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

The Effect of Corporate Cultural Factors on the Data Warehousing Success: An Empirical Investigation

This study is aimed at identifying the impact of corporate cultural factors on successful development of data warehousing in the United Arab Emirates. The theoretical framework of the study is formulated based on analysis of related literature coupled with the information gained from interviewing data warehousing experts. Five hundred and eighty data warehouse users in 34 companies were surveyed to obtain their perceptions of the extent that each of 132 items had actually contributed to their firms’ DW success at different phases of development. Rigorous multivariate statistical analysis procedure has been followed to design and construct an overall model of DW success. The model has proven that all its independent variables have significant influence on the DW overall success and that corporate cultural factors have dominant impact on this success throughout the different phases of DW development

Outsourcing the logistics function: the supply chain role of third-party logistics service providers in UK convenience retailing

Logistics, defined as the process of strategically managing the procurement, movement and storage of materials; parts; finished inventory and related information flow through the organisation and its marketing channels, is increasingly being recognised as a vital part of an organisation’s marketing strategy. In many organisations, the logistics function is currently facing significant challenges. Pressures from increasing competition and high customer service-level expectations have created a need for more professional and better-equipped logistics services. Confronted with such competitive pressures, these organisations are faced with decisions of the make OR buy kind with regard to the logistics processes of supply and distribution. In addition, the emergence of a need to focus on core capabilities has led many organisations to contract out all, or part of, the logistics function to third-party providers. This paper explores the challenges of outsourcing logistics in the UK convenience-retailing sector

Data Warehouse Success Lead towards Supply Chain Efficiency

Data warehouse management is crucial challenge due to which most of the supply chain companies are facing the issues of data management. These challenges effect adversely on data warehouse success and ultimately effect negatively on supply chain efficiency. Different studies are carried out by different researchers on the area of supply chain, however, these studies are missing with the element of data warehouse management. Therefore, the objective of the study is to examine the factors that influence on data warehouse success and supply chain efficiency. Data were collected from warehouse employees working in Indonesian supply chain companies. Results of the study shows that data warehouse success is based on various factors such as system quality, information quality, service quality and relationship quality. These factors have positive association with data warehouse success and data warehouse success increases the supply chain efficiency. Thus, companies should focus on these elements to promote data warehouse success. This study is helpful for practitioners to promote supply chain efficiency through data warehouse success

Partly Cloudy, Scattered Clients: Cloud Implementation in the Federal Government

Since the issuance of a federal mandate in 2010 requiring federal government agencies in the United States of America to immediately shift to a “Cloud First” policy, agencies have struggled to adopt cloud computing. Previous research has examined hindrances to cloud computing adoption across industries in the private sector (Raza et al., 2015, Park and Ryoo, 2012, and Bhattacherjee and Park, 2012). While this research provides important insights on cloud computing adoption in the private sector, it devotes scant attention to challenges of cloud computing adoption in the federal government. This study seeks to fill this gap by examining the roles of Top Management Support and Information Security Awareness on cloud computing implementation success in the federal government. Institutional theory serves as the theoretical framework for this study

The Impact of Business Intelligence Tools on Performance: A User Satisfaction Paradox?

While Business Intelligence (BI) initiatives have been a top-priority of CIOs around the worldfor several years, accounting for billions of USD of IT investments per annum (IDC), academicresearch on the actual benefits derived from BI tools and the drivers of these benefits remainsparse.This paper reports the findings of an exploratory, cross-sectional field study investigatingthe factors that define and drive benefits associated with the deployment of dedicated BI tools.BI is broadly defined as an analytical process which transforms fragmented data ofenterprises and markets into action-oriented information or knowledge about objectives,opportunities and positions of an organization; BI tools are software products primarilydesigned and deployed to support this analytical process (e.g. data warehouse software, datamining software, digital dashboards applications).Building upon DeLoneand McLean’s (1992; 2002; 2003) information systems successmodel, we develop, test and refine a BI quality and performance model adapted for the specificpurpose, application, user group and technology of BI tools. The ultimate performancepredictors in this model are user satisfaction and the impact of BI tools on managerial decisionquality, both of which are determined by data quality.Partial Least Square (PLS) modeling is used to analyze data collected in a surveyadministered to IT executives of large Australian Stock Exchange (ASX) listed companies.The results confirm some of the theoretical relationships established in – especially theoriginal – DeLone-McLean model in the specific context of BI. More importantly, the resultsalso confirm the important role of explicit BI management as antecedent of benefits derived fromBI tools, and the key impact of data quality on managerial decision making and organizationalperformance.However, the results also reveal a ‘user satisfaction paradox’: In contrast to thepredictions derived from the DeLone-McLean model, organizational performance is negativelyassociated with user satisfaction with BI tools. Financial performance data collected for ex-post verification of this unexpected result confirm this paradox. We discuss BI-specificinterpretations of these unexpected findings and provide avenues for future research

An Empirical Investigation of the Impact of Data Quality and its Antecedents on Data Warehousing

Data warehousing is a topic of great interest in the business community, due to increasing business intelligence demands, coupled with increased data availability and processing capability. Despite large financial backing of data warehousing implementations, many fail. Little research has been conducted pertaining to data warehousing success. Traditional system success models (DeLone and McLean, 1992; Seddon, 1997) may be extensible to data warehousing, provided both infrastructure and business application aspects of the implementation are carefully considered, and provided increased attention is paid to the antecedents of data and system quality. Wixom and Watson (2001) conducted an empirical study examining the antecedents to data and system quality to data warehousing success, but found no statistically significant support for the data quality antecedents proposed. This paper reviews system success and data quality literature and proposes a new model for data warehousing success. The new model extends traditional system success models to data warehousing, but proposes a new set of data quality antecedents, which can be empirically examined