All in Data Program Management
McCauley provides a nuanced view of open data in the CERN context that I believe helps make sense of a very complex situation.
Creating a data program governance strategy is not unlike creating other types of enterprise business strategies.
Whatever the environment, new data management and analysis technology may be important, but success and sustainability will also be driven by how we manage it and by how successful we are in putting data users and their priorities into the driver’s seat.
As an extension of my own research and consulting on big data project planning and management, I wanted to improve my understanding of how data governance and program management practices impact how medical and health data are used.
I’m fairly “old-school” when it comes to planning and building information systems. That is, first you decide what your requirements are and what services you need to provide, then you decide which technologies you need to adapt, develop, or purchase to help you meet those requirements.
Two risks are apparent for GE’s approach which may also be relevant to other industries where cloud-based analytical platforms are being developed and marketed. (I’m thinking here, for example, of medical devices, prescription medicine, and treatment costs.)
Government regulators need to be sensitive to the costs of complying with and overseeing the regulations they impose on data management, as do the organizations that are regulated. Costs related to data oversight, quality control, standardization, security, and privacy all need to be considered in comparison with the quantitative and qualitative benefits that will be generated.