• The Korean Journal of Archival Studies
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• no.71
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• pp.237-272
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• 2022

In 2019, the pilot project to establish a record management system for administrative information datasets started in earnest under the leadership of the National Archives. Based on the results of the three-year project by 2021, the improved administrative information dataset management plan will be reflected in public records-related laws and guidelines. Through this, the administrative information dataset becomes the target of full-scale public record management. Although public records have been converted to electronic documents and even the datasets of administrative information systems have been included in full-scale public records management, research on the quality requirements of data itself as raw data constituting records is still lacking. If data quality is not guaranteed, all four properties of records will be threatened in the dataset, which is a structure of data and an aggregate of records. Moreover, if the reliability of the quality of the data of the administrative information system built by reflecting the various needs of the working departments of the institution without considering the standards of the standard records management system is insufficient, the reliability of the public records itself can not be secured. This study is based on the administrative information dataset management plan presented in the "Administrative Information Dataset Recorded Information Service and Utilization Model Study" conducted by the National Archives of Korea in 2021. A study was conducted. By referring to various data, especially public data-related policies and guides, which are being promoted across the government, we would like to derive quality evaluation requirements in terms of records management and present specific indicators. Through this, it is expected that it will be helpful for record management of administrative information dataset which will be in full swing in the future.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.79-99
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• 2010

As data exchange between business partners in e-business becomes more active, obtaining and managing reliable data is emerging as a pressing issue for corporations and organizations. For the resolution of data quality, this paper proposes a framework for data quality management with its scenario. The data quality management framework consists of three phases: data quality monitoring, data quality improvement and data application, each of which has three processes. In each process, necessity, functions, roles, and relationships among processes are specified. In order for users to directly apply the framework to the business field, a business scenario is given with examples of product identification and classification code systems widely used in e-business.

• The Journal of Bigdata
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• v.3 no.1
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• pp.33-40
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• 2018

With the advent of the fourth industrial revolution, each industry has been innovated with new concepts. New concept of each industry takes advantage of new information technologies based on big data infra. Thus quality control of big data is becoming more important. In this paper, we try to develop a framework of big data service quality through a case study. A 'Legal Tech' service was selected for the case study. Especially a big data quality framework was developed for a living law service in the Ministry of Justice.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.209-215
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• 2021

Data quality management is an important issue these days. Improve data quality by providing consistent metadata. This study presents algorithms that facilitate standard word dictionary management for consistent metadata management. Algorithms are presented to automate synonyms management of database metadata through web dictionary crawling. It also improves the accuracy of the data by resolving homonym distinction issues that may arise during the web dictionary crawling process. The algorithm proposed in this study increases the reliability of metadata data quality compared to the existing passive management. It can also reduce the time spent on registering and managing synonym data. Further research on the new data standardization partial automation model will need to be continued, with a detailed understanding of some of the automatable tasks in future data standardization activities.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.3-13
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• 2005

This study aims to establish the quality management method for spatial data construction process. Total 17 regulations related to spatial database construction and major aerial survey firms were analyzed in terms of working process, equipments, accuracy of results. It is standardized the process of making new digital maps using aerial photography, revising existing digital maps, producing digital elevation models and developing imagery maps. Quality checking factors for spatial data construction process are also established.

• Information Systems Review
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• v.13 no.1
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• pp.1-11
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• 2011

This study empirically investigated the effect of firm-level data quality on economic performance in the Korean financial industry during 2008~2009. The data quality was measured by data quality management process index and data quality criteria by Korea Database Agency, and financial firm performance data was acquired from Financial Statistics Information System of the Financial Supervisory Service. The result showed that the data quality has statistically significant impacts on financial firm performance such as sales, operating profit, and value added. If the data quality management process index increases by one, the value added can increase by 2.3 percent. Moreover, the data quality criteria increase by one, the value added can increase by 72.6 percent.

• 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.

• 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 the Korea Society of Computer and Information
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• v.27 no.12
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• pp.189-195
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• 2022

The Korean government has made it a major national task to contribute to the revitalization of the creative economy, such as creating new industries and jobs, by encouraging the private opening and utilization of public data. The Korean government is promoting public data quality improvement through activities such as conducting public data quality management level evaluation for high-quality public data retention. However, there is a difference in diagnosis results depending on the understanding and data expertise of users of the public data quality diagnosis tool. Therefore, it is difficult to ensure the accuracy of the diagnosis results. This paper proposes a public data quality management level evaluation domain rule mapping model applicable to validation diagnosis among the data quality diagnosis standards. This increases the stability and accuracy of public data quality diagnosis.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.63-70
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• 2006

This study aims to development of web-based process management system for spatial data construction. For developing this system work classification of basic surveying was standardized and quality management method for spatial data was established. Production process and work classification system for basic surveying such as control point surveying using GPS, leveling, aerial photographing, digital mapping, topographic mapping, digital elevation modeling, aerial photographic DB construction and digital orthophotomap was standardized. The status of the output and quality inspection for basic surveying project were analyzed, and the elements of quality inspection and data format for the type of outputs were analyzed. Based on standardized and analyzed contents, web-based process management system was developed after database and process was designed. The process management system consisted of process management, quality control, metadata management, and system management.