmetry, respectively.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.3
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• pp.115-140
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• 2024

With the advent of the Fourth Industrial Revolution and the era of big data, the ability to handle data has become essential. This has heightened the importance and necessity of data literacy competencies. The purpose of this study is to propose a framework for data literacy competencies. To achieve this goal, data literacy frameworks from eight countries and twelve pieces of literature on data literacy competencies were analyzed and synthesized, resulting in five categories and twenty-three competencies. The five categories are: data understanding and ethics, data collection and management, data analysis and evaluation, data utilization, and data governance and systems. It is hoped that the data literacy competency framework proposed in this study will serve as a foundational resource for policies, curricula, and the enhancement of individual data literacy competencies.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.193-200
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• 2023

This paper selects 100 IT companies listed on the Shenzhen Stock Exchange from 2016 to 2020, and the public announcement in Hwajung collects ESG integrated ratings and grades for each sector and empirically verifies the relationship between ESG ratings and stock returns. Huazheng ESG level data and QIANZHAN database Using corporate financial data, a total of 500 samples were selected through correlation analysis and linear regression analysis with SPSS23 to analyze the effect of ESG on Return. As a result of the analysis, first, the impact on stock returns was found to be a significant positive (+) value for ESG integrated ratings and ratings by E (environment), S (social), and G (governance) sectors, confirming that ESG ratings have a positive mold of corporate stock returns. Currently, the world's major economies have proposed sustainable development strategies and "carbon neutral" goals. Development strategies are very consistent with ESG concepts, and companies that agree and execute ESG concepts may have higher ratings than other companies in the same industry, resulting in certain evaluation premiums. In addition, capital market performance in recent years shows that companies with ESG concepts or "carbon neutrality" concepts are generally considered to have higher growth potential and stronger anti-risk capabilities in the market. For listed companies, they should focus on ESG investment, improve ESG performance, and actively disclose related information to investors. Improving ESG performance should deliver positive information to society, enhance corporate image, increase market confidence in the future development of listed companies, and positively improve corporate value to actively increase financial, financial, trading, and other aspects of negotiation.