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Managing enterprise performance is an important, yet a difficult process due to its complexity. The process involves monitoring the strategic focus of an enterprise, whose performance is measured from the analysis of data generated from a wide range of interrelated business activities performed at different levels within the enterprise. This study aims to investigate management data systems technologies in terms of how they are used and the issues that are related to their effective management within the broader context of enterprise performance management (EPM).

A range of recently published research literature on data warehousing, online analytic processing and EPM is reviewed to explore their current state, issues and challenges learned from their practice.

The findings of the study are reported in two parts. The first part discusses the current business practices of these technologies, and the second part identifies and discusses the issues and challenges the business managers dealing with these technologies face for gaining competitive advantage for their businesses.

The study findings are intended to assist the business managers to effectively understand the issues and technologies behind EPM implementation.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Data warehousing is the technological trend for the corporate decision support process. This article investigates the current business environment of the data warehouse, including OLAP, data mining, data visualization and other technologies. This article also analyzes the importance of data warehouse management and maintenance and its future developments.

If you're awash in information you don't have time enough to analyze, invest in a data warehouse. It can give you access to data riches you didn't realize you had.

This paper examines the challenges that colleges and universities confront in the management of information necessary for strategic planning and decision making and explores data warehousing as an approach to knowledge management in academia.

Rapid innovation and globalization have generated tremendous opportunities and choices in the marketplace for firms and customers. Competitive pressures have led to sourcing and manufacturing on a global scale resulting in a significant increase in products. The paper tries to identify the need for real time business intelligence (BI) in supply chain analytics.

The paper provides argument and analysis of the advantages and hurdles in BI.

The paper focuses on the necessity to revisit the traditional BI concept that integrates and consolidates information in an organization in order to support firms that are service oriented and seeking customer loyalty and retention. Enhancing effectiveness and efficiency of supply chain analytics using a BI approach is a critical component in a company's ability to achieve its competitive advantage.

This paper furthers understanding of the issues surrounding the use of BI systems in supply chains.

The paper intends to find out the business justifications and requirements for incorporating business intelligence (BI) in organizations because many organizations that already have systems in place to collect data and gather information, often find themselves in a situation where they have no tools or roadmaps to put their vast data and information into use for strategic decision making.

In this paper BI and the growing potential for implementing BI is explained. The paper also explains a checklist for implementing BI.

During the last ten years, the approach to business management in the entire globe has deeply changed. Firms have understood the importance of enforcing achievement of the goals defined by their strategy through metrics‐driven management. Firms are evolving into new forms based on knowledge and networks in response to an environment characterized by indistinct organizational boundaries and fast‐paced change. New and complex changes are emerging that will force enterprises to operate in entirely new methods. Understanding the data and transforming, and shaping them into networked marketplaces is a key strategy for any organization to achieve competitive advantage. The business success factor for any enterprise is finding ways to bring the vast amount of data that are flowing within and across the business processes together and making sense out of them. Business Intercenine includes extraction, transformation and loading (ETL), data warehousing, database query and reporting, multidimensional/online analytical processing (OLAP) data analysis, data mining and visualization.

The paper provides useful information on business justifications and requirements for incorporating business intelligence in organizations.

This study illustrates the experience of scholar–practitioner collaboration for data-driven decision-making through the problematic of optimizing facility locations and minimizing logistics costs for La Palette Rouge (LPR) of Portugal.

The authors used a mixed mixed-method approach involving (1) a quantitative exploratory analysis of big data, which applied analytics and mathematical modeling to optimize LPR's logistics network, and (2) an illustrative case of scholar–practitioner collaboration for data-driven decision-making.

The quantitative analysis compared more than 20 million possible configurations and proposed the optimal logistics structures. The proposed optimization model minimizes the logistics costs by 22%. Another optimal configuration revealed that LPR can minimize logistics costs by 12% through closing one of its facilities. The illustrative description demonstrates that well-established resource-rich multinational enterprises do not necessarily have the in-house capabilities and competencies to handle and analyze big data.

The mathematical modeling for optimizing logistics networks demonstrates that outcomes are readily actionable for practitioners and can be extended to other country and industry contexts with logistics operations. The case illustrates that synergistic relationships can be created, and the opportunities exist between scholars and practitioners in the field of Logistics 4.0 and that scientific researcher is necessary for solving problems and issues that arise in practice while advancing knowledge.

The study illustrates that several Logistics 4.0 challenges highlighted in the literature can be collectively addressed through scholar–practitioner collaborations. The authors discuss the implications of such collaborations for adopting virtual and augmented reality (AR) technologies and to develop the capabilities for maximizing their benefits in mature low-medium technology industries, such as the food logistics industry.

This case is based on a real-life consulting engagement with a major Fortune 100 telecommunications company. The name of the firm has been disguised for confidentiality reasons. Completing the case teaches students how to develop a cost-containment ROI analysis and develop a business case for a large enterprise technology project. The class discussion focuses on strategies to understand and manage the risks of the project and organizational issues. In addition, the case teaches students good questions to ask when reviewing a complex project business case, and how to present a project for funding approval. This case is the second in a series of three cases designed to teach students ROI analysis for technology projects; the first is “B&K Distributors: Calculating Return on Investment for a Web-Based Customer Portal” and the third is the case “ROI for a Customer Relationship Management Initiative at GST.”

The case objective is for students to learn how to compute a return on investment (ROI) analysis for a large cost-containment technology project. Students learn the best practice of computing the range of possible outcomes (the best, worst, and expected case), and how to present the results to senior management. In addition, students learn how to incorporate important management issues of personnel reduction and technology project risk into an ROI analysis.

The digital economy has significantly altered the way business is conducted and financial information is communicated. A rapidly growing number of organizations are conducting business and publishing business and financial reports online and in real-time. Real-time financial reporting is likely to necessitate continuous auditing to provide continuous assurance about the quality and credibility of the information presented. The audit process has, by necessity, evolved from a conventional manual audit to computer-based auditing and is now confronted with creating continuous electronic audits. Rapidly emerging information technology and demands for more timely communication of information to business stakeholders requires auditors to invent new ways to continuously monitor, gather, and analyze audit evidence. Continuous auditing is defined here as “a comprehensive electronic audit process that enables auditors to provide some degree of assurance on continuous information simultaneously with, or shortly after, the disclosure of the information.” This paper is based on a review of related literature, innovative continuous auditing applications, and the experiences of the authors. An approach for building continuous audit capacity is presented and audit data warehouses and data marts are described. Ever improving technology suggests that the real-time exchange of sensitive financial data will place constant pressure on auditors to update audit techniques. Most of the new techniques that will be required will involve creation of new software and audit models. Future research should focus on how continuous auditing could be constantly improved in various auditing domains including assurance, attestation, and audit services.