• 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.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.345-350
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• 2007

최근 기업의 각 업무가 정보화 되면서 부문별, 업무별 정보시스템의 데이터 간에 심각한 중복성과 불일치성의 문제가 대두되면서 데이터 품질관리에 관심이 집중되고 있다. 본 연구는 실제로 데이터 표준 관리와 데이터 요구사항 관리를 통매 데이터 품질 관리 프로세스를 개선한 사례를 제시함으로써 데이터 품질 향상을 위해 노력하는 타 기업들에게 도움을 주고자 하였다. 또한, 개선된 데이터 품질 관리 프로세스에 대한 다차원적인 평가로서 데이터 품질, 생산성, 고객만족도, 조직 및 문화의 측면에서 정성 적이고 정량적인 지표를 통한 개선효과를 살펴보고 평가함으로써 제안된 프로세스에 의해 품질수준이 향상되었음을 검증하였고 평가 분석을 통한 시사점을 도출하였다.

• 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.

• Annual Conference of KIPS
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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.

• 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.

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.713-721
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• 2003

The time to market and productivity of embedded system needs a quality measurement process management of embedded software. But, defect management without preemptive analysis or prediction is not useful for quality measurement process management. This subject is focused on the defect that is one of the most important attributes of software measure in the process. Defining of defect attribute and quality measurement process management is according to understanding of embedded sw characteristics and defect data. So, this study contributes to propose the good method of the quantitative based on defect management in the test phase of sw lifecycle.

• 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.

• 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.

• 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 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.