The data governance discipline, the Master Data Management (MDM) discipline and the data quality discipline are closely related and happens to be my fields of work as told in the post Data Governance, Data Quality and MDM.
Every IT enabled discipline has an element of understanding people, orchestrating business processes and using technology. The mix may vary between disciplines. This is also true for the three above-mentioned disciplines.
But how important is people, process and technology within these three disciplines? Are the disciplines very different in that perspective? I think so.
When assigning a value from 1 (less important) to 5 (very important) for Data Governance (DG), Master Data Management (MDM) and Data Quality (DQ) I came to this result:
A few words about the reasoning for the highs and lows:
Data governance is in my experience a lot about understanding people and less about using technology as told in the post Data Governance Tools: The New Snake Oil?
I often see arguments about that data quality is all about people too. But:
- I think you are really talking about data governance when putting the people argument forward in the quest for achieving adequate data quality.
- I see little room for having the personal opinion of different people dictating what adequate data quality is. This should really be as objective as possible.
Now I am ready for your relentless criticism.
Liliendahl on Data Quality 
Hello, globally i agree with your matrix. I just find that people are more important for DQ, because you need someone to watch over Data Quality, to analyse evolution, new problems and to define the right way to improve data quality by processes or technologies …
I think the issue is that Data Governance is often light touch or completely ineffective. It takes a people driven DQ approach to often make the case for robust Data Governance, and thereby embed Data Governance and the value of DQ in an organisation.
Henrik, quite a bold and interesting post! I know you agree with me going by the last line of your post.
On DQ i couldn’t agree with you more. DQ is very objective and technology and processes play a strong role. Ideally the technology should encompass the processes, such that business requirements for standardization, normalization, attribute structuring, enrichment and de-duplication of data are all configured within the tool.
However, on DG with technology coming at 1, i have to disagree. I regularly come across tier 1 global organizations who have experienced people (5/5) and solid policies (4/5) for DG but trying to do govern data using free tech like spreadsheets or emails (1/5), who admit that their DG process is broken. That is because a collaboration intensive, multi-stakeholder process like DG needs strong tech to enable winning bits like workflow, workflow visibility and audit trails.
One of Verdantis’ customers who own sugar brands like Tate & Lyle in EU, and are the largest vertically integrated sugar producers in North America are sharing DG best practices on these very lines next week with the Americas’ SAP Users’ Group.
I am not as bold as you, but if i had to, i would rate people, processes and tech as 4,5,4 for DG.
Thanks Julien, Keith and Abhinav for your relentless criticism.
Also on twitter @ScottCSlavens said: Good start. Somewhat myopic. No organization’s data and process complexities are the same. The matrix must conform!