• Asia pacific journal of information systems
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• v.12 no.4
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• pp.21-36
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• 2002

This study empirically examined the effects of data warehousing methodology on the success of data warehousing. Research model and two hypotheses were set up to identify the relationships among two types of data warehousing methodology(top-down/bottom-up, dw configuration) and success based on the investigations of such theories as data warehousing, data warehousing methodology, IS success and so forth. And the survey instrument(questionnaire) was developed to collect data. Ultimately 183 questionnaires from 61 korean firms were collected. Findings showed that two types of data warehousing methodology affect significant effect on the success of data warehousing.

• Journal of Information Technology Services
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• v.8 no.1
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• pp.73-97
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• 2009

The IT implementation literature suggests that various implementation factors play critical roles in the success of an information system; however, there is little empirical research about the implementation of data warehousing systems has unique characteristics that may impact the importance of factors that apply to it. In this study, a cross-sectional survey investigated a model of data warehousing success. Data warehousing managers and data suppliers from 51 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The purpose of this study is to identify which factors have a positive impact on the successful implementation of DW among organizational implementation success, project implementation success, and technical implementation success which are selected as implementation factors of DW. To do so, this study comprehensively analyze the previous studies on the success factors of the information system and DW to develop the study model and set the hypothesized. Then, it verified the hypothesis via the empirical analysis after collecting the materials through the survey. Finally, the discussion of the study's results, offered the implications of the findings, and the conclusions of the findings followed.

• Asia pacific journal of information systems
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• v.11 no.1
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• pp.175-203
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• 2001

This study empirically examined the effects of 12 factors on the success of data warehousing. We also introduced the characteristics of end-user community as moderating variables. Several hypotheses were set up to identify the relationships among these variables. And the survey instrument(questionnaire) was developed to collect data. Ultimately 183 questionnaires from 61 korean firms were collected. Findings showed that 9 factors affect positive effect on the success of data warehousing. Also the characteristics of end-user community can be considered significant moderating variables between success factors and success of data warehousing.

• Information Systems Review
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• v.14 no.2
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• pp.47-63
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• 2012

The concept of data warehousing was born in the 1970-80s at the same time when Wal-Mart's growing database gave birth to its need to view records in a more effective way. As the world's largest retail warehouse, Wal-Mart represents the world's largest data warehouse. A large portion of the discussionaround Wal-Mart's database management heretofore has only been process-centric, however, not its data per se. The study largely involves technical concepts around database and takes a deep look at Wal-Mart's chronicle of pre-implementation (preparation), implementation, and post-implementation (evaluation) of the data warehousing project. The highlight underlies how Wal-Mart acted against solving endless problems and was able to gear their data management to business contribution. By revisiting their data warehousing project case from a current point of view, we expect their lessons to be still available and applicable. With the renewed interest in (big) data and business intelligence, Wal-Mart's high respect for data and technology holds a lot more meaning than a couple of decades ago.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.103-114
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• 1999

Marketing decision support systems rely on an underlying enterprise information infrastructure. In traditional business situations, a limited number of product lines and markets divided into large chunks were adequately served by existing management information systems. With the advent of an increasingly segmented focus on niche markets and individual customers, the demand for market information has grown exponentially. The practical solutions offered by such data warehousing tools as OLAP and data mining directly address this need, allowing organizations to discover new niches. Marketing decision support systems built on these foundations provide organizations with new avenues for creating specifically targeted marketing strategies and promotional campaigns. The contribution of this article lies in introducing a graphical framework for data warehousing applications. Based on prior research, the framework links data warehousing and database marketing. To illustrate the effectiveness of this approach, three case examples of successful database marketing conclude the paper.

• International Commerce and Information Review
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• v.7 no.1
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• pp.55-74
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• 2005

Recently, many enterprises have attempted to construct data warehousing systems for decision-support. Data warehouse is an intelligent store of data that can aggregate vast amounts of information. Building DW requires two important development issues:(i) DW for the decision making of business users and (ii) metadata within it. Most DW development methodologies have not considered metadata development; it is necessary to adopt a DW development methodology which develops a DW and its metadata simultaneously. Metadata is a key to success of data warehousing system and is critical for implementing DW. That is, metadata is crucial documentation for a data warehousing system where users should be empowered to meet their own information needs; users need to know what data exists, what it represents, where it is located, and how to access it. Furthermore, metadata is used for extracting data and managing DW. However, metadata has failed because its management has been segregated from the DW development process. Metadata must be integrated with data warehousing systems. Without metadata, the decision support of DW is under the control of technical users. Therefore, integrating data warehouse with its metadata offers a new opportunity to create a more adaptive information system. Therefore, this paper proposes a DW development methodology from a metadata perspective. The proposed methodology consists of five phases: preparatory, requirement analysis, data warehouse (informational database) development, metastore development, and maintenance. To demonstrate the practical usefulness of the methodology, one case is illustrated

• Asia pacific journal of information systems
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• v.10 no.2
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• pp.213-236
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• 2000

Many enterprises continue to have an interest in the usage of new information technologies to gain a competitive advantage. In particular, their interest in the data warehouse and the data mining reveals the aspect of such a trend. Although lots of vendors announce a variety of tools for data warehousing, many a enterprise have a difficulty in building a robust data warehouse due to the lack of the ability of selecting an appropriate data warehouse technology options. Therefore, this study presents some evaluation factors, evaluation methods, and evaluation results about ETT tools, mainly in terms of a comparative test for the current available data warehousing ETT tools, Also, this paper suggests some guides about choosing the right ETT tools.

• The Journal of Information Technology
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• v.8 no.3
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• pp.83-103
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• 2005

The IT implementation literature suggests that various implementation factors play critical roles in the success of an information system; however, there is little empirical research about the implementation of data warehousing has unique characteristics that may impact the importance of factors that apply to it. in this study, a cross-sectional survey investigated a model of data warehousing success. Data warehousing managers and data suppliers from 51 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The results from a regression analysis of the data identified relationships between the system quality and data quality factors and perceived net benefits. It was found that management support and resources help to address organizational issues that arise during warehouse implementations, resources, user participation, and highly-skilled project team members increase the likelihood that warehousing projects will finish on-time, on-budget, with the right functionality; and diverse, unstandardized source systems and poor development technology will increase the technical issues that project teams must overcome. The implementation's success with organizational and project issues, in turn, influence the system quality of the data warehouse; however, data quality is best explained by factors not included in the research model.

• The Journal of Information Systems
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• v.17 no.4
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• pp.51-77
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• 2008

Empirical studies on the implementation of data warehousing systems(DWS) are few while there exist a number of researches on the implementation of IS. This article intends to examine the factors affecting the implementation success. of DWS. It adopts the empirical analysis of a sample of 112 responses from DWS practitioners. The study results suggest several implications for researchers and practitioners. When the support from top management becomes great, the implementation success of DWS in organizational aspects is more likely. The support of resources increases the implementation success of DWS in project aspects, while it is not significantly related to the implementation success of DWS in organizational aspects. The user involvement in systems implementation, however, affects the implementation success of DWS in organizational and project aspects. Prototyping for the DWS implementation positively affects the implementation success of DWS. This indicates that the extent of understanding the requirements and the communication among project members increases also the implementation success of DWS. The extent of consulting activities in DWS projects increases the implementation success of DWS in project aspects. The continuous support of consulting activities and technology transfers enhances the adherence to the project schedule, preventing the exceeding of the project budget and ensuring the implementation of the intended system functions.

• Asia pacific journal of information systems
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• v.8 no.3
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• pp.1-20
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• 1998

We propose an Executive Information Systems (EIS) Architecture for chief officers of the Air Force using data warehousing method. This architecture has two main proposes. The one is to provide the information for chief officers to control and command their organizations effectively by analyzing operation data at normal times. The other is to provide chief officers with the information about current situation so that they may make right and rapid decisions at emergency. The architecture introduced here is one that analyzes operational trends as well as current trends in a hierarchical organization environment. System analysis and design techniques for the Air Force EIS such as data flow diagram, system structure, entity-relational diagram, and third normal form of relational database were presented. After prototype screens are demonstrated, benefits of new EIS architecture were also discussed as a conclusion.