There is an awful lot in the business press about failure, and the important role it can play in the learning, both of individuals and organisations.  I wrote last year about the great amount of scientific research that gets wasted each year, purely by virtue of not making it into an academic journal.

All those dead ends that rarely get shared are dead ends that other researchers could subsequently avoid.  As Edison famously said

I have not failed.  I’ve just found 10,000 ways that won’t work.

The movement to change that is known as Open Notebook Science, where researchers are encouraged to document everything they do in the public domain, whether it was good or bad, successful or not.

Of course, not looking after data is not just about not documenting failures.  It was estimated recently that a whopping 80% of scientific data has been lost within the last two decades, with the main culprits being old email addresses and obsolete storage devices.

“Publicly funded science generates an extraordinary amount of data each year,” says Tim Vines, a visiting scholar at the University of British Columbia. “Much of these data are unique to a time and place, and is thus irreplaceable, and many other datasets are expensive to regenerate. The current system of leaving data with authors means that almost all of it is lost over time, unavailable for validation of the original results or to use for entirely new purposes.”

So 80% of all output from research conducted over the last 20 years is no longer available for other researchers to build on.  Kinda mad isn’t it?

Suffice to say, this doesn’t just affect the academic community.  In crowdsourcing the ‘failure’ rate is equally high.  I wrote last year about the incredibly low implementation rate at various high profile crowdsourcing projects, with Dell Idea Storm running at around 3%, and Starbucks’ My Ideas even lower at 0.5%.

Now it’s quite possible that many of those ideas just aren’t very good, but equally, there might be some that either the sponsor themselves, or someone else looking on from the outside might decide would be worth developing.

The GE/Quirky partnership was a nice example of this in action, with GE opening up a number of their patents to the Quirky community for them to try and turn them into commercial products.  Suffice to say, this opens up the obvious intellectual property issue with such openness, because GE clearly had control over the IP they were loaning out.

In an open innovation competition, the IP is often a bit murkier.  Think of a couple of scenarios:

1. All submissions are automatically the IP of the sponsor.  In such an instance, the sponsor may decide to open up all submissions for further development under a kind of revenue share model, but people wouldn’t be able to cherrypick ideas they think could work and start developing them willy nilly.

2. Winning submissions are the IP of the sponsor.  In this second instance, the ‘losing’ submissions remain the IP of the entity that submitted them.  They may again look badly on someone taking their idea, which may have received a lot of time and energy, and turning it into something.

This kind of intellectual piggy packing often works in open source environments, but many crowdsourcing projects have a financial bounty at the end of it, which may muddy the waters somewhat with regards to the willingness of participants to see others building on their original idea.