This is post number 100 on this blog. Besides that this is a time for saying thank you to those who have read this blog, those who have re-tweeted the posts and not at least those who have commented on the posts on this blog, it is also time for a recapitulation on my opinions (based on my experiences and observations) about data quality.
Let me emphasize three points:
- Fit for purpose versus real world alignment
- Diversity in data quality
- The role of technology in data quality improvement
Fit for purpose versus real world alignment
According to Wikipedia data may be of high quality in two alternative ways:
- Either they are fit for their intended uses
- Or they correctly represent the real-world construct to which they refer
My thesis is that there is a breakeven 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.
This theme is so far covered in 19 posts and pages including:
Diversity in data quality
International and multi-cultural aspects of data quality improvement have been a favorite topic of mine for a long time.
While working with data quality tools and services for many years I have found that many tools and services are very national. So you might discover that a tool or service will make wonders with data from one country, but be quite ordinary or in fact useless with data from another country.
I have made 15 posts on diversity in data quality so far including:
The role of technology in data quality improvement
Being a Data Quality professional may be achieved by coming from the business side or the technology side of practice. But more important in my eyes is the question whether you have made serious attempts and succeeded in understanding the side from where you didn’t start. I have always strived to be a mixed skilled person. As I have tried single handed to build a data quality tool – or to be more specific a data matching tool – I do of course write a lot about data quality technology.
This blog includes 37 posts on data quality technology so far including:
- Service Oriented Data Quality
- Upstream prevention by error tolerant search
- Data Quality Tools Revealed