“Innovation,” Wong explained, “develops when an officer is given a minimal number of parameters (e.g., task, condition, and standards) and the requisite time to plan and execute the training. Giving the commanders time to create their own training develops confidence in operating within the boundaries of a higher commander’s intent without constant supervision.”
According to Wong, too many rules and requirements “remove all discretion, resulting in reactive instead of proactive thought, compliance instead of creativity, and adherence instead of audacity.” Wong believed that it came down to a difference between cooks, those who are quite adept at carrying out a recipe, and chefs, those who can look at the ingredients available to them and create a meal. A successful military strategy is executed by officers who are trained to be chefs, not cooks.
Data Governance’s Kitchen
Data governance requires the coordination of a complex combination of a myriad of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data quality remediation, data stewardship, business process optimization, technology enablement, and, perhaps most notably, policy enforcement.
Because of this complexity, many organizations think the only way to run data governance’s kitchen is to institute a bureaucracy that dictates policies and demands compliance. In other words, data governance policies are recipes and employees are cooks.
Although implementing data governance policies does occasionally require a cook-adept-at-carrying-out-a-recipe mindset, the long-term success of a data governance program is going to also require chefs since the dynamic challenges faced, and overcome daily, by business analysts, data stewards, technical architects, and others, exemplify today’s constantly changing business world, which can not be successfully governed by forcing employees to systematically apply rules or follow rigid procedures.
Data governance requires chefs who are empowered with an understanding of the principles of the policies, and who are trusted to figure out how to best implement the policies in a particular business context by combining rules with the organizational ingredients available to them, and creating a flexible procedure that operates within the boundaries of the policy’s principles.
But, of course, just like a military can not be staffed entirely by officers, and a kitchen can not be staffed entirely by chefs, in order to implement a data governance program successfully, an organization needs both cooks and chefs.
Similar to how data governance is neither all-top-down nor all-bottom-up, it’s also neither all-cook nor all-chef.
Only the unique corporate culture of your organization can determine how to best staff your data governance kitchen.
This post originally appeared at OCDQ Blog.
I like your distinction between Cook (i.e., Data) Governance and Chef (i.e., Information/Analytics) Governance, with stricter governance for the cooks, but more flexible governance for the chefs.
I also like your governance example based on driving, traffic signals, rules of the road, etc.
I agree with your point that, for lack, on my part, of better phrases, "the real world" provides many lessons in good governance, which we have not found a way to easily apply to "the virtual worlds" of data and information.
In fact, data and information are more similar to the theoretical "multiverse" since although the real world is constrained by an immutable set of physical laws and constants, the virtual worlds of data and information are not constrained by the same parameters.
One simplistic example is the fact that I can only physically exist in one place at one time, whereas the data and information that describes me simultaneously exists in countless databases all around the world. Another simplistic example is that what would happen to me if I drove my car at 100 miles/hour into a stone wall can only produce one outcome (my certain death), whereas what could happen with the data and information describing me whizzing around the Internet at 100 MB/second into the analytical applications of countless organizations can produce a wide variety of outcomes, the possibilities of which are not easily predictable -- especially not with the certainty that the laws of physics can predict the outcome of my car slamming into that stone wall.
Although the real world provides us with many good lessons and useful metaphors, I have always believed (and many people disagree with my perspective on this) that data and information can NOT be managed or governed in the same way that physical objects can -- which is why, for example, I have never liked the attempted brute force application of Manufacturing Quality principles to Data and Information Quality.
Best Regards,
Jim