Improvement of business process in order to manage the quality of information

Nasution, Wendri Syahreza; Albarda,

doi:10.1109/ictss.2013.6588084

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…In fact, the process-based DQ management methodology originates from total data quality management, whose objective is treating the processes of producing data as though it were manufacturing the physical products (Glowalla and Sunyaev, 2014; Yunus et al , 2018). For simply understanding the data manufacturing system, the IP-Map is designed to systematically visualize the important phases in manufacturing the information products (IPs) (Nasution and Albarda, 2013). Table 1 demonstrates the basic symbols in constructing IP-Maps (Lee et al , 2006; Shankaranarayanan and Wang, 2007) and Figure 1 shows a simple IP-Map example.…”

Section: Related Workmentioning

confidence: 99%

“…The major difference between workflow and IP-Map is that the former deals with a much coarser process granularity while the latter deals with the fine-grained processes related to manufacturing an IP. However, most of the current research on the IP-Map focuses on how to construct an IP-Map (Effendi, 2017), how to evaluate DQ by the IP-Map (Nasution and Albarda, 2013) or how to apply the IP-Map to improve DQ (Zong et al , 2019), mostly in the fields of medical, government and ERP implementation. As an extension of the previous research, the IP-Map is preferred in the current paper to systematically visualize the important stages in manufacturing scientific data to explore and identify the root causes of poor DQ in scientific data.…”

Section: Related Workmentioning

confidence: 99%

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Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

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Purpose This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data. Design/methodology/approach First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward. Findings The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data. Research limitations/implications As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders. Practical implications The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues. Originality/value This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

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“…that which efficiently meets the needs of those who use it (PAIM et al, 1996;CALAZANS, 2008, ALMEIDA, 2009. Considering information to be a product and therefore having measurable characteristics, the dimensions of information quality are studied by several authors (WANG and STRONG, 1996;WANG et al, 1998;NAUMANN and ROLKER, 2000;EPPLER and MUENZENMAYER, 2002;LEE et al, 2002;ALMEIDA, 2009;LEE and HAIDER, 2011;OSTROWSKI, 2012;FILIERI and MCLEAY, 2013;NASUTION and ALBARDA, 2013;JABAR and ALNATSHA, 2014;AYYASH, 2015;ZARRAGA-RODRIGUEZ and ALVAREZ, 2015). Although there are variations in how they are presented by each author --sometimes grouped into categories, and at other times presented individually being separated into subcategories as characteristics --the most prominent dimensions of information quality are: completeness, timeliness, accuracy, accessibility, relevance, reliability and security.…”

Section: Information Quality and The House Of Qualitymentioning

confidence: 99%

Strategic information quality utilizing the House of Quality

Koehler

Bastos

et al. 2020

Int J Innov Educ Res

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Living in the Age of Knowledge means living in search of innovation. That is, quality information and high valued added knowledge that can lead companies and individuals to the spotlight in a highly competitive and globalized world. Information is considered to be raw material for creating knowledge, which in turn, adds value to organizations, promotes innovation and puts the spotlight on organizations. For this reason, the objective of this study is to use a tool to analyze the quality of organizational strategic information in two phases. In the first phase, data and sources will be assessed; and in the second phase of the tool, strategic information and information guidance practices will be analyzed. Thereby, the study provides analyses of an organization a step-in advance, so as to improve its processes and tools to truly transform its strategic information into competitive advantage.

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“…Figure 2. Information process flow [18,19] To determine IQ dimension standard, we create IQ dimension-attribute catalogue. This catalogue describes what attributes will be used in IQ assessment.…”

Section: Probability Modelmentioning

confidence: 99%

Risk assessment of information production using extended risk matrix approach

Sembiring¹,

Wiharni²

2019

TELKOMNIKA

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In many cases poor information quality appears mainly due to in-effectiveness of information management including information production and delivery. Where this situation poses a certain risk. A holistic information risk management model has been previously proposed. But the model has some limitations especially on risk calculation and risk priority ranking as the model does not consider existing control effectiveness. In this paper, a new risk assessment method is proposed in order to improve the model of total impact of risks and to improve the accuracy of risk priority ranking by modifying the extended risk matrix approach (RMA) where we take into account the existing control effectiveness. Using our approach by adding a new dimension on extended RMA. We are able to improve the accuracy (7.15%) and reduced the ambiguity (1.34) of assessment results on real cases illustration.

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Improvement of business process in order to manage the quality of information

Cited by 6 publications

References 5 publications

Process-driven quality improvement for scientific data based on information product map

Process-driven quality improvement for scientific data based on information product map

Strategic information quality utilizing the House of Quality

Risk assessment of information production using extended risk matrix approach

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