Haves and Have Nots

When I speak of have here, it should be clear that I'm referring to resources. Less clear but no less important things to have include:

• need (acknowledged)
• commitment
• known cause of pain

First and foremost is availability of resources to be applied to making improvements. Data and information quality diseases have much in common with human diseases in terms of diagnosis and treatment. There is much discussion today concerning the state of health care in the U.S. The discussion focuses not on diagnosis or treatment--those aspects are well understood (if imperfectly practiced)--but on paying for the diagnosis and treatment.

It seems that financial resources or the lack of financial resources is the single most important determinant of physiological well being. If we examine the whys behind this, we soon see that expectations have much to do with it. The person without financial resources learns to expect that some problems will be chronic and learns to live with them, perhaps at a lower level of function. The financially well-off person learns to expect that every problem has a cause and a cure and that time and money will produce the expected well-being.

Neither is absolutely correct and both sets of expectations produce advantages as well as disadvantages.

We can apply the lessons of health expectations to data quality. Larger or wealthier companies expect that they will be able to attack a quality issue with sufficient resources to conquer it. Smaller or less well-off organizations will not feel able to dedicate one or more people to the issue and will elect to "do the best they can" (see previous post). Small business leaders will see that everyone must be involved in the solution for it to work and that alone will cause them to turn away from a frontal attack and "make do." Large business leaders may believe that the right manager or leader with sufficient resources can bring it off.

Again, neither is absolutely correct.

A person or an organization resigned to living with pain is always going to find it difficult or impossible to improve while a person or organization immersed in full scale battle with the problem may well miss opportunities for improvement.

As it turns out, a "data quality" campaign is like a campaign against bacteria--almost meaningless. Because the scope and scale of the campaign preclude considerations of nuance, we find that we make enemies from within the ranks and everything degenerates until nothing is happening. We can make progress against a specific bacterium or a specific quality issue but we soon realize that we can't hold those gains without creating a framework within which we can establish trust, confidence and consistency. That framework has come to be called data governance. In the case of physiological disease, the framework is Medicine.

Whether you're a have or a have not, the resource issue turns out to be far less important than we might have thought. Consider expectations first.

• Can we live with or adapt to the pain?
• Have we already adapted? How?
• What limitations are imposed by the adaptation?
• We can choose to treat symptoms, cure the disease, and prevent the disease. Which is within our reach? What can we do? What should we do?

In most cases, the best choice is to treat symptoms while making lifestyle changes to prevent the disease. Sometimes we have to cure the current disease or we die before we can implement the lifestyle changes. The point is that we always have options. A specific option must consider the past, present and future. A combination of options may produce the best result. Last but not least, have and have not is not really about resources but is about expectations. Commitment is often born of desperation when we realize that we just can't tolerate the future implied by our current expectations. Now we're really ready to do something meaningful.