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
Liliendahl on Data Quality 
Congratulations Henrik!
Thank you for continuing to provide the international community with a great resource for understanding data quality (especially data matching) and its related disciplines (especially master data management).
I am looking forward to reading your next 100 blog posts.
🙂
As for fit for purpose versus real world alignment, I agree that accurately reflecting the real world object is far less cumbersome than trying to align all known purposes. I like to use data quality to describe real world alignment and information quality to describe fitness for purpose(s), making data an unambiguous shared definition and information the customized definition fit for each specific purpose.
As for the role of technology in data quality improvement, I definitely agree that whether you are approaching data quality (or any of its related disciplines) from a business perspective or a technical perspective, you need to understand the other perspective. Only the combined viewpoint of both business and technology will provide a comprehensive vision of the solution required.
Thanks and Best Regards,
Jim
Henrik,
It’s been a pleasure, and hugely informative reading your first 100 posts. I plan to take the time to reread them.
Rgds Ken
Jim and Ken, thanks for the comment and kind words – and all the comments from you on many of the previous posts. I really value your input and support and find great inspiration in your blogs.
Congratulations Henrik!
The most recent debate among us learned folks is a great example of the value of your blog.
Thanks Phil for your kind words and many great inputs on the debates on my blog.