• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.51-71
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• 2023

This study analyzed the government data quality management model, big data quality management model, and data lifecycle model for research data management, and analyzed the components common to each data quality management model. Those data quality management models are designed and proposed according to the lifecycle or based on the PDCA model according to the characteristics of target data, which is the object that performs quality management. And commonly, the components of planning, collection and construction, operation and utilization, and preservation and disposal are included. Based on this, the study proposed a process model for research data quality management, in particular, the research data quality management to be performed in a series of processes from collecting to servicing on a research data platform that provides services using research data as target data was discussed in the stages of planning, construction and operation, and utilization. This study has significance in providing knowledge based for research data quality management implementation methods.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.155-160
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• 2023

In principle, data from all databases and systems managed by the Ministry of Defense or public institutions must be guaranteed to have a certain level of quality or higher, but since most information systems are built and operated, data quality management for all systems is realistically limited. Most defense data is not disclosed due to the nature of the work, and many systems are strategically developed or integrated and managed by the military depending on the need and importance of the work. In addition, many types of data that require data quality management are being accumulated and generated, such as sensor data generated from weapon systems, unstructured data, and artificial intelligence learning data. However, there is no data quality management guide for defense data and a guide for selecting quality control targets, and the selection criteria are ambiguous to select databases and systems for quality control of defense data according to the standards of the public data quality management manual. Depends on the person in charge. Therefore, this paper proposes criteria for selecting a target system for quality control of defense data, and describes the relationship between the proposed selection criteria and the selection criteria in the existing manual.

• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.249-275
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• 2003

In companies competing for today's information society, Data quality deterioration is causing a negative influence to generate company competitiveness fall and new cost. A lot of Preceding study about data qualify have been proceeded in order to solve a problem of these data qualify deterioration. Among the sides of data qualify, it has been studied mainly on qualify of the data valve and quality of data service that are the results quality concept. However. this study studied structural qualify of the data which were cause quality concept in a viewpoint of meta data management and presented data quality management maturity model through this. Also empirically this study verified that data quality improved if the management level matured.

• The Journal of Society for e-Business Studies
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• v.14 no.3
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• pp.39-57
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• 2009

As electronic commerce becomes more common and the data volume of e-catalog increases, a systematic approach to data quality management is being required. Upon the necessity, we propose a process-based framework for e-catalog data quality management. This is the methodology for data management and improvement activities continuously performed to satisfy the expectation of industry to e-catalog systems. In the framework, contents for quality management consist of data, quality management items, and quality management processes. These are again subdivided according to organization levels, i.e, user, data administrator, and chief information officer.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.251-266
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• 2013

By the progress of informatization, the data which is involved in the administration and public organizations are increased the requestion of the utilization. Nevertheless most of the agencies could not actively participate in sharing and opening the data to the public because of data quality problems. The purpose of this study is to verify the relationship for data quality, managerial and organizational factors which is to derive at the level of the organization's data quality management success factors suggested in previous studies, and the acceptance of the organization's quality management. The result identify that organizational factors, organization's data quality management encouragement and support, give effect data quality through the acceptance of data quality management. However, managerial factors was no effect the data quality management acceptance. This study than managerial approach when considering the quality control for the public organizations, in the early days of the current situation of a company-wide consensus was required, as well as directly to the level of quality factors affecting the quality of acceptance is presented to derive but has significance.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.278-281
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• 2007

데이터 품질 기준은 반드시 현업의 입장에서 바라봐야 하며, 현업의 마인드가 데이터 품질에 가장 결정적인 영향을 미친다. 이에 따라 데이터 품질을 향상시키기 위해서는 현업이 데이터 품질 관리에 직접 참여할 수 있는 연구가 필요하다. 본 연구에서는 데이터 값(Data Value)에 대한 데이터 오너 (Owner)를 부여하여 데이터 품질 오류 시 현업이 직접 데이터 품질 관리 프로세스에 참여 할 수 있는 방안을 제시하였다. 데이터 품질 관리 프로세스는 데이터 품질 대상 및 기준을 정의하고 측정, 분석, 개선하는 방법이다. 본 연구에서 제시한 데이터 오너 관리 방안은 보다 효율적인 데이터 품질 관리 프로세스를 개선 시킬 수 있을 것이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.319-326
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• 2015

Rainfall data is necessary component for water resources design and flood warning system. Most analysis are used long-term hourly data of surface synoptic stations from the Meteorological Administration, Ministry of land, Infrastructure and Transport and others. However, It will be used minutely data of more high density automatic weather stations than surface synoptic stations expecting to increase the frequency of heavy precipitation. But minutely data has a problem about quality of rainfall data by auto observation. This study analyzed about quality control method using automatic weather station's minutely rainfall data of meteorological administration. It was performed assessment of the quality control that was classified quality control of miss Data, outlier data and rainfall interpolation. This method will be utilized when hydrological analysis uses minute rainfall data.

• Informatization Policy
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• v.22 no.1
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• pp.28-46
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• 2015

Although the demand for the use of public data increases in accordance with the expansion of Government 3.0, the poor level of data quality and its management currently implemented is becoming obstacles to opening data to the public. To improve the efficiency of management, linkage and usage for data, standardized processes for data quality management have to be prepared and appropriate data quality assessment criteria should be established. In this paper, we propose the organizational maturity model that can assess the public data quality management level. This model consists of the process reference model and the measurement framework. Fifteen processes grouped by the PDCA cycle are defined in the process reference model. The measurement framework measures the organizational maturity level based on process capability levels. The organizational maturity model can be used to establish objectives and directions for public data quality improvement by diagnosis of current level of public data quality management and problem solving. This model can also facilitate open to the private sector and activate usage of stable public data through reliability enhancement.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.83-105
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• 2013

There are two ways to assess data quality : measurement of data itself and assessment of data quality management process. Recently maturity assessment of data quality management process is used to ensure and certify the data quality level of an organization. Following this trend, the paper presents the process reference model which is needed to assess data quality management process maturity. First, the overview of assessment model for data quality management process maturity is presented. Second, the process reference model that can be used to assess process maturity is proposed. The structure of process reference model and its detail processes are developed based on the process derivation approach, basic principles of data quality management and the basic concept of process reference model in SPICE. Furthermore, characteristics of the proposed model are described compared with ISO 8000-150 processes.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.217-222
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• 2020

This study is about the data quality management evaluation model. As the information and communication technology is advanced and the importance of storage and management begins to increase, the guam feeling for data is increasing. In particular, interest in the fourth industrial revolution and artificial intelligence has been increasing recently. Data is important in the fourth industrial revolution and the era of artificial intelligence. In the 21st century, data will likely play a role as a new crude oil. It can be said that the management of the quality of this data is very important. However, research is being conducted at a practical level, but research at an academic level is insufficient. Therefore, this study examined factors affecting data quality management for experts and suggested implications. As a result of the analysis, there was a difference in the importance of data quality management.