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I recommend this article as an entry point into a research program on information quality: Stvilia, B., Gasser, L., Twidale, M. B. and Smith, L. C. (2007), A framework for information quality assessment. J. Am. Soc. Inf. Sci., 58: 1720–1733. doi:10.1002/asi.20652 Available at: http://stvilia.cci.fsu.edu/wp-content/uploads/2011/03/IQAssessmentFramework.pdf 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. Bob On 5/6/2015 4:32 PM, Diane Hillmann wrote: > You might try this blog post, by Thomas Bruce, who was my co-author on an > earlier article (referred to in the post): > https://blog.law.cornell.edu/voxpop/2013/01/24/metadata-quality-in-a-linked-data-context/ > > Diane > > On Wed, May 6, 2015 at 5:24 PM, Kyle Banerjee <[log in to unmask]> > wrote: > >>> On May 6, 2015, at 7:08 AM, James Morley <[log in to unmask]> >> wrote: >>> >>> I think a key thing is to determine to what extent any definition of >> 'completeness' is actually a representation of 'quality'. As Peter says, >> making sure not just that metadata is present but then checking it conforms >> with rules is a big step towards this. >> >> This. >> >> Basing quality measures too much on the presence of certain data points or >> the volume of data is fraught with peril. In experiments in the distant >> past, my experience was that looking for structure and syntax patterns that >> indicate good/bad quality as well as considering record sources was useful. >> Also keep in mind that any scoring system is to some extent arbitrary, so >> you don't want to read more into what it generates than appropriate. >> >> Kyle >> >