• ETRI Journal
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• v.28 no.2
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• pp.191-204
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• 2006

Many previous studies of data quality have focused on the realization and evaluation of both data value quality and data service quality. These studies revealed that poor data value quality and poor data service quality were caused by poor data structure. In this study we focus on metadata management, namely, data structure quality and introduce the data quality management maturity model as a preferred maturity model. We empirically show that data quality improves as data management matures.

• 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 Information Technology Services
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• v.18 no.1
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• pp.91-112
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• 2019

The IoT environment has led to explosive growth of existing enterprise data, and how to utilize such big data is becoming an important issue in the management field. In this paper, major factors affecting the decisions of companies to utilize big data have been studied. And also, the effect of big data utilization on the management quality is studied empirically. During this process, we have studied the difference according to the award of Korean national quality award. As a result of the study, we confirmed that the five factors such as cost from technology, organization and environment perspective, compatibility, company size, chief officer support, and competitor pressure are key factors influencing big data utilization. Also, it was confirmed that the use of big data for management activities has an important influence on the six management quality factors based on MBNQA, and that the management quality level of Korean national quality award companies is relatively high. This paper provides practical implications for companies' use of big data because it demonstrates for the first time that big data utilization has an impact on management quality improvement.

• 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.

• 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 Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.18-26
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• 2017

Defense industries which require high reliability need an optimized quality management system with well-planned implementation. And the government should examine the overall status of defense industries, then establish practical policies with a proper support plan in required areas to upgrade the quality management level of manufacturers. Thus, DTaQ developed the model for 2 years from 2014, which specialized in quality management level analysis for defense industries. And a survey has been undertaken with that model by DTaQ and Korea Research Center in 2016. The surveyed companies randomly sampled among those which have more than 30 employees and delivery history over past 3 years, and finally 106 defense industries were selected. This paper present survey method and indexes for survey of defense industry quality management level. The survey was conducted in the order of planning, data collection and data processing, and the validity and reliability of the data were verified to increase objectivity of survey results. The survey contents mainly consist of system quality and management quality. System quality includes Product Development Management, Production Operation Management, supply chain quality management, Safety & Environment Management and Reliability Management, on the other hand, management quality includes Strategic Leadership, Human Resource Management, Customer Market Management and Information & Knowledge Management. Thus this proposes the current overall quality management status of the 106 defense industries and shows level differences by company sizes and manufacturing sectors based on the result of survey. Specifically, this paper enables to track the areas which need prompt government support with the policy directions to make quality management level higher. Therefore, it is expected that this can be used as reference data in establishing quality policies for military supplies in the future.

• 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.

• 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.

• Information Systems Review
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• v.10 no.1
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• pp.91-113
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• 2008

Recently, as most functional business activities in an enterprise are supported by computerized information systems, data duplication and inconsistency among functional information systems become serious problems. It brings people to have many interests on data quality management. This paper presents a case study in which a company had improved their data quality by enhancing their data quality management processes. Though the case study, we describe main issues and risk factors in the process of data quality improvement projects as well as solutions to resolve the issues, which can be referred by other companies who pursue data quality improvement. Also, the improvement effects are evaluated by multidimensional perspectives which include quantitative and qualitative measures on data quality, productivity, customer satisfaction, organization, and culture.

• 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.