I recommend this article as an entry point into a research program on
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:
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.
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):
> On Wed, May 6, 2015 at 5:24 PM, Kyle Banerjee <[log in to unmask]>
>>> On May 6, 2015, at 7:08 AM, James Morley <[log in to unmask]>
>>> 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.
>> 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.