The purpose makes all the difference. Being clear about your purpose is critical. Without clarity on purpose, you will make wrong data governance choices.
For example, last week I wrote about different deduplication models. However, a different approach or model is required if your project is ending and the continuation of the work is being done by another entity. Or if it is more of a case management approach where one agency is referring a person on to another agency for assistance. Or if aggregate analysis of the data across the consortium is your goal, a different model is required again.
And sometimes what we say is the purpose, isn’t. Take deduplication. Sometimes when we propose model 3 using unique identifiers, we learn the real purpose is wanting a master list of all the details about the person.
Purpose matters. Asking “what problem are we trying solve” can help. Additionally, asking “when will we know we are successful” teases out other insights into the purpose. And sometimes, new insights surface after working through the other layers of data governance.
Efficiency and improving effectiveness are meaningless. They are management speak and unhelpful. The purpose has to be more specific, more detailed.
Data sharing is not one thing. It has different strains. Identifying the strain takes time; sometimes even trial and error. Nuance is important so look for it. Voice assumptions. The data sharing purpose path is almost never straight, it is a winding road covered in brambles and pot holes. Walk it anyway.