Hugh J Watson | The University of Georgia (original) (raw)

Papers by Hugh J Watson

Mis Quarterly Executive, 2004

for their comments and suggestions during the revision process.

International Conference on Information Systems, 1984

Decisionsupport systems are one of the latest developments incomputer-based information systems. ... more Decisionsupport systems are one of the latest developments incomputer-based information systems. There are a variety of indications that their development differs in important ways from othertypes of information systems. This article reports the findings of an investigation of how 18 decision support systems were developed. Six major areas were explored: (1) the nature of the developmental approach; (2) user involvement in system development; (3) the time required for system development; (4) the incorporation of the decision maker's style in the system; (5) the role of information systems and operations research/management science personnel in the developmental effort; and (6) specific procedures and techniques used in system development

Hawaii International Conference on System Sciences, Jan 5, 2004

Communications of the Association for Information Systems, 2001

Companies can build a data warehouse using a top-down or a bottom-up approach, and each has its a... more 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

Eight studies of data warehousing failures are presented. They were written based on interviews w... more Eight studies of data warehousing failures are presented. They were written based on interviews with people who were associated with the projects. The extent of the failure varies with the organization, but in all cases, the project was at least a disappointment. Read the cases and prepare a one or two page discussion of the following: 1. What's the scope of what can be considered a data warehousing failure? Discuss. 2. What generalizations apply across the cases? 3. What do you find most interesting in the failure stories? 4. Do they provide any insights about how a failure might be avoided? Case Studies of Data Warehousing Failures Auto Guys Auto Guys initiated a data warehousing project four years ago but it never achieved full usage. After initial support for the project eroded, management revisited their motives for the warehouse and decided to restart the project with a few changes. One reason for the restructuring, according to the project manager, was the complexity of the model initially employed by Auto Guys. At first, the planner for the data warehouse wanted to use a dimensional model for tabular information. But political pressure forced the system's early use. Consequently, mainframe data was largely replicated and these tables did not work well with the managed query environment tools that were acquired. The number of tables and joins, and subsequent catalog growth, prevented Auto Guys from using data as it was intended in a concise and coherent business format. The project manager also indicated that the larger the data warehouse, the greater the need for high level management supportsomething Auto Guys lacked on their first attempt at setting up the warehouse. Another problem mentioned by the project manager was that the technology Auto Guys chose for the project was relatively new at the time, so it was not accepted and did not garner the confidence that a project using proven technology would have received. This is a risk inherent in any "cutting edge" technology adoption. The initial abandonment of the project was undoubtedly hastened by both corporate discomfort with this new technology and the lack of top management support. A short time after dropping the project, top management felt pressure to reestablish it. Because Auto Guys initially planned an enterprise-wide warehouse, they had considerable computer capacity. It was put to use on a much smaller project that focused exclusively on a single subject area. Other subject areas were due to be added once the initial subject area project was completed. Auto Guys expects to grow the warehouse to two terebytes within a year or two and

Communications of the Association for Information Systems, 2009

Business intelligence (BI) is a broad category of applications, technologies, and processes for g... more Business intelligence (BI) is a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions. This tutorial discusses some of the early, landmark contributions to BI; describes a comprehensive, generic BI environment; and discusses four important BI trends: scalability, pervasive BI, operational BI, and the BI based organization. It also identifies BI resources that are available for faculty and students.

International Journal of Business Intelligence Research, 2024

Companies are moving from a cottage industry to a factory approach to analytics, especially in re... more Companies are moving from a cottage industry to a factory approach to analytics, especially in regard to machine learning (ML) models. This change is motivating companies to adopt ML operations (MLOps) as a methodology for the timely development, deployment, and maintenance of ML models in order to positively impact business outcomes. The adoption of MLOps requires changes in processes, technology, and people, and these changes are informed by previous work on decision support systems (DSS), development operations (DevOps), and data operations (DataOps). The processes, technologies, and people needed for MLOps are discussed and illustrated using a customer purchase recommendation example. Current and future directions for MLOps practice driven by artificial intelligence (AI) are explored. Suggestions for further academic research are provided.

Communications of the Association for Information Systems, 2013

Information systems faculty strive to educate students and provide them with the knowledge and sk... more Information systems faculty strive to educate students and provide them with the knowledge and skills they need to succeed as IS professionals. Given the dynamic nature of the IS discipline and workplace, what to teach our undergraduates, especially regarding the issue of balancing technical and "soft-skills," presents an ongoing challenge. While the ACM/AIS IS 2010 Curriculum offers faculty a place to start, we suggest an approach to enhancing traditional IS curriculum guidelines through active engagement with an industry Advisory Board. During the course of a fall and a spring meeting, we posed two questions to our MIS Advisory Board members: "If I only knew _____ [during college, first job, etc.]" and "How do we share and impart your wisdom with our students?" This article illustrates the method (modified Nominal Group Technique) we used to collect our Board's wisdom and advice, refine it, and deliver it to our undergraduate students. We also share several ways that our MIS faculty integrate professional development into our undergraduate MIS program.

John Wiley & Sons, Inc. eBooks, Sep 1, 1992

Management Information Systems Quarterly, Sep 1, 1993

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

Communications of the Association for Information Systems, 2024

In this historical perspective, I share my thoughts and experiences working with companies to eng... more 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.

Journal of Management Information Systems, 1997

Page 1. Information Requirements of Turnaround Managers at the Beginning of Engagements WILLIAM B... more 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. ...

International Journal of Business Intelligence Research, 2013

To understand and be successful with analytics, it is important to be precise in understanding wh... more 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.

Handbook on Decision Support Systems 2, 2008

... Insights about the methods and challenges of providing real-time data feeds are ... Therefore... more ... 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 ...

The Journal of Clinical Hypertension, 2009

Decision Support Systems, 1995

Executive information systems (EISs) are high-risk systems as evidenced by the large percentage o... more Executive information systems (EISs) are high-risk systems as evidenced by the large percentage of companies that have reported EIS failures. To minimize this risk and exploit the potential of EISs, it is important to study the keys to successful EIS development and operation. Forty-eight persons representing three groups of EIS stakeholders (EIS executive users, EIS providers, and EIS vendors and consultants), noted 23 factors important to successful EIS development and 46 factors important to successful operational EIS. The findings denote factors that had not previously appeared in the literature, rank order the factors across all three stakeholder groups, and note differences in stakeholder rankings. The findings integrate and extend the EIS literature by contributing two stable sets of factors that are suitable for further research.

The British Accounting Review, 1989

An academic directory and search engine.

What do we like about the IS field? This article is based on a panel discussion at the 2009 Inter... more What do we like about the IS field? This article is based on a panel discussion at the 2009 International Conference on Information Systems (ICIS) held in Phoenix, Arizona. The panel was sponsored by the Senior Scholars' Consortium. Given the recent enrolment downturn in IS programs and concerns expressed by some about the strength of the field, this article sets out the views of some senior scholars who describe what they like about the IS field.