It seems like we’re so busy running that we no longer have time to think. We want to be faster and more responsive, but we aren’t even sure what we are trying to achieve. It’s like the person at your office that is always too busy, is working extra-long hours (and makes sure that everybody knows!), and yet somehow nobody is quite sure what the person is accomplishing with all that effort.
Motion is not the same as momentum. This is at the heart at one of our companies’ greatest risks: achieving little in relation to our investments of time and energy.
With the popularization of methodologies such as Agile and DevOps, we are emphasizing adaptability and responsiveness—but in doing so we risk losing the cohesive vision that comes from effective design. By driving systems development from the bottom-up, it is inevitable that our data solutions will become fragmented.
Enterprise Architecture is a domain that represents structured thinking: a deliberate approach to organizing the well-intended chaos that results when bright people all independently arrive at decisions with the best of intentions, but drawn from varied and unique experiences.
The inevitable result is similar (but not the same) techniques that require extra energy to coordinate than suboptimal but more consistent processes. Enterprise Architecture, though easy to vilify because so many people historically holding that title have been notably ineffective, represents the missing link in today’s highly-motivated-but-loosely-organized technology teams.
Structured approaches drawn from Enterprise Architecture are rarely the best for each individual consideration, but do represent answers that maximize overall benefits at the macro-level. By adopting these principles within an organization, we can make the case that we are making the most of the resources we are given.
So how does Data Governance relate to Enterprise Architecture? We can draw a parallel between the coordination of the systems and tools that a company uses (i.e., Enterprise Architecture) and the coordination of the data assets that those systems and tools create and manage (i.e., Data Governance). In fact, if we dig into it a little more, we can see how the policies and standards of Data Governance mirror the platform selections of Enterprise Architecture. We can see that our Data Stewards mirror our Developer Teams, and even our Data Governance Councils look an awful like our Change Advisory Boards and Project Management Offices!
And we wonder why it’s hard to get Data Governance functioning smoothly in an organization! The difficulties of structured thinking are losing ground to the empty promises of technology innovation with loose coordination. And why is that? Because with the latter the payoffs are more immediate, and more directly measurable.
Unfortunately, however, the immediacy of rapid payoffs may hide a larger opportunity cost outside our view. It’s as if our data and technology efforts were like painting a wall: the biggest brushes and fastest hands win every time—until a roller comes along!
But just like painting a wall, when we need to get the edges right and cover the fine details, having the nimbleness and flexibility of a brush is important. We need our Agile and DevOps to handle such precision. But the structured approaches of Enterprise Architecture and Data Governance are invaluable when we need to cover a larger area.
This is the lesson: use everything at our disposal, and be deliberate about it. Recognize that there is no single answer for our complex and varied technology and data challenges. Sometimes the job can easily be handled by brushes, and other times we need rollers.
Another item to think about is how to empower the folks that can make the best macro-scale decisions. Are those the people in senior positions with strong alignment to business strategy, or are they the ones most familiar with the technologies and the data? Are they currently incentivized to make the best decisions for the organization as a whole, or what will help them directly the most? Are the economic implications of the decisions fully-baked in (direct costs, indirect costs, return-on-investment)?
The path of the Data Leader can often feel like the road less traveled, because much of the time it is. Our organizations are struggling to stay competitive, and their future relies on data and technology excellence. Businesses that have been around for decades are failing because they are not responding quickly enough at scale to the world changing around them. Through a combination of structured thinking and nimble responsiveness, we can give our venerable organizations a better outcome.
And until next time, go make an impact!
