Who's data is it? (Part 3)

In the first two parts of this series on negative attitudes on data ownership, I talked about protectionists who want to keep tight control over who has access to what data.  The most important negative consequence of that attitude is that it stifles both understanding and innovation. Fewer people looking at data means that the data is being examined from fewer perspectives.  Innovation comes from looking at existing situations or information from a new perspective and reaching new conclusions.  So, one of the best ways to confront data protectionists is to put the argument in terms of that business driver: innovation and resulting improvements.

It's NOT my data...

The final negative data ownership pattern I'll present in this series is the "it's NOT my data" attitude.  In this scenario, people within the information supply chain simply abdicate responsibility for anything regarding information, beyond their immediate operational duties.  This can manifest itself in several ways.  With respect to data quality concerns, the "not my data" crowd will simply point their fingers upstream:

• "That's what's in the system."
• "That's what the sales team entered."
• "That's what the customer told me."

All of these are only excuses for poor data quality.  By themselves, they do nothing to address data quality problems.  These are reflective of a culture that doesn't look beyond the operational duty of data entry and service or product delivery. 

A more subtle manifestation of the same attitude might look something like "You can have the data, but don't ask me what it means.  You'd have to ask so-and-so about that."  And when you ask "so-and-so" he directs you to "what's-her-face," who tells you to ask her supervisor, who explains that they're just doing it the way the Standard Operating Procedure tells them to.  In this chain of inquiry, everyone is abdicating their own responsibility to understand how their work fits into a larger picture.  It's a form of willful ignorance.

In his motivational work, Christopher Avery uses a model for responsibility that describes several responses that all come before actually taking responsibility for a situation:

• Denial
• Lay Blame
• Justify
• Shame
  
• Obligation

In the "not my data" culture, groups are stuck in one of those first three levels: denial, lay blame, justify, or shame.  They either deny that there is anything to even consider with regard to data ownership and data quality; they lay blame on other constituencies that are upstream from them (even all the way to a customer!); or they justify the situation, telling themselves that there simply isn't another way that things can be done.

The engineer in me sees a certain appeal to "not my data" situations.  In the extreme, they're a great challenge in reverse engineering.  Every steps yields more questions than it answers, and opens new places to explore for broken processes and more denial of responsibility.  It's like a software debugging exercise that leads you into deeper and deeper through twists and turns of function callbacks until suddenly you discover that critical nugget of information that finally allows you to describe the end to end flow of information, and the real impacts of poorly implemented processes or policies.

The real-world corporate director in me sees situations that need to be addressed, supervisors to be educated or replaced, policies and procedures to be changed -- all great places for change and growth -- but also, much less enthusiastically, politics to be navigated.

Counter-Points...
There are always multiple perspectives to any situation.  Teams and organizations don't usually evolve what may appear as negative attitudes out of ignorance or malice alone.  Often, other negative influences or behaviors lead to a culture that once served a valuable purpose, but may no longer yield more benefits than harm as the previously negative influence dissipate.  The final in this series will present situational counter-points to some of the arguments I've presented.