Hugh J Watson

Page 1. EIS-Requirements Determination Determining Information Requirements for an EIS Determining Information Requirements for an EIS Determining Information Requirements for an EIS Determining Information Requirements for an EIS Determining Information ...

In this historical perspective, I share my thoughts and experiences working with companies to engage and support academic research. I show the process from finding the right topic to deciding when it is time to move on to something new. As I go through my experiences, I will introduce 10 lessons learned to help in your research efforts. I also introduce three example professors who operate in different academic environments, have different academic and personal goals, and take different paths in working with the business community. I close by exploring the four evolutionary stages of academic IS research. The latest stage, big data/machine learning/artificial intelligence, offers new opportunities for engaging the business community, as well as impacting what academic IS research is and how it is conducted.

Page 1. Information Requirements of Turnaround Managers at the Beginning of Engagements WILLIAM B. FREDENBERGER, ASTRID LIPP, AND HUGH J. WATSON William B. Fredenberger is an Associate Professor of MIS at Valdosta State University, Valdosta, Georgia. ...

To understand and be successful with analytics, it is important to be precise in understanding what analytics means, the different targets or approaches that companies can take to using analytics, and the drivers that lead to the use of analytics. For companies that use advanced analytics, the keys to success include a clear business need; strong, committed sponsorship; a fact-based decision making culture; a strong data infrastructure; the right analytic tools; and strong analytical personnel in an appropriate organizational structure. These are the same factors for success for business intelligence in general, but there are important nuances when implementing advanced analytics, such as with the data infrastructure, analytical tools, and personnel. Companies like Amazon.com, Overstock.com, Harrah’s Entertainment, and First American Corporation are exemplars that illustrate concepts and best practices.

... Insights about the methods and challenges of providing real-time data feeds are ... Therefore, anyproposal must have a business partner who identifies and stands behind the ... of critical success factors is now seen in business performance management (BPM), digital dashboards ...

It has been almost 25 years since the original data warehouse was conceived. Although the term business intelligence (BI) has since been introduced, little has changed from the original architecture. Meanwhile, business needs have expanded dramatically and technology has advanced far beyond what was ever envisioned in the 1980s. These business and technology changes are driving a broader and more inclusive view of what the business needs from IT; not just in BI but across the entire spectrum—from transaction processing to social networking. If BI is to be at the center of this revolution, we practitioners must raise our heads above the battlements and propose a new, inclusive architecture for the future. Business integrated insight (BI2) is that architecture. This article focuses on the information component of BI2—the business information resource. I introduce a data topography and a new modeling approach that can support data warehouse implementers to look beyond the traditional h...

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 st...

This study constitutes the third in a series of ongoing surveys of the use of computer simulation in US industry. It explores such issues as which organizational groups develop and use simulations, the computer hardware and software used, and the level of usage of advanced simulation techniques. The benefits, effectiveness, and future of simulation in industry is also examined. A comparison of these findings with those of the previous two surveys identifies trends in simulation practice.

The analytics life cycle provides a road map for developing analytics models. As organizations progress along their analytics maturity journey, the successful execution of the life cycle becomes more challenging as the number of models developed and supported grow, the number and skill mix of people increase, and the need for defined processes and coordination becomes critical. This deep dive into the analytics life cycle reveals the problems, successful solutions, and best practices used by analytics-mature organizations.

Real-time business intelligence (BI) is taking Continental Airlines to new heights. Powered by a real-time data warehouse, the company has dramatically changed all aspects of its business. Continental’s president and COO, Larry Kellner, describes the impact of real-time BI in the following way: “Real-time BI is critical to the accomplishment of our business strategy and has created significant business benefits." In fact, Continental has realized more than $500 million in cost savings and revenue generation over the past six years from its BI initiatives, producing an ROI of more than 1,000 percent.

BI and analytics managers face many challenges and TDWI's conferences provide great opportunities to further develop one's managerial skills and keep up with the latest technology developments. Some of the TDWI conferences include a Strategy Summit-essentially a conference within a larger conference-which brings together BI, analytics, and business leaders to discuss how to best move forward with their firm's BI and analytics initiatives. These Strategy Summits include presentations by experts on specific, timely topics; case studies of companies that are BI and analytics exemplars; and roundtable discussions of the challenges facing BI and analytics leaders. At a recent Strategy Summit in Las Vegas, participants were asked to identify the major challenges they were facing, and then the entire group discussed possible solutions. The challenges included: ■ Obtaining and maintaining executive sponsorship ■ Changing the organizational culture for BI and analytics ■ Investing in BI and analytics staff (improving skills) ■ Understanding and meeting the needs of various kinds of users for self-service BI and analytics ■ Operationalizing analytics Some of these topics, especially the first three in the list, are seemingly timeless. In the case of self-service BI, this dream has existed for years, but the interest has grown recently. An emerging concern is operationalizing analytics

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss
Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the
context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring.

To develop the success metrics, the authors reviewed practitioner and academic literature and interviewed 20 leading authorities on BI and data warehousing. Two major constructs emerged as relevant: product and development measures. People and companies always want to know how they are doing. That's why they keep score, whether it is for golf or for business performance. The scores show how people and companies are doing over time, against goals or in comparison to others. Scores provide feedback and incentive to improve performance. In business, benchmarks are especially useful. They are helpful in answering the question: How are we doing, especially in comparison to other companies? This is true in the business intelligence (BI) and data warehousing world. People want to know how successful their BI and data warehousing initiatives are in comparison to other companies. We conducted a study (promoted by DM Review) that created metrics used for assessing the success of a data warehouse architecture and a company's use of BI. We collected survey data from 454 companies that can be used for benchmarking purposes. In this article, we discuss how the success measures were selected, what the success metrics are, the survey data that was selected, the benchmark data for the metrics and the use of the metrics. BI and Data Warehousing Success Metrics Both practitioners and academics have a long-standing interest in measuring success. BI practitioners need to know how successful they are and communicate this assessment to management. Academics need to be able to measure success in their research. To develop the success metrics, we reviewed practitioner and academic literature, and interviewed 20 leading authorities on BI and data warehousing. Two major success constructs emerged as relevant: product measures and development measures. Each of these constructs has component parts. Product Measures Information quality: The data warehouse should provide accurate, complete and consistent information. System quality: The data warehouse should be flexible, scalable and able to integrate data. Individual impacts: Users should be able to quickly and easily access data; think about, ask questions, and explore issues in new ways; and improve their decision-making because of the data warehouse and BI. Organizational impacts: The data warehouse and BI should meet the business requirements; facilitate the use of BI; support the accomplishment of strategic business objectives; enable

Every 10 years there has been a significant evolution in computer-based support for decision making. The next cycle, or generation, is due in the early 2020s and is starting to emerge. While this new cognitive generation has several important characteristics, the most significant will be the widespread use of artificial intelligence. This article
describes the cognitive generation and provides recommendations for how companies should prepare for it.