• 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.

• The Journal of Society for e-Business Studies
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• v.7 no.3
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• pp.93-103
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• 2002

The combination of data warehousing and Internet technology produces a new concept - web warehousing. Due to the availability of web technologies and the need to make prompt decisions with timely information, web warehousing is emerging as a key strategic business weapon. Yet despite the many promising benefits of web warehousing, researchers have also identified several challenges, including scalability and availability. With the rise of the Internet and data centric computing applications, the use of new Storage Area Network (SAN) technology has been spotlighted for the possibility of a new data repository for web warehousing. In this article, the two new concepts of web warehousing and storage area networks are introduced. In particular, a SAN is discussed in detail as an alternative data repository to overcome the current limitations of web 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.

• The Journal of Information Technology and Database
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• v.7 no.2
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• pp.61-85
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• 2000

This study empirically examined the effects of 12 factors on the success of data warehousing. 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(having the right resources, championship, management support, management of user expectations, planning for the project, prototyping, user participation, quality of the data sources, having the right development tools) affect positive effect on the success of data warehousing.

• 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.

• 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.

• 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.

• IE interfaces
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• v.8 no.1
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• pp.61-78
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• 1995

Warehouse planning or design is not a static, one-time activity. To survive in the constantly changing market warehouse design should be a continuous process in which the anticipated requirment are reflected on the current warehouse status. Thus, the goal of warehouse management is to effectively utilize the various warehouse resources to meet the varying customer requirment. This paper presents an engineering framework for warehousing system analysis and design from the perspective of a total system approach. The complexity pertinent to warehousing system analysis and design is first discussed, which mainly come from the interplay among product data, order data, equipment types, operating strategies, and functional specification of the overall system. The understanding of the complexity leads to a general structure and a conceptual design procedure for a warehousing system. The general structure is comprehensive enough to represent most warehousing and flexible enough to accommodate the more sophisticated ones. The conceptual design procedure portrays the high-level interelationships the design issues.

• 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.