Data Governance and the Buttered Cat Paradox
Jim Harris in Data Quality, Debates tagged Best of 2011, DQ-Poll, Data Governance Tuesday, March 8, 2011 at 3:00AM 
One of the most common questions about data governance is:
What is the best way to approach it—top-down or bottom-up?
The top-down approach is where executive sponsorship and the role of the data governance board is emphasized.
The bottom-up approach is where data stewardship and the role of peer-level data governance change agents is emphasized.
This debate reminds me of the buttered cat paradox (shown to the left as illustrated by Greg Williams), which is a thought experiment combining the two common adages: “cats always land on their feet” and “buttered toast always lands buttered side down.”
In other words, if you strapped buttered toast (butter side up) on the back of a cat and then dropped it from a high height (Please Note: this is only a thought experiment, so no cats or toast are harmed), presumably the very laws of physics would be suspended, leaving our fearless feline of the buttered-toast-paratrooper brigade hovering forever in midair, spinning in perpetual motion, as both the buttered side of the toast and the cat’s feet attempt to land on the ground.
It appears that the question of either a top-down or a bottom-up approach with data governance poses a similar paradox.
Data governance will require executive sponsorship and a data governance board for the top-down-driven activities of funding, policy making and enforcement, decision rights, and arbitration of conflicting business priorities as well as organizational politics.
However, data governance will also require data stewards and other grass roots advocates for the bottom-up-driven activities of policy implementation, data remediation, and process optimization, all led by the example of peer-level change agents adopting the organization’s new best practices for data quality management, business process management, and technology management.
Therefore, recognizing the eventual need for aspects of both a top-down and a bottom-up approach with data governance can leave an organization at a loss to understand where to begin, hovering forever in mid-decision, spinning in perpetual thought, unable to land a first footfall on their data governance journey—and afraid of falling flat on the buttered side of their toast.
Although data governance is not a thought experiment, planning and designing your data governance program does require thought, and perhaps some experimentation, in order to discover what will work best for your organization’s corporate culture.
What do you think is the best way to approach data governance? Let’s conduct an unscientific data governance poll:
Additionally, please feel free to post a comment below and explain your vote or simply share your opinions and experiences.
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Reader Comments (5)
Jim,
Great post as always and I like the analogy. Too many companies get paralyzed thinking about how to do this and implement it. (Along with the overwhelmed feeling that it is too much time/effort/money to fix it.)
But I think your poll needs another option to vote on. I would suggest the option of "Whatever works for the company/culture/organization". Not all solutions will work for every organization. In some where it is highly structured, rigid and controlled, there wouldn't be the freedom at the grass roots level to start something like this and it might be frowned upon by upper level management. In other organizations that foster grass roots things then it could work.
However, no matter which way you can get it started and working, you need to have buy-in and commitment at all levels to keep it going and make it effective.
Thanks for your great comment, Rob.
I was tempted to include a "Whatever works for the company/culture/organization" option, but I was afraid most people would vote for it by default. However, I definitely agree with you that it is the true answer to the poll question because an organization’s corporate culture is the most significant variable in the data governance equation.
Best Regards,
Jim
Great post, Jim.
Just one or two comments.
One area that is commonly misunderstood with regard to Data Quality is Business Processes.
The fact is that Business Processes do NOT create or transform data. All data in an enterprises is created and transformed by Business Functions.
A Business Process is merely the definition of the required order of execution of Business Functions in response to a business trigger in order to arrive at a desired business outcome. Each Business Function may be a step in one or more Business Processes. So, sort out the Business Function and it will be right in every Business Process in which it occurs.
I regret to say that, "Whatever works for the company/culture/organization", is really a euphemism for "we have no idea of what approach to take".
Both Top Down on its own and Bottom Up on its own should definitely be avoided.
Top Down will evolve into ever greater navel searching, so called "governance", politics and policing and will never get anywhere.
Bottom Up on its own will become all about "really clever" ways of turning existing data into something else. Will this "something else" be any nearer to what the enterprise requires? The fact is that no one will be able to tell.
It is really very simple:
1) Start cleaning up all of you existing data by removing all duplicates, etc.
2) Build an enterprise Logical Data Model*
3) Make sure that all of your databases contain only information in a form and structure defined by the logical data model.
Regards,
John
* One other thing, in order to build and effective Logical Data Model you must first of all model the Business Functions. But that is where all data quality starts!
Good post - I definitely agree that it needs to be a combination of both. "Data Governance" at a senior level making key decisions to provide air cover and "Data Management" at the grass-roots level actually making things happen.
@John - Interesting comments, my only concern with your 3 points are that often starting the task of building an Enterprise Logical Model and mapping/ ensuring all databases conform to it is a job that is easily started but never finished!! I wonder is there a more pragmatic way of starting the journey without having to do this all at once?
Interesting, I had interpreted the hovering cat as the achievement of perfect balance when adopting the hybrid model, something akin to heightened awareness or, even, levitation. That was until I read your take on it as being the organisation caught forever in mid-decision and therefore no action . . .
At any rate, in my view a little bit of buttered toast is probably required before a well-prepared approach can be made to the cat to gain executive sponsorship. Thereafter the two work not so much in tandem but certainly towards the same goal.
Thank you Jim, your posts in this realm have greatly assisted me to take heart during the waning moments of keeping data governance actively on the agenda in my organisation.