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| 1997 and 1998 Working Paper Abstracts |
| TDQM-97-01: January 1997
Manage Your Information as a Product
by by Richard Wang, Yang Lee, Leo Pipino, and Diane Strong |
| Information quality is a critical issue to organizations. In order to deliver high-quality information to their internal and external customers, firms must treat information as product. All too often, they treat information as by-product. This paper presents four key principles to manage information as product: (1) understand the consumer's information needs, (2) manage information as prodcut the of a well-defined production process, (3) manage information as a product with a life cycle, and (4) appoint an Information Product Manager to manage the information product. A set of real-world cases are used to illustrate these principles. |
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Published in Sloan Management Review, Summer 1998 Volume 39 Number 4 pp. 95-105 |
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TDQM-97-05: April 1997 Total Data Quality Management: The Case of IRI by by Rita Kovac, Yang W. Lee and Leo L. Pipino |
| Implementing a Total Data Quality Management (TDQM) program is not a trivial undertaking. Two key steps are (1) to clearly define what an organization means by data quality and (2) to develop metrics that measure data quality dimensions and that are linked to the organization's goals and objectives. This paper presents the case of Information Resources, Inc., which exemplifies how a company can develop a viable TDQM program |
| Published in the 1997 Conference on Information Quality, pp. 63-79 |
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TDQM-97-06: June 1997 Information Quality Benchmarks: Product and Service Performance by Beverly K. Kahn, Diane M. Strong, and Richard Y. Wang |
| Both the product and the service characteristics of information must be considered in delivering quality information to information consumers. Using the quality definitions from the Total Data Quality Management literature, specifically quality as conformance to specifications and quality as meeting or exceeding consumer expectations, and the product service characteristics of information, we develop the Information Quality Product and Service Performance (PSP/IQ) Model. We map the information quality dimensions from our previous research into this model. We demonstrate the efficacy of the model via a case study in three large healthcare organizations. The PSP/IQ Model is useful for explaining how organizations can improve the quality of information delivered to information consumers. |
| Accepted for publication in Communications of the ACM |
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TDQM-98-01: March 1998 AIMQ: A Methodology for Information Quality Assessment by Richard Y. Wang, Diane M. Strong, Beverly K. Kahn and Yang W. Lee |
| Information Quality (IQ) is a critical issue to organizations. Despite a decade of active IQ research and practice, the field lacks comprehensive methodologies for IQ assessment and improvement. In this research, we develop such a methodology, which we call AIMQ (AIM Quality), that forms a rigorous and pragmatic basis for IQ assessments and benchmarks. The efficacy of the AIMQ methodology is illustrated through an application to five leading organizations. The AIMQ methodology encompasses the IQ Assessment (IQA) instrument, the IQ Product and Service Performance (PSP/IQ) Model, and the IQ Gap Analysis techniques. We develop and validate the IQA instrument and use it to collect data on the status of organizational information quality. These data are used to assess and benchmark IQ for the four quadrants of the PSP/IQ model. The Gap Analysis techniques are applied to analyze the gap between an organization and a best-practice organization. This reveals four IQ gaps, Soundness Gap, Dependability Gap, Usefulness Gap, and Usability Gap. The Gap Analyses techniques are also applied to analyze gaps between information systems professionals and information consumers. The results of the analysis techniques are useful for determining the focus for IQ improvement activities. |
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TDQM-98-02: February 1998 Modeling Information Manufacturing Systems to Determine Information Product Quality by Donald Ballou, Richard Wang, Harold Pazer and Giri Kumar Tayi |
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Many of the concepts and procedures of product quality control canbe applied to the problem of producing better quality information outputs.From this perspective, information outputs can be viewed as information products, and many information systems can be modeled as information manufacturing systems. The use of information products is becoming increasingly prevalentboth within and across organizational boundaries. This paper presents a set of ideas, concepts, models, and proceduresappropriate to information manufacturing systems that can be used to determine the quality of information products delivered, or transferred, to information customers. The systems analyzed involve predefined processes applied toa predefined set of data units. These systems produce information productson a regular or as-requested basis. To measure the timeliness, quality,and cost of information products, the model systematically tracks relevant parameters. This is facilitated through an Information Manufacturing Analysis Matrix which relates data units and various system components. Measures of these attributes can then be used to analyze potential improvements to the information manufacturing system under consideration. An illustrative example is given to demonstrate the various features of the information manufacturing system and show how it can be used to analyze and improve the system. Following this is an actual application, which, although not as involved as the illustrative example, does demonstrate the applicability of the model and its associated concepts and procedures. |
| Published in Management Science, Volume 44, Number 4 (April 1998) pages 462-484. |
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TDQM-98-04: June 1998 Modeling Quality Requirements in Conceptual Database Design by Veda C. Storey and Richard Wang |
| The quality aspects of data in a database are important from the user's perspective. However, they have largely been overlooked in the database design literature. This research presents an approach to incorporating quality requirements into the design process and representing them in a conceptual model. Distinctions are made among application requirements, application quality requirements, and data quality requirements. The implications of incorporating these requirements into database design are analyzed, and the research results applied to a detailed example. |
| Published in the Proceedings of the 1998 Conference on Information Quality, pp. 64-87 |
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TDQM-98-05: September 1998 Product and Service Performance Model for Information Quality: An Update by Beverly K. Kahn and Diane M. Strong |
| The Product and Service Performance Model for Information Quality (PSP/IQ Model) integrates definitions from the Total Quality Management literature, specifically quality as conformance to specifications and quality as meeting or exceeding customer expectations, with the product and service characteristics of information, producing a two by two framework. This paper highlights the assignment of the Wang-Strong IQ dimensions to the four quadrants of the PSP/IQ Model. Quadrant IQ values can be determined using the IQA Instrument to measure the associated IQ dimensions, thus providing a means for assessing an organization’s IQ. The PSP/IQ Model provides the foundation for IQ assessment, benchmarking, and improvement over time. |
| Published in the Proceedings of the 1998 Conference on Information Quality, pp. 102-115 |
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TDQM-98-06: October 1998 Institutionalizing Information Quality Practice: The S. C. Johnson Wax Case by James D. Funk, Yang W. Lee and Richard Y. Wang |
| Institutionalizing a total data quality management (TDQM) program that will sustain long-term benefits is a challenging endeavor and an area that has not been systematically approached. In this paper, we present how S. C. Johnson Wax starts to institutionalize their TDQM. We illustrate the effort in the context of a data warehouse project and show how information quality tools are deployed to facilitate these processes. |
| Published in the Proceedings of the 1998 Conference on Information Quality, pp. 1-17 |