Soccer versus baseball: which is the best analogy for data governance?

By Carol Newcomb, Data Management Consultant, SAS

A few years ago I wrote a blog post that drew parallels between the skills you should develop to improve your golf game and the organizational discipline required to build a data governance program. Today's post is another sports analogy, but it focuses on baseball and soccer.

In David Brooks' recent New York Times article “Baseball or Soccer?” he compares a sport with highly individualized competition (baseball) with one where the changing dynamics of the ball in flight is shaped by team members striving toward a collective goal (soccer). While he was talking about effective lifetime strategies, his analogy also fits data governance very nicely.

When we coach organizations to launch effective data governance programs, our messaging around do’s and don’ts starts to feel prescriptive. It would be nice to come up with a playbook (like "The Rules of Golf") that describes every conceivable situation as well as the official rule to determine its impact.

But the types of issues that need to be addressed through a data governance program often can’t be anticipated or predicted. Is it a source data problem? An integration problem? A transformation problem? A combination of everything – or something else?

In this regard, the business SMEs (the players) need full engagement from their data stewards and the data governance council (the umpires or referees) to determine the best course of action. So, in this respect, data governance is both baseball and soccer. The full team needs to be engaged and involved.

But, let’s think about the dynamics of a baseball game versus a soccer match. In baseball, the game’s action is almost entirely between the pitcher and hitter. The fielding team is poised to get the ball (much as the data governance organization is poised to handle various types of issues requiring governance), but in this case, if the action is strictly between just the two individuals, isn’t this behavior closer to what a good data governance program is designed to prevent?

We call it the "urban legend" or "cowboy culture" when data issues get handled this way – solely by the person who seems most able to field it, which is obviously not a repeatable method. This leaves the whole organization vulnerable to data issues. It also leaves it unequipped to create policies and processes to more effectively anticipate recurrent data issues. Heroic saves (diving catches in the outfield or magnificent fly balls snatched from the fence line) are fun to watch, but they are hardly helpful in protecting data as a business asset.

Let’s also think about the fielding team as simply waiting to field wherever the ball is struck. They don't have a role in determining where it goes. You’ve got seven players waiting for the outcome of what happens between the pitcher and the hitter. This is an enormous investment in players who do very little during most of the game. Three minutes of action that takes three hours to watch! Of course, when the ball flies their way, the players must hustle. This is the type of dynamic (we call it over-investment in resources) that we’re trying to combat through data governance.

Now, if we consider the game of soccer from a team dynamics perspective, we have 11 players with specific roles and responsibilities playing in relation to the other players and the opposing team. The team plays “collectively,” meaning the players work together based on each specific position to create a spatial, strategic and tactical environment for controlling the ball. This is not a team waiting for a solitary ball to come from one point in any direction. Rather, the team works together to influence the trajectory and conditions for moving the ball towards the goal. This is how we need to model effective data governance.

In designing a data governance framework, we lay out the key personnel roles and how they will interact from a decision-making standpoint. We design workflows detailing how individual roles will work with each other, what each role is accountable for and where handoffs or exchanges need to occur. Some of these processes are more standard than others, but given the type of data issue, the data governance organization will design a strategy that involves collective action.

Clarifying roles and processes gives all the players enough information to know when they need to be engaged and where they need to hand off to move the ball towards its target. There is still the need for referees, but these individuals also know the types of issues they need to make a ruling on.

Yet another way that data governance is (or should be) more like soccer than baseball is that the players (data stewards and business users) don't operate alone. They never bat alone against a solitary pitcher. Instead, they have the collective knowledge of the data governance organization for consultation and support. If you get into trouble with the ball on the soccer field, you pass the ball to another player who's better positioned. You have the other players’ skills and team dynamics to support you. Similarly, you support their efforts as the game is played.

And the rules of the game, the policies and standards for acceptable behaviors and actions, are understood by the entire field. Data governance policies and the organizing framework lay out the ground rules for all players – business users, data stewards, data managers – so that data issues can be anticipated and handled swiftly.

As you start to think more about data governance and how to design it within your enterprise, think carefully about the players you need to involve and how you want them to interact with and support each other. There will be times when you need a referee and a ruling, and there will be surprises and exceptions. But with a data governance organization in place, your enterprise will be shaping the trajectory for where you want the ball to be headed.

I say, game on!