- Kingland Platform
Not long ago during a data management maturity workshop at a client site, I was struck by the irony that the team said they felt that the DMM Model seemed like ‘gobbledygook’ and they didn’t understand what the model was expecting them to do. This team was made up of IT professionals, scientific data repository professionals, and bonafide scientists all of whom were extremely intelligent, well respected by their peers (and me) and running a highly regarded and well-used repository of scientific data. What they meant was that sound data management activities described at very high levels of abstraction may be difficult to understand at the ground level where people must put these things into practice. This makes perfect sense when you consider that each industry has its own needs, nuances, language and cultural norms. These kinds of differences even exist between organizations within the same industry, but are magnified across different industries.
Therein lay one of the challenges with models such as the DCAM and DMM. Having been involved with these models since their initial concepts, I understand that they must be written at a high enough abstraction level to be able to apply to all industries and types of organizations. (Full disclosure, I was a co-author of the DMM Model, so I can lay some criticism for lack of clarity in its text at my feet.)
My point here however, is not to criticize the level of abstraction of clarity (or lack thereof) of the text in these models or data management activities at-large. Rather I bring this out to call for engagement in conversation. I can certainly explain the expectations described in the models in a way for my clients to understand what they need to do (I do that all the time). But I think if we collectively engage in broader conversations then much of the cloud of abstraction will start to fall away and more people will be able to gain better understanding on their own. Remember, a rising tide floats all boats. Fortunately I don’t need oceanographic data to understand that.
Join me in the conversation won’t you? Don’t be shy. Let’s discuss the questions and concepts and raise everyone’s understanding of data management requirements. Let’s clear up the gobbledygook.