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 <kyle.baner...@gmail.com>
wrote:
On May 6, 2015, at 7:08 AM, James Morley <james.mor...@europeana.eu>
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
