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| 1996 Working Paper Abstracts |
| TDQM-96-01: January 1996 Unavailable |
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TDQM-96-02: April 1996 Data Quality in Context by Diane Strong, Yang Lee, and Richard Wang |
| High quality data are accurate, accessible, and useful. Conventional approaches to data quality focus only on the first aspect. In a field study of forty-two data quality projects in three organizations, we discovered that data accuracy is a necessary but not sufficient condition for high data quality. Data accessibility and data relevance to the contexts of data consumers’ tasks are also necessary attributes of high data quality. Furthermore, conventional approaches (e.g., edit checks and integrity constraints) for increasing data accuracy do not increase these other attributes of data quality. We document how organizations are redefining data quality, and we use this redefinition to substantially revise conventional approaches to data quality. |
| Publshied in Communications of the ACM, May 1997, volume 40 number 5, pages 103-110 |
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TDQM-96-03: June 1996 Unavailable |
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TDQM-96-04: July 1996 Ten Potholes in the Road to Information Quality by Diane Strong, Yang Lee, and Richard Wang |
| Information quality problems can create serious crises in organizations. Without diagnosing their root causes, all efforts for avoiding information quality problems are like patching potholes in the road. Information quality problems include problems attributable to (1) information process problems, (2) technical problems with storing and accessing information, and (3) the tasks and information needs of information users. Ten key root causes of information quality problems are revealed. We present these ten key problem causes, warning signs of the problems, and how these problems are typically patched. With this knowledge, information quality problems can be uncovered easily and addressed proactively with long-term solutions before they cause financial and legal consequences for organizations. |
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TDQM-96-05: August 1996 Total Data Quality Management: An Information Product Perspective by Richard Wang |
| Data are used in the delivery of many products and services, and so data quality is an important component of customers' perceptions of the quality of these products and services. The paper describes efforts initiated by AT&T to control and improve the quality of data it uses to operate its worldwide intelligent network, to conduct its day-to-day operations, and to manage its businesses smoothly. These efforts stem from the observation that it is extremely difficult to fix faulty data once they are in a database. Therefore attention must be directed at processes that introduce, modify, and transform data. Only when these processes have been put into a state of statistical control can sustainable improvements in data quality be expected. The report describes AT&T's four-part data quality improvement program. |
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TDQM-96-06: September 1996 Can you defend your information in court? by Richard Wang, Yang Lee, and Diane Strong |
| Information defensibility is becoming a critical issue to many organizations. We define information defensibility and interpret it in light of the dimensions of information quality. This understanding of information defensibility provides the background for exploring solutions. We explore how information quality and information disclosure policies can support and insure information defensibility. These information defensibility solutions are illustrated via two mini-cases. Addressing information defencibility issues can improve customer relations as well as avoid potentially high legal and financial risks. |
| Also in the 1996 Conference on Information Quality, Cambridge, MA, pp. 53-64. |
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TDQM-96-07: November 1996 Anchoring Data Quality Dimensions in Ontological Foundations by Yair Wand and Richard Wang |
| Poor data quality can have a severe impact on the overall effectiveness of an organization. In order to design information systems that deliver high quality data, the notion of data quality has to be well-understood. However, there is still no consensus on what constitutes a good set of data quality dimensions and on appropriate definitions for each dimension. We propose an ontologically-based approach to defining data quality dimensions based on the role of an information systems as a representation of a real-world system. The dimensions are derived from possible failures of the representation. The analysis leads to four intrinsic dimensions of data quality: complete, unambiguous, meaningful, and correct. We discuss the relationships of these dimensions to those cited in the literature and briefly present some implications of the analysis to information systems design. |
| Also in Communications of the ACM, November 1996. |