I sat down to write a piece on one of the more serious and pressing topics around data governance – the challenges of adoption and organizational resistance to change. When I did so, I got very serious and began to choose my words very carefully and solicited the feedback from several others to make sure that I got it my message right. After several iterations, I barely recognized the piece. Then it hit me! I got caught in the trap. I was chasing “quality for the sake of quality” and did not take into account the proper context and the desired outcome of the process. How could I have made such a rookie mistake?
What resulted was a very articulate piece (you’ll have to take my word on that) which was way too long, used lots of words from a thesaurus and took way too much time to produce. It was very high quality, I can assure you – but to what end? As is evident by this post, the original piece isn’t producing any value as it hasn’t been shared. I burned many of my own business cycles and those of several others which have turned out to be non-value-add activities. Over-engineering, quality standards that are too restrictive (self-imposed in this case), rules that go far beyond “fit-for-purpose” of data; this was a classic case of not thinking through the process and the desired outcomes before setting out to achieve them. In today’s dynamic business world, perhaps Lexus can commit to the “relentless pursuit of perfection”. The rest of us need to meet immediate deadlines in an ever changing world while competitors are trying to grab up what’s left of a limited market.
At least I can take solace in knowing that I am not alone in this. I’ve had the opportunity to speak to many data governance practitioners that have gotten caught up in this same wave of exuberance. Once given the directive to “govern data as a corporate asset”, a flurry of activity kicks off. A wave of data policies and rules begin to be produced and we’re off and “doing data governance”. Of course in retrospect we end up with a set of rules – some too restrictive, some not restrictive enough. A set of policies which may be poorly documented, or perhaps well-documented but poorly communicated. And, we end up with a set of processes that few will follow making adoption of the data governance program challenging to say the least.
Programs must set realistic objectives and understand the purpose of data policies. What are we trying to achieve? What level of access or degree of quality is actually required to meet business requirements? What is the effort and associated cost to achieve these levels? These among other questions must be answered before we can start “doing data governance”.
The other trap that we get caught in is the trap of cultural roadblocks. In the case of this posting, I was forcing a more formal and structured approach to writing than is my norm. I assure you, it was met with great internal resistance. And, look at the result. The process broke down, the “data” produced was not “fit for purpose” and I resorted to a style that was more suited to my culture (a previously known state or historical process). No motivation, aside from “quality for the sake of quality”, existed for me to follow the other path, so I did what anyone in my shoes would do and reverted to my comfort zone. Sound familiar? This is precisely what happens when data governance programs are imposed on organizations rather than being ingrained into the culture of the organization.
In my next blog post, I’ll address the topic of cultural roadblocks and how to drive past them to ensure an effective and sustainable data governance program. It will be of sufficient quality to meet my writing objectives and using a process with which I am comfortable and is fully ingrained into the fabric of my culture.
