Business Intelligence Software for the Classroom: MicroStrategy Resources on the Teradata University Network

Faculty members are challenged with staying abreast of business intelligence and teaching the topic in relevant ways. The latest enhancement to the Teradata University Network (www.TeradataUniversityNetwork.com) is the addition of business intelligence software The website now offers MicroStrategy 7i, an interactive environment for business reporting and analysis and several MicroStrategy analytic modules that focus on analysis for specific business processes. The new software is available for hands-on use by faculty and students. This tutorial describes these business intelligence resources and provides several ways in which the resources can be used to create effective classroom experiences. The resources are available to all faculty and students at no cost by registering with the Teradata University Network

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

An Empirical Investigation of the Factors Affecting Data Warehousing Success

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 projects. 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 111 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The results from a Partial Least Squares analysis of the data identified significant 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\u27s 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

Introduction to the Data Warehousing Minitrack

The field of data warehousing has become increasingly important to organizations that work to stay competitive in today\u27s volatile business environment. This paper describes the recent growth of data warehousing along with future trends

Realizing Business Benefits through CRM: Hitting the Right Target in the Right Way

Many companies are in the early stages of implementing customer relation-ship management (CRM). Although CRM promises increased revenues, profits, and customer service, companies face potential failure because of the complex technical and organizational issues involved. Our research on CRM, and six company experiences in particular, illustrate three CRM ¡°targets¡± that companies aim for: implement a single or a few applications, create a strong infrastructure to support CRM, or use CRM to trans-form the organization. These targets have very different impacts and different challenges, as reflected in six lessons: They differ on both costs and benefits; sponsorship varies; each suggests a different evolution for a CRM effort; prepare to get your hands dirty in cleaning the data; ensure that the architecture will scale; and you can (sometimes!) teach old dogs new tricks

Understanding Fit and Appropriation Effects in Group Support Systems Via Meta-Analysis

Many previous papers have lamented the fact that the findings of past GSS research have been inconsistent. This paper develops a new model for interpreting GSS effects on performance (a Fit-Appropriation Model), which argues that GSS performance is affected by two factors. The first is the fit between the task and the GSS structures selected for use (i.e., communication support and information processing support). The second is the appropriation support the group receives in the form of training, facilitation, and software restrictiveness to help them effectively incorporate the selected GSS structures into their meeting process. A meta-analysis using this model to organize and classify past research found that when used appropriately (i.e., there is a fit between the GSS structures and the task, and the group receives appropriation support), GSS use increased the number of ideas generated, took less time, and led to more satisfied participants than if the group worked without the GSS. Fitting the GSS to the task had the most impact on outcome effectiveness (decision quality and ideas), while appropriation support had the most impact on the process (time required and process satisfaction). We conclude that when using this theoretical lens, the results of GSS research do not appear inconsistent

Continental Airlines Flies High with Real-Time Business Intelligence

Real-time data warehousing and business intelligence (BI), supporting an aggressive Go Forward business plan, have helped Continental Airlines transform its industry position from worst to first and then from first to favorite. With a $30M investment in hardware and software over six years, Continental has realized conservatively over$500M in increased revenues and cost savings in areas such as marketing, fraud detection, demand forecasting and tracking, and improved data center management. Continental is now recognized as a leader in real-time business intelligence based upon its scalable and extensible architecture, prudent decisions on what data are captured in real-time, strong relationships with end users, a small and highly-competent data warehouse staff, a careful balance of strategic and tactical decision-support requirements, its understanding of the synergies between decision support and operations, and changed business processes that utilize real-time data