A measurement model for factors influencing data quality in data warehouse

Zellal, Nouha; Zaouia, Abdellah

doi:10.1109/cist.2016.7805102

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…The contextual sense is that the rules reflect the logic of a particular business process, company knowledge, or environmental, social or other conditions. Dimensions and indicators of data quality in this study are: 1) Dimensions of effectiveness; indicators are accurate and consistent; and 2) Dimensions of usability; indicators are usability, accessibility and timely (Jaya et al, 2017;Zellal & Zaouia, 2017;Cai & Zhu, 2015;Debbarma et al, 2013).…”

Section: Data Qualitymentioning

confidence: 95%

“…The effect of data quality on the quality of business intelligence systems Gaardboe & Svarre (2018); Eder & Koch (2018), found that data quality is one of the keys to success in implementing a business intelligence system, while Zellal & Zaouia (2017); Sangar & Lahad (2013); Dawson & Van Belle (2013), found empirical facts that data quality is one of the critical success factors in a quality business intelligence system. These arguments suggest that data quality is likely to affect quality of business intelligence systems.…”

Section: Proposed Hypothesesmentioning

confidence: 99%

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determinant of business intelligence systems quality on Indonesian higher education information center

Murti

Mulyani

2022

lingcure

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PDDIKTI Feeder as a business intelligence application is used as an information center in higher education, containing master data of each student and lecturer, learning process data, reporting graduate data and lecturer activities in teaching for decision making. Paradoxically, through the empirical data there are many problems in implementing business intelligence systems in private universities, related to the maturity of information technology, data quality and information culture. Addressing this gap, we present a descriptive verification analysis research on 40 private universities in Bandung city, Indonesia, using the Partial Least Square Model. We conclude there is a positive influence of information technology maturity, data quality and information culture on the quality of the business intelligence system.

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Section: Data Qualitymentioning

confidence: 95%

Section: Proposed Hypothesesmentioning

confidence: 99%

determinant of business intelligence systems quality on Indonesian higher education information center

Murti

Mulyani

2022

lingcure

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“…For this reason, we found the chosen articles according to the topic discussed and we classified these research topics into three main areas including data quality impact, technical solution in the database area and technical solution in the computer science area. Most focus has been given to the technical solution in data goodness impact and technical solution in the database area (Zellal and Zaouia, 2016;Micic et al, 2017;Abdellaoui et al, 2016;Serra and Marotta, 2016;Izham Jaya, 2019). However, just five examination articles focus on the quality of data effect.…”

Section: Data Qualitymentioning

confidence: 99%

“…The period of data quality involves terminology such as data scrubbing, data approval, data manipulation, quality data tests, data refining, data separating and tuning. It is an essential area to keep up to keep the data warehouse reliable trustworthy for business customers (Zellal and Zaouia, 2016;Serra and Marotta, 2016;Tiwari et al, 2017;Prakash and Prakash, 2017;Sokolov and Turkin, 2018;Rana, 2016). At the end of this Systematic Literature Review (SLR), we can bethink of a new approach in managing data scrubbing to produce data quality in an integrated database and data warehouse.…”

Section: Data Scrubbing Data Quality and Their Impact On Data Ware House (Dwh)mentioning

confidence: 99%

A Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL Phase

Bahaa¹,

Eldemerdash²,

Fahmy³

2021

Journal of Computer Science

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Nowadays, no one can deny the importance of Data Ware House (DWH) in all organizations. The most important components in Data Ware House (DWH) are the Extraction, Transformation, Loading (ETL) phase. Data cleaning is a basic piece of the transformation stage in Data Warehousing. This may affect critical activities such as data collection and decision-making in various organizations Data Ops is an evaluation technique of Dev Ops in the data domain. This study conducts a Systematic Literature Review (SLR) to assess the previous studies of data warehouses related to Data Ops efforts. This study collects 55 primary studies related to the detection of Data Scrubbing, Data Consistency, Data warehouse, Dev Ops and Data Ops and we have conducted a Systematic Literature Review (SLR). Based on these findings, we discuss many concerns related to the study of current approaches in terms of abstraction level, metrics used, implementation and validation. That is why the analysis covers the published efforts between 2016 and 2021 since Data Ops is a significantly new technique. The survey should cover only research that took plan in recent years. The result of the study observed that 29% of the studies focused on solving the importance of data quality in the data warehouse, 62% of them focused on related Dev Ops, only 9% focused on Data Ops techniques and no 0% survey on enhancing ETL phase with Data Ops. This SLR brings to the attention of the research community several opportunities for using Data Ops in future research and the nearly proposed model DW Ops.

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A measurement model for factors influencing data quality in data warehouse

Cited by 2 publications

References 15 publications

determinant of business intelligence systems quality on Indonesian higher education information center

determinant of business intelligence systems quality on Indonesian higher education information center

A Systematic Literature Review for Implementing Data Ops in the Data Warehouse Lifecycle during the ETL Phase

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