We use cookies and other similar technologies (Cookies) to enhance your experience and to provide you with relevant content and ads. By using our website, you are agreeing to the use of Cookies. You can change your settings at any time. Cookie Policy.

Blog: Ronald Damhof Subscribe to this blog's RSS feed!

Ronald Damhof

I have been a BI/DW practitioner for more than 15 years. In the last few years, I have become increasingly annoyed - even frustrated - by the lack of (scientific) rigor in the field of data warehousing and business intelligence. It is not uncommon for the knowledge worker to be disillusioned by the promise of business intelligence and data warehousing because vendors and consulting organizations create their "own" frameworks, definitions, super-duper tools etc.

What the field needs is more connectedness (grounding and objectivity) to the scientific community. The scientific community needs to realize the importance of increasing their level of relevance to the practice of technology.

For the next few years, I have decided to attempt to build a solid bridge between science and technology practitioners. As a dissertation student at the University of Groningen in the Netherlands, I hope to discover ways to accomplish this. With this blog I hope to share some of the things I learn in my search and begin discussions on this topic within the international community.

Your feedback is important to me. Please let me know what you think. My email address is Ronald.damhof@prudenza.nl.

About the author >

Ronald Damhof is an information management practitioner with more than 15 years of international experience in the field.

His areas of focus include:

  1. Data management, including data quality, data governance and data warehousing;
  2. Enterprise architectural principles;
  3. Exploiting data to its maximum potential for decision support.
Ronald is an Information Quality Certified Professional (International Association for Information and Data Quality one of the first 20 to pass this prestigious exam), Certified Data Vault Grandmaster (only person in the world to have this level of certification), and a Certified Scrum Master. He is a strong advocate of agile and lean principles and practices (e.g., Scrum). You can reach him at +31 6 269 671 84, through his website at http://www.prudenza.nl/ or via email at ronald.damhof@prudenza.nl.

"Cleaning the lake or reducing the pollution from the factory" - is an analogy used by Thomas Redman. It perfectly paints a picture of data quality issues 'we' all face in our data management projects. 

In projects we often have to struggle against forces that 'just wanna create the freekin report'. Whether the data is wrong is of no concern. In these instances the goal apparently is the information system ('our DWH is running', 'The report is build' or 'SAP is live') and not the data. Put in other words; data is often treated as a by- product and the information system is the main product.

Lets take a closer look - by using (among others) Richard Wang's analogy with a manufacturing process1:

Schermafbeelding 2011-07-20 om 11.23.21

I have never seen any manager, CEO or foreman happy with a successful implemented assembly line but a lousy product. Have you? In software engineering I sometimes have the feeling we lost touch....

The Information System - be it a Data Warehouse, a report or an ERP - is not the purpose, it is a means to an end. And the end should at least be sufficient data quality (where data quality is defined in the perpective of the customer, fit for his/her task)

The cool thing about the (somewhat) oversimplified analogy of Richard Y Wang is its usefulness for another reason. It stresses the system perspective you gotta have on dealing with data quality issues. You cannot go about 'cleaning your lake while the factory is still polluting'. Producing quality Information Products is executed by means of a system. Now, do not translate this 'system' into 'information system'. This system consists of people, processes and technology. Dealing with data quality issues requires a system perspective to really add value in terms of better quality products and a 'greener' environment.

So - do not blame your ERP department for creating bad data

So - do not blame your report builder for creating useless reports

So - do not blame the person entering the data

Maybe, something to consider; who do you think is accountable for organizing the 'system'? Yes - management should embrace quality in its DNA...

I know I am corny - management should have read Deming, Juran, Crosby in their MBA's. Knowledge that is like half a century old.


1 - Richard Wang - A Product perspective on Total Data Quality Management - feb.1998, Communications of the ACM

Posted September 21, 2011 3:26 AM
Permalink | 1 Comment |

1 Comment

I often worry about the "sufficient data quality" tag that is all too common amongst data teams and management who want to do the bare essentials.

Competitive advantage rarely gets created through "sufficient" services or "sufficient" products.

The other problem with the sufficient tag is that businesses are adapting and evolving all the time and what is sufficient today will be woefully ill-prepared for tomorrow; just look at the nightmares companies face when trying to migrate to new platforms.

Management have to consider the false economics of constantly pouring dirty data into the organisation only for their own teams or others to clean it up.

Small companies with miniscule budgets have proven that it does pay to do it right so there is no longer any shortage of testimonials for the right way forward.

Great discussion topic, thanks for sharing.

Leave a comment