Barbara H. Wixom

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 implementations 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.

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.

The Current State of Business Intelligence

Business intelligence (BI) is now widely used, especially in the world of practice, to describe analytic applications. BI is currently the top-most priority of many chief information officers. BI has become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation. BI is a process that includes two primary activities: getting data in and getting data out. Getting data in, traditionally referred to as data warehousing, involves moving data from a set of source systems into an integrated data warehouse. Getting data in delivers limited value to an enterprise; only when users and applications access the data and use it to make decisions does the organization realize the full value from its data warehouse. Thus, getting data out receives most attention from organizations. This second activity, which is commonly referred to as BI, consists of business users and applications accessing data from the data warehouse to perform enterprise reporting, OLAP, querying, and predictive analytics.

Data warehousing supports corporate strategy at first American corporation 1,2

From 1990 through 1998, First American Corporation (FAC) changed its corporate strategy from a traditional banking approach to a customer relationship-oriented strategy that placed FAC.s customers at the center of all aspects of the companys operations. The transformation made FACs an innovative leader in the financial services industry. This case study describes FACs transformation and the way in which a data warehouse called VISION helped make it happen. FACs experiences suggest lessons for managers who plan to use technology to support changes that are designed to significantly improve organizational performance. In addition, they raise interesting questions about the means by which information technology can be used to gain competitive advantage.

The BI-Based Organization

Business intelligence (BI) is an umbrella term that is commonly used to describe the technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help users make better decisions. For BI-based firms, BI is a prerequisite for competing in the marketplace. Though there are several possible BI targets, it is important to understand how they differ in terms of strategic vision, level of sponsorship, required resources, impact on people and processes, and benefits. Some companies like Harrah’s Entertainment, Continental Airlines, Norfolk Southern, and Blue Cross and Blue Shield of North Carolina are exemplars of BI best practices. Despite the progress made with BI, there are still many opportunities for academic research.

Investigating the Moderators of the Group Support Systems Use with Meta-Analysis

This paper presents a meta-analysis that investigates five moderators (task, tool, the type of group, the size of the group, and facilitation) and their influences on the overall effects of group support systems (GSS). Results show that process satisfaction is higher for idea-generation tasks than for decision-making tasks. The GSS tool (that is, the use of level 1 or level 2 GSS) influences decision quality. Level 1 tools support the exchange of information, whereas, level 2 tools are designed to aid in decision-making. Decision quality is higher when using level 2 tools, however, there is no difference in the number of ideas generated when using level 1 or level 2 tools. Decision quality is lower for virtual teams, but there is no difference in the number of ideas generated between virtual teams and face-to-face teams using GSS. Group size is an important moderator when measuring decision time and satisfaction with process. The former is shorter for larger groups, and the latter is higher for larger groups. Process facilitation leads to higher decision quality and higher satisfaction with the process. These results illustrate the importance of examining the moderators of GSS use and the viability of conducting a meta-analysis to investigate a large body of research with seemingly conflicting or equivocal results.

Real-Time Business Intelligence: Best Practices at Continental Airlines

Abstract Data management for decision support has moved through three generations, with the latest being real-time data warehousing. This latest generation is significant because of its potential for affecting tactical decision making and business processes. Continental Airlines is a leader in real-time business intelligence, and much can be learned from how they have implemented it.

The benefits of data warehousing: why some organizations realize exceptional payoffs

Data warehousing is one of the key developments in the information systems (IS) field. While its benefits are plentiful, some organizations are receiving more significant returns than others. The types of returns can vary in the impact they have on the organization and the ease in which they can be quantified and measured. This article presents a framework that shows how data warehouses can transform an organization; it also offers a compelling explanation for why differences in impact exist. Case studies of data warehousing initiatives at a large manufacturing company (LMC), the Internal Revenue Service, and a financial services company (FSC) are presented and discussed within the context of the framework. The analysis shows that the benefits that each company received can be tied to the way in which it conforms to the framework.

The Current State of Business Intelligence in Academia: The Arrival of Big Data

In December 2012, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congress 3 and conducted surveys to assess academia’s response to the growing market need for students with Business Intelligence (BI) and Business Analytics (BA) skill sets. This panel report describes the key findings and best practices that were identified, with an emphasis on what has changed since the BI Congress efforts in 2009 and 2010. The article also serves as a “call to action” for universities regarding the need to respond to emerging market needs in BI/BA, including “Big Data.” The IS field continues to be well positioned to be the leader in creating the next generation BI/BA workforce. To do so, we believe that IS leaders need to continuously refine BI/BA curriculum to keep pace with the turbulent BI/BA marketplace.

Continental Airlines Continues to Soar with Business Intelligence

Abstract As the business intelligence industry matures, it is increasingly important to investigate and understand the nature of mature data warehouses. Although data warehouse research is prevalent, existing research primarily addresses new implementations and initial challenges. This case study of Continental Airlines describes how business intelligence at Continental has evolved over time. It identifies Continentals challenges with its mature data warehouse and provides suggestions for how companies can work to overcome these kinds of obstacles.