Examining Quality Factors Influencing the Success of Data Warehouse

Increased organizational dependence on data warehouse (DW) systems has drived the management attention towards improving data warehouse systems to a success. However, the successful implementation rate of the data warehouse systems is low and many firms do not achieve intended goals. A recent study shows that improves and evaluates data warehouse success is one of the top concerns facing IT/DW executives. Nevertheless, there is a lack of research that addresses the issue of the data warehouse systems success. In addition, it is important for organizations to learn about quality needs to be emphasized before the actual data warehouse is built. It is also important to determine what aspects of data warehouse systems success are critical to organizations to help IT/DW executives to devise effective data warehouse success improvement strategies. Therefore, the purpose of this study is to further the understanding of the factors which are critical to evaluate the success of data warehouse systems. The study attempted to develop a comprehensive model for the success of data warehouse systems by adapting the updated DeLone and McLean IS Success Model. Researcher models the relationship between the quality factors on the one side and the net benefits of data warehouse on the other side. This study used quantitative method to test the research hypotheses by survey data. The data were collected by using a web-based survey. The sample consisted of 244 members of The Data Warehouse Institution (TDWI) working in variety industries around the world. The questionnaire measured six independent variables and one dependent variable. The independent variables were meant to measure system quality, information quality, service quality, relationship quality, user quality, and business quality. The dependent variable was meant to measure the net benefits of data warehouse systems. Analysis using descriptive analysis, factor analysis, correlation analysis and regression analysis resulted in the support of all hypotheses. The research results indicated that there are statistically positive causal relationship between each quality factors and the net benefits of the data warehouse systems. These results imply that the net benefits of the data warehouse systems increases when the overall qualities were increased. Yet, little thought seems to have been given to what the data warehouse success is, what is necessary to achieve the success of data warehouse, and what benefits can be realistically expected. Therefore, it appears nearly certain and plausible that the way data warehouse systems success is implemented in the future could be changed

Sherwin-Williams\u27 Data Mart Strategy: Creating Intelligence Across the Supply Chain

Companies can build a data warehouse using a top-down or a bottom-up approach, and each has its advantages and disadvantages. With the top-down approach, a project team creates an enterprise data warehouse that combines data from across the organization, and end-user applications are developed after the warehouse is in place. This strategy is likely to result in a scaleable data warehouse, but like most large IT projects, it is time consuming, expensive, and may fail to deliver benefits within a reasonable timeframe. With the bottom-up approach, a project team begins by creating a data mart that has a limited set of data sources and that meets very specific user requirements. After the data mart is complete, subsequent marts are developed, and they are conformed to data structures and processes that are already in place. The data marts are incrementally architected into an enterprise data warehouse that meets the needs of users across the organization. The appeal of the data mart strategy is that a mart can be built quickly, at relatively little cost and risk, while providing a proof of concept for data warehousing. The risk is that the initial data mart will not scale into an enterprise data warehouse, and what has been built will have to be scrapped and redone. This article provides a case study of Sherwin-Williams\u27 successful use of the bottom-up, data mart strategy. It provides background information on Sherwin-Williams, the data warehousing project, the benefits being realized from the warehouse, and the lessons learned. The case is a textbook example of how to successfully execute a data mart strategy. Video clips of interviews with key individuals at Sherwin-Williams help bring the case alive

Business Intelligence Implementation Success Framework: A Literature Review

In contemporary competitive business context, managers are increasingly using Business Intelligence (BI) as the technique and solution to improve their understanding of a business environment.  This paper introduces studies performed on the implementation of BI in SMEs between 2000 and 2015, and it is multi-purpose. First, it examines types of research questions addressed by studies of BI carried out in developing countries. Second, its purpose is to identify the gaps in the BI studies in these countries. Third, it aims to be a base for developing a framework for BI implementation success through the classification of the critical success factors (CSFs) found in the relevant literature. Due to this model, BI stakeholders can identify and understand the crucial factors behind the successful implementation of BI systems in SMEs. Keywords: business intelligence, small and medium-sized enterprises, literature review, critical success factors. DOI: 10.7176/EJBM/11-6-0

Flexibility – Designing for Optionality on Warehouse Modernization Projects

The Department of Defense relies upon a vast network logistics facilities and capabilities across the planet. Assets are largely sourced from the Defense Logistics Agency, which is considering a modernization project for their Eastern Distribution Center (EDC). It handles 25% of all materials that DLA supplies, and the facility is antiquated. In order to maximize their investment, flexible options for the modernization should be considered. This research was conducted as a case study of the EDC in order to develop a framework of flexibility for DoD decision makers going forward. This case study gathers subject matter expert knowledge from government and commercial decision makers to develop a framework for DoD managers moving forward into the future

Business Intelligence (BI) Critical Success Factors

Companies are increasingly focussing their information systems efforts around Business Intelligence (BI) solutions. The benefits realised from BI vary significantly from company to company. BI systems are now being used as extensions of Enterprise Resource Planning (ERP) systems as they consolidate, transform and analyse the vast amounts of data generated by the firm. Much attention has been given to the identification of critical success factors (CSF) associated with the adoption of ERP systems. However, there is only limited research that has focussed on the CSF associated with BI implementations as part of an ERP system environment. Hence, this research documents BI specific critical success factors that industry partners, venders or systems users have identified in their presentations at conferences, education forms or formal user group meetings

Researches on Cognitive Process and Their Applications to Organizational Change in Information Systems

Government intervention, industry cooperation, new vendors, and foreign competition are all factors that exert a powerful influence on the information technology, marketplace, and on the individual IS organization. When these influences need to change an organization, it is likely the culture or identity of the organization will be targeted for change. Because an organization is also composed of cognitive frameworks, there is an implication that the existing cognitive structures are in jeopardy. Thus, the cognitive component is important in how all members of organizations react and respond to change.This paper defines cognitive process and its related research history, introduces organizational change matters, tries to solve conflicts in organizational changes, and applies this topic to the information systems fiel

Implementation Issues of Enterprise data Warehousing and Business Intelligence in the Healthcare Industry

The healthcare industry is following the lead of other industries and finding value in enterprise data warehousing (EDW) and business intelligence (BI) tools. Healthcare organizations are leveraging these tools to provide a plethora of benefits realized through enhanced business operations and performance. The EDW combines data from multiple source systems across an enterprise, and BI tools extract the data in meaningful ways to enable managers to make the best informed decisions. As with all management information systems, there are technical issues to be considered that impact the design, build, implementation, and support of the system. These benefits and challenges are explored, as well as special considerations necessary for the healthcare industry compared to other industries utilizing data warehousing and business intelligence. This paper investigates these critical issues and provides suggestions to harness the implementation of EDW and BI in the healthcare industry

Enterprise Metadata Management: Identifying Success Factors For Implementing Managed Metadata Environments

Managed metadata environments (MME) are being employed in organisations that need to assure a consistent and efficient capture, integration and delivery of enterprise metadata. Initiatives to implement a MME in an organisation may be a daunting endeavour and various information systems have evolved over time to support such environments. The expert study at hand used a multi-round Delphi research method in order to identify critical success factors of these initiatives. Out of the ten critical success factors nominated through the early rounds, nine factors were found to be very-toextremely important and one factor moderately important. The identified success factors can be used as a basis for implementation frameworks in metadata management initiatives. An effective and efficient metadata management system is one of the key components of data and information management, and can greatly aid organisations‟ efforts toward improved information quality and governanc