The goal of data quality improvement is often set as ”fit for purpose”. The first purpose addressed will almost naturally be within the domain where the data in question are captured. Then you address other domains where the same data also may be used, but probably with other purposes leading to additional or varying measures for fitness.
If an organisation identifies several domains where the same data are used the normal approach will be to gather all purposes and then start to align all the needs, find the highest common denominators and so on. This may be a very cumbersome process as you need to consider all the different dimensions of data quality: uniqueness, completeness, timeliness, validity, accuracy, consistency.
Another way will be to assume that if you gather many purposes the total needs will almost certainly tend to be a reflection of the real world objects to which the data refer.
So my thesis is, that there is a break even point when including more and more purposes where it will be less cumbersome to reflect the real world object rather than trying to align all known purposes.
Master Data are often used in many different functions in an organisation and not at least party data – names and addresses – are known to be a focus area for data quality improvement. Here it is very obvious that real world objects exists and they are basically the same to every organisation.
Earlier this year I wrote an entry on dataqualitypro about possibilities with external party reference data: http://www.dataqualitypro.com/data-quality-home/external-reference-data-an-overview.html
In my previous post on this blog I noticed that governments around the world are releasing data stores that surely add traction to the real world approach to data quality improvement.
I will for sure touch this subject in forthcoming posts on this blog.
Liliendahl on Data Quality 