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

Overcoming Organizational Obstacles to Artificial Intelligence Value Creation: Propositions for Research

Artificial intelligence (AI) is the next technology revolution, and one which offers huge potential benefits for companies around the world. In fact, companies that learn how to use AI effectively will be positioned to maximize value creation using data in the emerging algorithmic economy. Uptake of AI has been limited, however, and there are mounting concerns associated with AI use. This paper explores what companies need to better understand about AI so they can make the most of this transformational technology. The paper develops a research framework and an associated research agenda intended to motivate practice-based research that will help organizations overcome obstacles for AI value creation

Enablers and Mechanisms: Practices for Achieving Synergy with Business Analytics

Business Analytics (BA) systems use advanced statistical and computational techniques to analyze organizational data and enable informed and insightful decision-making. BA systems interact with other organizational systems and if their relationship is synergistic, together they create higher-order BA-enabled organizational systems, which have the potential to create value and gain competitive advantage. In this paper, we focus on the enablers and mechanisms of synergy between BA and other organizational systems and identify a set of organizational practices that underlie the emergence of BA-enabled organizational systems. We use a case study involving a large IT firm to identify the organizational practices associated with synergistic relationships that lead to the emergence of higher-order BA-enabled organizational systems

Data Liquidity: Conceptualization, Measurement and Determinants

Despite the rhetoric that “data is the new oil” organizations continue to face challenges in data monetization, and we don’t have a reliable way to measure how easily data assets can be reused and recombined in value creation and appropriation efforts. Data asset liquidity is a critical, yet underexamined, prerequisite for data monetization initiatives. We contribute to the theorizing process by advancing a definition, conceptualization, and measurement of data liquidity as an asset level construct. Based on interviews with 95 Chief Data and Analytics Officers from 67 distinct large global organizations, we identify three determinants of data liquidity: inherent asset characteristics, structural asset characteristics, and asset environment characteristics. We theorize the existence of equifinal configurations that yield liquid data assets, configurations that should prove helpful to academics and practitioners seeking to understand data liquidity and its impact on firms’ data monetization efforts as well as society at large

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

The Current State of Business Intelligence in Academia

Current trends suggest that academia may be behind the curve in delivering effective Business Intelligence programs and course offerings to students. In December 2009 and 2010, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congresses and conducted surveys to improve the understanding of the state of BI in academia. This panel report describes the key findings and best practices that were identified. The article also serves as a “call to action” for universities regarding the need to close a widening gap between the BI skills of university graduates in Information Systems and other fields and BI market needs. The IS field is well positioned to be the leader in creating the next generation BI workforce. To do so, it is important for IS to begin moving on this opportunity now. We believe the necessary first step is for BI and IS leaders to advance the BI curriculum

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