Professor, Department of Management Information Systems
Academic Director, Executive Doctorate in Business Administration
Director of Research Projects and Reports, Institute for Business and Information Technology
Fox School of Business, Temple University
207G Speakman Hall (006-00)
1810 N. 13th Street, Philadelphia, PA 19122
Email: David.Schuff at temple.edu
Enabling Self-Service BI: A Methodology and a Case Study for a Model Management Warehouse
The promise of Self-Service Business Intelligence (BI) is its ability to give business users access to selection, analysis, and reporting tools without requiring intervention from IT. This is essential if BI is to maximize its contribution by radically transforming how people make decisions. However, while some progress has been made through tools such as SAS Enterprise Miner, IBM SPSS Modeler, and RapidMiner, analytical modeling remains firmly in the domain of IT departments and data scientists. The development of tools that mitigate the need for modeling expertise remains the “missing link” in self-service BI. By introducing a structured methodology for model formulation specifically designed for practitioners, this paper fills the unmet need to bring model-building to a mainstream business audience. The paper also shows how to build a dimensional Model Management Warehouse that supports the proposed methodology, and demonstrates the viability of this approach by applying it to a problem faced by the Division of Fiscal and Actuarial Services of the US Department of Labor.
[Reference: Schuff, D., Corral, K., St. Louis, R., and Schymik, G., “Enabling Self-Service BI: A Methodology and a Case Study for a Model Management Warehouse,” forthcoming in Information Systems Frontiers.]
The Benefits and Costs of Using Metadata to Improve Enterprise Document Search
People spend up to 20% of their time searching for documents they never find. While many argue that metadata can improve enterprise document search, in reality few organizations use metadata. This article describes the results of two experiments that evaluate the impact of metadata on enterprise document search effectiveness. The first study provides quantitative evidence of the increase in recall and precision from the use of metadata-enhanced searches. The second study demonstrates that simple metadata structures are nearly as effective as complex ones, implying that the cost of creating and maintaining metadata is likely much lower than one might expect.
[Reference: Schymik, G., Corral, K., Schuff, D., and St. Louis, R.D. “The Benefits and Costs of Using Metadata to Improve Enterprise Document Search,” Decision Sciences. 46(6) (December 2015). pp. 1049-1075.]
Data Science for All: A University-Wide Course in Data Literacy
Infusing data literacy into a curriculum is an unrealized opportunity for higher education to truly make an impact on the current generation as they prepare to move into the workforce. This paper describes the design and structure of a new, unique undergraduate elective course introduced into the curriculum of a large, public University in the Northeastern United States. The design of the course is designed to inspire an “evidence-based” mindset, encouraging students to identify and use data relevant to them in their field of study and the larger world around them. The paper includes the course goals mapped to specific learning objectives, examples of exercises and assignments, a reading list, and a course syllabus. Instructors and institutions interested in bringing data science concepts to a broad audience can use this course as a foundation to build their own curriculum in this area.
[Schuff, D., “Data Science for All: A University-Wide Course in Data Literacy,” forthcoming in A. Deokar, A. Gupta, L. Iyer, and M. Jones (Eds.), Annals of Information Systems: Analytics and Data Science: Advances in Research and Pedagogy.]