Factors Affecting Data Warehousing Success - Assignment Example

Factors Affecting Data Warehousing success Name: Institution: Date: Factors Affecting Data Warehousing success Q1. The factors that affect data warehousing success verses those that affect any other information system Factors that affect information system are not different from those that affect data warehousing success. Researchers have explored the success of information system in many ways like measuring the users’ satisfaction, the perceived usefulness of specific applications, and service quality. Researchers have to treat information system success as being a multi-faceted construct and choose various appropriate success measures according to the research objectives and the phenomena being investigated. The relationships amongst different dimensions have to be explored. The factors that affect information system were listed as system quality, data quality and perceived net benefits which are similar to factors affecting data warehousing success. Whereas there may be other factors that affect information system success the three studied in this research also affect data warehousing success (Kaur & Aggrawal, 2013). The dimensions are related to each other. High levels of system quality and data quality means high levels of net benefits. Data quality describes the quality of data available at the data warehouse. Provision of high quality data to the decision makers in organization is the basic reason for development of data warehouse. Data completeness, consistency, and accuracy are important aspects of data quality within a warehouse. With regard to system quality, the emphasis is on the system itself. Performance measures for system quality include system integration, response time, flexibility and reliability. Integration and flexibility are particularly crucial for decision-support applications. Systems which integrate data from a variety of sources can enhance organizational decision making. Flexibility permits decision makers room for modification of applications according to the change in their information needs. System quality is one of the invaluable illustrations of data warehousing since a warehouse offers the infrastructure which integrates data coming from multiple sources while flexibility supports future and current users and applications (Amazon, 1996). A system showing high data quality as well as system quality leads to net benefits for various stakeholders such as organizations, groups of individuals and individuals. It increases decision-making productivity and result in transforming how people carry out tasks. It gives users a clear understanding of the context of the decision. The dimensions of system quality, data quality and perceived benefits were applied as three dimensions of success of data warehousing and also information system success. These factors are among the factors that affect information system success. In other words, system success is a reflection of data warehousing success. Implementation factors and implementation success have a bearing on the system success that means success of data warehousing. Q2. The most important findings from analyzing the study data Collection of data from a large sample of subjects was done to validate study hypothesis and there were important findings from the analysis of the data. Implementation success factors have an influence on the success of data warehousing. As predicted perceived net benefits was affected by data quality and system quality hence explaining the 37% of the dependent construct’s variance. Both factors had positive effects. It was important to note that project, organizational and technical implementation has no effect on the quality of data as demonstrated by the non-significant paths. The R squared value indicated that factors not included in the model are crucial in explaining the data quality variance (Amazon, 1996). Moreover, implementation success with project and organizational issues did have a bearing on the system quality. User participation, resources and team skills enhance the success of project implementation. Development technology and source system contributed to technical implementation success. Technical implementation success was not substantially related to system quality. Amazingly a champion for warehousing had no impact on the ability of the project to address organizational issues. Contrary to decision support applications that are likely to benefit from having an individual proponent, the wide scope as well as the far-reaching impact of data warehousing seem to call for broad-based support from a various sources. One individual being the project champion can desert the project at the first sing of any trouble or difficulties. The champion has limited knowledge and influence outside his area of the organization. In the same way, grassroots support may not be adequate for implementation success (Stair & Reynolds, 2013). The study has highlighted some of the challenges that managers are likely to face when working on a warehouse initiative. The findings demonstrate that having appropriate people on the project team, resources and user participation impact positively on the project' outcome. Warehousing requires a huge financial investment that can be challenging to market to management without guaranteed tangible benefits. The need for experienced warehousing personnel is more than the available supply. In-sourcing of experts is common regardless of the appropriate experience. The development technology applied seems to affect the technical problems that may come up in the course of implementation. The technical problems do not bear long-lasting effects that affect benefits drawn from operational warehouses. The study has focused on operational warehouses and not failed warehouses (Turban, McLean & Wetherbe, 2005). The findings demonstrate that the traditional factors like resources and management support further affect the data warehouse success hence offering further indications of existence of a common set of involving IT implementation factors. The study shows that implementation success models cannot be applied in investigating data warehousing unless with some modification. The study has contributed to the understanding the way in which factors of implementation can be grouped together Q3. The study findings contributions to our understanding of the factors that influence data warehousing success The study findings have enhanced our understanding of the factors that influence the success of data warehousing. System quality and data quality had significant correlation with perceived net benefits and explained a good part of the variance of construct. The outcome indicate that the quality of data warehouse as well as the data that it offers relates to the net benefits which are perce3ived data suppliers of the organization. Consequently a warehouse that has good data quality as well as system quality enhances the manner in which data is supplied to decision makers and decision support applications (Kaur & Aggrawal, 2013). Data warehouses has to maintain high quality data, respond flexibly to request for data by users, and integrate data in the manner needed by users for the purpose of creating value for the organization. Through the research we have understand the significance of quality of data on the success of warehousing. Perceived benefits have been discussed as one measure of data warehouse success. The research has boosted the perception that success dimensions like perceived benefits, data quality, and system quality are interrelated. The use of these multiple success dimensions has been supported in this study. It is observed that data quality and system quality have an impact on the perceived net benefits within the context of data warehousing (Amazon, 1996). Further research on the interrelationships of the dimensions is needed although it has already been show they are connected to each other. The level of implementation or use is another dimension that shows data warehousing success. Data quality influence data warehousing success but factors that affect data quality have not been discussed hence calling for further research. The role of cleansing, transformation and extraction on creation of high-quality data has to be investigated. The study suggests that more has to be done to understand what affects quality of data since it has an impact of the data warehousing success. Many questions regarding data quality have yet to be answered. System quality relay on a number of factors like the selection of data and subject areas for data store, the warehouse architecture selected, and the created underlying data model. Not all organizations have the knowledge and vision to accurate incorporate these considerations during the design of their warehouses hence prompting the possibility lack of the integration and flexibility of the data. The study indicates that system quality related to implementation success with project and organizational issues. When organizational barriers are removed, it is easy to create an integrated and flexible data warehouse. Technical implementation success had no bearing on system quality since operational and successful data warehouses means technical problems encountered were overcome. For the purpose of understanding the relationship between technical implementation success and system quality, warehousing projects that have failed have to be studied. The R squared value for system quality demonstrate that other factors which are not part of the research model influence the quality of the data warehouse. It has been understood that whereas system quality and data quality affect the data warehousing success, there are other factors that affect the two dimensions that have not explored in this research (Thomas & Fernández, 2008) Technical implementation of success is overcome where there is successful data warehousing. In order to widely and comprehensively understand factors that influence the success of data warehousing further research has to be done on the factors that affect system quality and data quality. References Amazon, A.C. (1996). Distinguishing the effects of functional and dysfunctional conflict on strategic decision making: Resolving a paradox for top management teams, Academy of Management Journal 39 (1): 123-148. Kaur, BP, & Aggrawal, H. (2013). Exploration of Success Factors of Information System, International Journal of Computer Science Issues, 10 (1): 1694-0814 Thomas, G. & Fernández, W. (2008) Success in IT projects: a matter of definition, International Journal of Project Management, 26 (7), 733-742. Turban, E., McLean, E. & Wetherbe, J. (2005). Information Technology for Management: Transforming Organizations in the Digital Economy (5th Ed.). New York: John Wiley & Sons, Inc. Stair, R. & Reynolds, G. (2013). Principles of Information Systems, New York: Cengage Learning Read More