The Three Most Important Letters in Data Governance
Jim Harris in Books, Data Quality tagged Best of 2011, Change Management, Collaboration, Communication, Data Governance, Philosophy Tuesday, November 8, 2011 at 3:00AM 
In his book I Is an Other: The Secret Life of Metaphor and How It Shapes the Way We See the World, James Geary included several examples of the psychological concept of priming. “Our metaphors prime how we think and act. This kind of associative priming goes on all the time. In one study, researchers showed participants pictures of objects characteristic of a business setting: briefcases, boardroom tables, a fountain pen, men’s and women’s suits. Another group saw pictures of objects—a kite, sheet music, a toothbrush, a telephone—not characteristic of any particular setting.”
“Both groups then had to interpret an ambiguous social situation, which could be described in several different ways. Those primed by pictures of business-related objects consistently interpreted the situation as more competitive than those who looked at pictures of kites and toothbrushes.”
“This group’s competitive frame of mind asserted itself in a word completion task as well. Asked to complete fragments such as wa_, _ight, and co_p__tive, the business primes produced words like war, fight, and competitive more often than the control group, eschewing equally plausible alternatives like was, light, and cooperative.”
Communication, collaboration, and change management are arguably the three most critical aspects for implementing a new data governance program successfully. Since all three aspects are people-centric, we should pay careful attention to how we are priming people to think and act within the context of data governance principles, policies, and procedures. We could simplify this down to whether we are fostering an environment that primes people for cooperation—or primes people for competition.
Since there are only three letters of difference between the words cooperative and competitive, we could say that these are the three most important letters in data governance.
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Reader Comments (3)
Great post! I was reading Competitive, thereby placing me in the business prime group :-)
From the LinkedIn Group for Data Governance & Stewardship, David Abayev commented:
“To expand the discussion’s alphabet point of view, the below anagrams DO NOT have different letters at all, and yet greatly affect the data governance.
Do you agree that in data governance it is important
- to learn to LISTEN, but not to be SILENT when it matters
- to agree to ALTER things , but not much LATER
- to connect the CRATES ( silos ;) ) of data and look for TRACES of data lineage
- work in TEAMS and not to lose STEAM ?
Do you agree that no stakeholder
is an ANGEL,
and each may have their own ANGLE,
we just have to carefully GLEAN
everyone’s input and address the differences?
If you do, then I have only 47 letters for you: You are on your way to building data governance.”
And I responded: Thanks for your excellent comment, and awesome anagrams, David.
From the LinkedIn Group for the IAIDQ Professional Open Community, Andres Perez commented:
“Great blog! I couldn't agree more.
If the setting preceding Data Governance is IT or the DG leaders are from IT, the tendency is almost always to focus on technologies to address the governance issues while downplaying or even ignoring the people issues such as policies, procedures, roles, responsibilities and organizational change management. This is definitely food for thought for the planners of Data Governance deployments.”
And Milan Kucera responded:
“I understand the needs of data/information governance. Probably I look at this term narrowly and my understanding is that governance is very close to management because we need to implement: methodology, techniques, procedures, etc.
It’s one side of the coin, but the second is a company culture transformation - requires enormous effort that’s associated with Deming quality point. Sorry, I understand governance as a buzzword, because I am sure all the quality management and information quality management principles are here and used widely. From my point of view, it is just something about willingness to change. Simply it is all about the use of appropriate tools correctly applied to identified issues and problems.”
And Sherry Michaels responded:
“I also liked this blog and forwarded onto my team for reading. I've even written the two words - Cooperation and Competition - on my white board to continually remember the very slight difference.
I couldn't agree more about this being a mindset change. You can have the best tools that spit out fantastic results and measures, but if folks aren't willing to do something with those results, what advantage do you really have?
It takes more than willingness to do something - it takes action! Folks can buy into and support data quality, data governance, etc., but until someone(s) is doing something about it and making positive change, the overall governance process will never take hold.”
And I responded:
Thanks for your comments, Andres, Milan, and Sherry.
@Andres - Yes, downplaying the importance of people issues is a common cause of data governance failure.
@Milan - Yes, governance has become a buzzword, and I agree that its underlying principles are really not new, but, as you said, the key obstacle is, and has always been, the lack of a willingness to change (in those environment where these principles are not being widely used).
@Sherry - Excellent point about data governance requiring specific actions, not a willingness to do something in general.
And Milan Kucera added:
“I agree with Jim that Sherry presented important view on this topic. I would like to add another view. If we are discussing governance, it’s possible to talk about the Philip Crosby Maturity model. It is a benchmarking tool showing companies how they are successful with the implementation of something (ITIL, project management, etc.). Larry English presents these ideas and how to apply it into the area of information quality management. It is a very complex tool that can help information quality strategy planning.
I recommend using as much as possible simple tools like: information value chain, root cause analysis, quality assessment template, information quality reuse control chart, Pareto diagram, data definition quality assessment tool, PAF model – supporting analysis of costs of poor quality, etc. It’s mostly about the organization and its culture.”
Love the manual inclusion of Linkedin conversation! Whole post makes me think I should start each day out with flashcards of cooperative metaphors!