by on November 3, 2009
An recent article from the Economist made me think about how companies could learn from the distributed innovation of open source to find the great ideas within. The article is about InnoCentive, which helps connect problems with solutions:
[Innocentive] is based on a simple idea: if a firm cannot solve a problem on its own, why not use the reach of the internet to see if someone else can come up with the answer? Companies, which InnoCentive calls “seekers”, post their challenges on the firm’s website. “Solvers”, who number almost 180,000, compete to win cash “prizes” offered by the seekers. Around 900 challenges have been posted so far by some 150 firms including big multinationals such as Procter & Gamble and Dow Chemicals. More than 400 have been solved. InnoCentive reckons the approach can work for innovations in all sorts of fields, from chemistry to business processes and even economic development. It has formed a partnership with the Rockefeller Foundation, a charity, to help solve problems posted by non-profits working in poor countries, with some initial success.
InnoCentive is now looking inwards with a new service called Innocentive @Work which “replicates the solver network inside a firm”:
Challenges are first offered to “seeker” companies’ own employees. Only if they cannot help is the outside network brought into play. “Companies often don’t know how much they already know,” says Dwayne Spradlin, InnoCentive’s chief. An early challenge at one firm was to find a source of some data, which, it turned out, had already been acquired by another division.
This is particularly interesting given that InnoCentive began in 2000 as e.Lilly, a place for pharmaceutical giant Eli Lilly to put out the problems it was failing to solve internally.
Right under their noses
The fact that InnoCentive is successfully being used to solve problems shows the power of setting up a structured framework for innovation. Why is this important? Let’s take a look at the open source software community and ask this: how can thousands of individual coders collaborate on something as huge as Linux or Android?
A large part is the framework in which contributors operate. Much of the coding that needs to be done is well defined and there is a solid framework for executing the solutions. Want to fix a bug? Go into the source code repository, download the project, work on your bit, test it and then upload it for community approval. All your contributions are tracked, and you can see who’s working on what and resolve conflicts. Because of this, a lone coder can quickly change just a single line of code (that’s what most do), while at the same time huge companies can put thousands of their people to task (for example, IBM has contributed 6.3% of Linux, and Sun is mostly responsible for Java).
What would happen if the strengths of this model were applied to business problems, which are a whole lot fuzzier? By what framework could a salesperson easily fix a bug in a large marketing campaign? Or an engineer contribute to an ethnographic study for a new vacuum cleaner? To be fair, programming has the particular advantage of being a granular, text based medium, but tools like Innocentive@Work could make problems visible within an organisation and give solutions a place to go (and the solvers to be rewarded).
InnoCentive is not the only service to link problems and solvers. In the same space are Hypios, InnovationXchange, NineSigma and Tekscout in US, PRESANS in France, Innoget in Spain, and Fellowforce. Moreover, this approach is not only for research and development in the traditional sense: just look at the success of Threadless in generating t-shirt designs, Fold.it for science (see NYT article) or the several dozen sites like TopCoder and ODesk which allow you to outsource self contained business problems, from coding to marketing.
As web based applications, these companies are essentially a testing ground for highly automated processes which allow people to contribute innovative solutions (or even just good work). Organisations should therefore keep a close on eye on the fittest of these services to see exactly what they do to make it easy to specify problems, maintain relationships with solvers and to communicate clearly.
Too often, innovation is forced to squeeze through bureaucracy. Implemented correctly, such automated frameworks could make it look a lot more meritocratic.
What do you think? Please comment below.
by on October 18, 2009
October’s DISCOVER magazine has a nice article about the Large Hadron Collider (LHC), the colossal particle accelerator which amongst many other things may reveal the Higgs Boson and the secret of gravity. The LHC is 27km long and requires a frankly ridiculous amount of power to fulfill its single goal of making particles crash at high speeds. But how much is “a frankly ridiculous amount”? What exactly is a “high speed crash” in particle physics?
‘Just the facts’ is only enough if your audience can put them in context without your help
Wikipedia’s entry, likely updated by and for professionals with more than a passing knowledge of the field puts it as follows:
The collider tunnel contains two adjacent parallel beam pipes that intersect at four points, each containing a proton beam, which travel in opposite directions around the ring. Some 1,232 dipole magnets keep the beams on their circular path, while an additional 392 quadrupole magnets are used to keep the beams focused, in order to maximize the chances of interaction between the particles in the four intersection points, where the two beams will cross. In total, over 1,600 superconducting magnets are installed, with most weighing over 27 tonnes. Approximately 96 tonnes of liquid helium is needed to keep the magnets at their operating temperature of 1.9 K, making the LHC the largest cryogenic facility in the world at liquid helium temperature. Superconducting quadrupole electromagnets are used to direct the beams to four intersection points, where interactions between accelerated protons will take place.
Once or twice a day, as the protons are accelerated from 450 GeV to 7 TeV, the field of the superconducting dipole magnets will be increased from 0.54 to 8.3 teslas (T). The protons will each have an energy of 7 TeV, giving a total collision energy of 14 TeV (2.2 ?J). At this energy the protons have a Lorentz factor of about 7,500 and move at about 99.9999991% of the speed of light. It will take less than 90 microseconds (?s) for a proton to travel once around the main ring – a speed of about 11,000 revolutions per second. Rather than continuous beams, the protons will be bunched together, into 2,808 bunches, so that interactions between the two beams will take place at discrete intervals never shorter than 25 nanoseconds (ns) apart. However it will be operated with fewer bunches when it is first commissioned, giving it a bunch crossing interval of 75 ns.
[...] While operating, the total energy stored in the magnets is 10 GJ (equivalent to 2.4 tons of TNT) and the total energy carried by the two beams reaches 724 MJ (173 kilograms of TNT).
Technical, yes. Informative? Maybe, but only if you know enough about the field to make sense of the units of measurement presented. This is probably enough for a particle physicist. It likely doesn’t generate understanding in a layman.
If you need to, give useful contexts to the facts
DISCOVER’s Lisa Randall goes one step further in an attempt to translate for the reader:
“I learned more about the backstory [of the Large Hadron Collider] during my visit. Keep in mind that the ultimate goal for collisions is a center of mass energy of 14 TeV, or trillion electron volts. I realise these might be unfamiliar units, so to give some perspective, it is seven times the energy of the Tevatron particle accelerator at Fermilab in Illinois, which is presently the highest-energy machine, and 15,000 times the energy contained in the mass of a single proton at rest” Lisa Randall, ‘The Heart of the Matter’
The effort is laudable, but it falls short of really communicating what 14 trillion electron volts because it uses examples that are only meaningful to the kind of reader who most likely already understands that unit of measurement. The problem is that electron volts, Fermilab and the energy of a stationary proton are all part of the same language.
Know what language your audience is speaking
Description is not communication. You can’t deposit knowledge in a person’s head, just as you can’t stick new leaves directly onto a plant. Successful communication is about feeding your audience the right blend of facts, stories, examples and experiences so that their understanding of a topic can grow within what they already know.
In both cases, the comparisons given (”largest cryogenic facility”, “99.9999991% of the speed of light”, “seven times Fermilab”, “mass of a singe proton”) will not mean much without similar facts in their mind acting as ‘hooks’. But what if you put it in terms that made comparison easier?
This kind of translation isn’t always necessary, but always make sure to speak the same language as your audience. It’s not what you say that matters, it’s what they understand.
