One cannot manage information quality (IQ) without first being able to measure it meaningfully and establishing a causal connection between the source of IQ change, the IQ problem types, the types of activities affected, and their implications. In this article we propose a general IQ assessment framework. In contrast to context-specific IQ assessment models, which usually focus on a few variables determined by local needs, our framework consists of comprehensive typologies of IQ problems, related activities, and a taxonomy of IQ dimensions organized in a systematic way based on sound theories and practices. The framework can be used as a knowledge resource and as a guide for developing IQ measurement models for many different settings. The framework was validated and refined by developing specific IQ measurement models for two large-scale collections of two large classes of information objects: Simple Dublin Core records and online encyclopedia articles.

Most computing serves as a resource or tool to support other work: performing complex analyses for engineering projects, preparing documents, or sending electronic mail using office automation equipment, etc. To improve the character, quality, and ease of computing work, we must understand how automated systems actually are integrated into the work they support. How do people actually adapt to computing as a resource? How do they deal with the unreliability in hardware, software, or operations; data inaccuracy; system changes; poor documentation; inappropriate designs; etc.; which are present in almost every computing milieu, even where computing is widely used and considered highly successful? This paper presents some results of a detailed empirical study of routine computer use in several organizations. We present a theoretical account of computing work and use it to explain a number of observed phenomena, such as: -How people knowingly use "false" data to obtain desired analytical results by tricking their systems.-How organizations come to rely upon complex, critical computer systems despite significant, recurrent, known errors and inaccurate data.-How people work around inadequate computing systems by using manual or duplicate systems, rather than changing their systems via maintenance or enhancement.In addition, the framework for analyzing computing and routine work presented here proves useful for representing and reasoning about activity in multiactor systems in general, and in understanding how better to integrate organizations of people and computers in which work is coordinated.

The classic problem within the information quality (IQ) research and practice community has been the problem of defining IQ. It has been found repeatedly that IQ is context sensitive and cannot be described, measured, and assured with a single model. There is a need for empirical case studies of IQ work in different systems to develop a systematic knowledge that can then inform and guide the construction of context-specific IQ models. This article analyzes the organization of IQ assurance work in a large-scale, open, collaborative encyclopediaWikipedia. What is special about Wikipedia as a resource is that the quality discussions and processes are strongly connected to the data itself and are accessible to the general public. This openness makes it particularly easy for researchers to study a particular kind of collaborative work that is highly distributed and that has a particularly substantial focus, not just on error detection but also on error correction. We believe that the study of those evolving debates and processes and of the IQ assurance model as a whole has useful implications for the improvement of quality in other more conventional databases. IntroductionLarge-scale, continuously evolving, open collaborative content creation systems such as Wikipedia have become increasingly popular. At the same time, in an attempt to lower the bottom line, many traditional publishers and informationintensive organizations have opened their content creation processes to the general public by adding wikis and blogs to their regular channels of information creation and distribution. We are witnessing the establishment of a dynamic grid of large-scale, open information systems fueled by active participation from the general public in content creation and quality assurance activities. Although providing valuable information services to the users, the new information grid also poses new and significant challenges in many areas of information organization, including information quality (IQ).These new systems have complex, dynamic workflows that need to react successfully to changes in both their communities and the environment, including identifying the most effective and efficient IQ assurance interventions for different circumstances. Furthermore, the concept of IQ itself is context sensitive (Wang & Strong, 1996). The same information can be judged as being of different quality depending on the context of a particular use and the individual or community value structures for quality. Hence, no one fixed model of IQ assurance can be applied for all these systems. There is a need for empirical studies of existing IQ assurance models, with a goal to develop a knowledge base of conceptual models of IQ, taxonomies of quality problems and activities, metrics, trade-offs, strategies, policies, and references sources. The knowledge base can then be reused for constructing context-specific IQ assurance models faster, cheaper, and with less effort. The English Wikipedia, with its large-scale, complex, and collaborative information...

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