In a recent article, all data that’s valuable is internal and proprietary”.McKinsey discusses how significant it could be if organisations would agree to share some of the vast quantities of data they have. It already happens in some areas. As they point out, “not
Hot on the heels of this comes a perfect example of what McKinsey means in action. Johnson & Johnson has just announced an agreement with Yale School of Medicine’s Open Data Access Project (known as YODA) to open up access to all of its company-sponsored studies. Xconomy reported on this as an “unprecedented” transparency move due to the extent of the data on offer. J&J will “honor scientific data requests going as far back in time as its data are available”, plus detailed clinical study reports and patient-level data.
Yale professor of medicine, Harlan Krunholz, described this as “crowdsourcing of science”. No doubt with this type of move, will come a note of caution. J&J seems to have made a fairly comprehensive deal, but others may not always be so fulsome.
Inevitably, questions come to mind. Will this lead to peer pressure with J&J’s competitors feeling obliged to follow suit? What about restrictions on how much data is released – might not partial data be worse than none at all? The devil will be in the detail of these “open information” agreements. Also, J&J’s is a Business-to-University (B-to-U?) agreement, what about B-to-B (as McKinsey suggests in some of its examples)?
It has often been said that the possession of information is power. It now seems that sharing of information is beginning to be seen as a powerful force for mutual gain…in some sectors at least. I doubt there’s much choice here – we need to sit up and take notice.
“Open information” is one thread of a big data movement that will escalate as cloud storage becomes increasingly the norm and analytical methods improve. There are plenty of other areas to compete on beyond having access to the raw data itself – analytical tools, real-time data versus retrospective, speed of retrieval, and, maybe the most important: interpretation. He who can make the most accurate analysis (for his needs), from the vast quantity of data probably wins, well, until the others catch up anyway.
Yale’s project is called YODA, which reminds me of a quote that seems somehow appropriate: “Do or do not. There is no try”.