Thank you for your comments and real life examples. I agree, MDM should be a standard requirements of the business process. As I’ve said before, MDM should be part of your organizational DNA.

Daragh

Thanks for your comments (both of them ) Incremental improvement is a fine way to define how MDM or any DQ initiative should be approached. No ‘big bang’ approach but constant improvement in line with business growth and change.

Cheers

Charles

I meant to say that your post is excellent and echoes a lot of the points I was making back in 2008 when I wrote about the long tail of risk and the need to keep incrementally improving to prevent yourself slipping backwards in Information Quality and Data Governance terms.

I wrote an article on this topic for the IAIDQ Newsletter a while back. http://iaidq.org/publications/obrien-2008-04.shtml

From my experience, Master Data Management should be a standard requirement of the business process. Working with large manufacturers, the amount of new data introduced into the engineering / purchasing / maintenance is astounding. I categorize the procedures to process data into three buckets: new, active legacy and non active legacy, each category requires a different data cleansing and set up process as the data affects each department differently. Once the processes are set up the cost avoidance saving in time, process, controlled ordering, parts sharing, etc are extremely beneficial. The processes from the user perspective should be simple and non-intrusive to core business.

Agree 100%, mediocrity should not and will not be tolerated!!

BTW I’ve not heard the name Dee Snider mentioned in ages. Rock on baby!!!!

I agree with the need for continuous improvement but would like to focus for a moment on Julian’s comment. Engagement, commitment, and accountability are really important factors on these types of things as I have seen throughout my years.

I agreed much of with Jim’s post, a scathing rebuttal of my own:

http://www.dataflux.com/dfblog/?p=1403

I jest. The point that I was trying to make was pretty simple: During new system implementations, it’s a unique opportunity to change the culture, to shake things up, and to say “We’re not gonna take it.”

Sorry, I was channeling my inner Dee Snider.

Where was I?

Oh yeah. If you tell people that mediocrity isn’t going to be tolerated anymore, then continuous improvement can still take place. If you don’t, then you’ll have the same DQ issues immediately after the fact.

On my last gig, there were data issues days after going live.

It’s a sad state of affairs.

Enough rambling.

ps

Thank you for your comments, and fine examples. They are a great addition to the topic.

Well worth your 2 cents.

Thanks for a nice post. This is indeed a vast topic. I have blogged about some aspects of this topic on my blog as well.
I believe that to be able to inject data quality consideration in every aspect of data capture that business does, we have to propose a financial model around value of the data and its quality to the business. Once we put a monitory value on the data and its quality…. It will become imperative to track ownership and quality of data being captured. Far too many times we have seen businesses repeat same mistakes and not monitor ongoing/continuous data quality/metrics.
In your discussion, you provide example of fast food chain and the “Data Quality Ladder”. What if this organization actually figured out $$ value of accurate data (Up-sell, cross sell, favorite items, promotions based on the sales trends etc…) entered through POS system and provided a monitory credit towards certain % of accurate data to the individual stores? This will incentivize each owner of the franchise to train/educate his/her POS employees to enter detailed information.
As organizations start treating data as an asset with monitory value, great deal of efforts will be spent on making sure that risks associated with poor quality (3 factors you identify in the blog) of data are mitigated by ensuring/embedding data quality measurements on a ongoing basis.
My 2 cents

Vish Agashe
http://www.linkedin.com/vishagashe
http://www.twitter.com/vishagashe

I was wondering if someone would pick up on the “static advancement”. I was taking a bit of ‘poetic licence’ there.

Cheers

Charles

I think another key factor is engagement – data quality improvement activities (and sustaining quality) should be seen by all staff as part of their role. This requires clear communication on expectations and responsibilities backed up by ongoing communication processes.

One electricity company has been using an ongoing, multi-faceted communication process to explain the need for data quality improvements to staff which has been ongoing for 18 months and is planned to continue to ensure that the message does not get forgotten.

I fully agree with the comments on measurement and continuous improvement. A transport company I have been working with have instigated performance dashboards related to a data improvement project where each unit is ranked against all the others. The outputs are then published on the intranet, so those towards the bottom of the table have an incentive to improve.

I was amused by the phrase in your third para “static advancement” which sounds like a contradiction, but I think I got the meaning.