Perspectives on Innovation

Hello Readers!  I am writing to tell you about some exciting news from the Client Services group at InnoCentive.

Just last week we launched an exciting new Challenge entitled Predictive Data Analysis. This $100,000 Challenge asks Solvers from all backgrounds to build a predictive model based on a complex dataset.  I know, $100,000 on its own makes this Challenge quite special. But, there is another really cool feature that’s so noteworthy – The Prodigy. This website feature allows enables Solvers to get instant feedback on how well they’re doing in comparison to other Solvers.  Sound exciting? Let me tell you a bit more.

First, a bit of background. The Predictive Data Analysis Challenge asks Solvers to build a model using a large dataset in order to estimate the relative performance of various breeds of an organism.  We have provided the molecular and performance data on 100 breeds of the organism and ask Solvers to estimate the relative performance of an independent set of 150 breeds based on their molecular data.  The Prodigy allows Solvers to upload the relative ordering of the breeds and then it will instantly be compared to the known answer provided to InnoCentive by the Seeker.  After submitting, the Prodigy will provide Solvers with their score, their ranking and if they are within the top 10 best submitted scores thus far, their username will appear on the leader’s table.

This feature is obviously not amenable to all of InnoCentive’s Challenges. However, we think that it will encourage you to continue if you’re on the right track and to go back to the drawing board if you aren’t!  We recommend that you log into the Predictive Data Analysis Challenge and let us know what you think about the Prodigy. We’d love to hear from you in the comments section below.

Happy Solving and good luck.

Gabriel Eichler
InnoCentive Client Services

Peter Lohse InnoCentive Client Services

In today’s installment of “Help a Solver Succeed” (HASS), where we ask InnoCentive experts to provide resources that they think might be helpful to you in solving Challenges, Peter Lohse talks about the InnoCentive Solver Study conducted by the University of Wisconsin-Madison and Indiana University – Bloomington.

At InnoCentive we strive to provide our Solvers with the best tools and processes in support of their solution efforts. We plan, build and deploy these services based on Solver’s needs and the resources we have at hand. While we make every effort to frame and support a successful Challenge process, we rely on our Solvers for performing the creative part of process. This key step of our value chain is in your, the Solvers hands. We rely on your know-how, skills and commitment to submit solutions which our Seekers value and award. Without your creativity and work no problems would be solved and no business was possible.

Even though InnoCentive does not “own”’ the creative step of the innovation process we have a deep interest in understanding the prerequisites and conditions of successful solving routines. This has many reasons, the most important being our realization that we can effectively support this process only if we understand the factors which are indicative for success. No doubt problem solving routines are different from person to person and depend on the education, work experience etc. Nevertheless, there may be a preferred routine underlying successful solution finding efforts. Earlier this year, we launched a collaboration to study these questions with researchers from the University of Wisconsin-Madison and Indiana University – Bloomington.

In short, our research was designed to help determine why some Solvers are more successful than others. To answer this and other questions, we combined a web-based survey of the knowledge, problem solving routines, motivations, resource investments, and characteristics of InnoCentive’s external problem Solvers with an examination of secondary data regarding their solution submission activity. A survey was fielded by the research team in spring 2009 and was sent to approximately 1,600 Solvers who had submitted solutions to Theoretical and Reduction-to-Practice Challenges during 2007 and 2008. Nearly 500 Solvers responded to this survey and many provided qualitative comments and suggestions. The results from this research will be published in detail some time down the road. For the purpose of this Blog though, I want to give a short summary of our key findings, problem solving tips and an outlook on some exciting future research.

Findings: Nature of Successful Solvers
The results show that average successful Solvers invest a much larger amount of time and money than unsuccessful Solvers. Moreover, successful Solvers tend to rely on deliberate, analytical solving routines. They pay attention to the details of the challenge and thoroughly consider the relevant information instead of only relying on their intuition and making ‘off the head’ decisions.

However, our analysis also shows that deliberate, analytical solving routines lead only in combination with high time investment to solving superior performance. If Solvers devote only a little time to problem solving, then having creative routines such as thinking “outside the box” and generating solutions that no one else will conceive, can be more successful than analytical routines.

Successful Solvers are also highly intrinsically motivated and gain satisfaction from challenge solving. However, successful Solvers are also motivated by the award money. Moreover, in comparison to unsuccessful Solvers, successful Solvers submit more frequently, draw on formal reports and scientific articles rather than on their personal practical know-how and rules of thumb to solve Challenges.

Tips for problem solving
Based on the investigated Challenges and on our analysis, successful Solvers…

1. Devote considerable time to understand Challenges and to develop solutions
2. Become familiar with the Challenge task by breaking it down into smaller parts and by paying attention to every detail
3. Develop a solution based on a logical, step-by step approach and weighing logical arguments rather than relying on intuition and flashes of insight
4. Craft solutions in a very structured manner
5. Analyze prior solving attempts and learn from successes and failures

In sum, the results suggest that successful external problem Solvers are largely defined by their willingness to devote time together with their analytical routines.

Conclusions
Overall the results of this study confirmed our perception of successful solving routines. In this respect the tips for problem solving listed above are not different from what we have recommended before, but are congruent and somewhat more granular than what we have said in the past. Having statistically validated data to support our past recommendations certainly put my scientist mind at ease.

While the findings were no surprise to us, some of the statistics which resulted from the study caught my attention and are worth highlighting: For example, of the all the Solvers who had submitted to Theoretical and Reduction-to-Practice Challenges during 2007 and 2008, almost 10% were successful with winning an award! I think this number is absolutely stunning and contrasts the sentiment that the chance of winning on InnoCentive is vanishingly small due to the large size of the Solver Community. This is clearly not the case as the numbers show. In contrary, simply by submitting Solvers already have a reasonably good chance of winning. This also means that an above average proposal has an excellent chance of winning an award. I think this is great news for every Solver who is committed to submitting a quality proposal!

While the study emphasizes the importance of spending time on deliberate solving routines, it also yielded some interesting findings as to what solving routine is successful under what circumstances. The better we understand the relationships between Solver characteristics, solving routine and problem information the better we will be able to match Solver with Challenge for a successful outcome, and the further we can move away from a highly parallel solution approach closer to a more targeted match between problem and Solvers. This would save time and resources for all stakeholders involved and really would be a step forward in terms of Innovation efficiency.

Peter

In today’s installment of “Help a Solver Succeed” (HASS), where we ask InnoCentive experts to provide resources that they think might be helpful to you in solving Challenges, Marilyn Toomey introduces OpenOffice.org.

As a member of Client Services, I spend a lot of time scanning and organizing the many submissions received for our posted Challenges.  When asked to blog about a service or technology that might be of interest to our Solver community, my first thought was “OOOOOOO”….. sort of like when you are excited and don’t know what to say!!   In this case, I know exactly what to say, so I’m going to shorten my “OOOOOOO” to OOo (OpenOffice.org) which is the official name of the open source office suite called Open Office.org.

What is OpenOffice.org?

I was first introduced to OpenOffice.org by my husband and we have used it on our home computer ever since.  It’s a free downloadable suite of applications that includes a document editor, a spreadsheet, presentation software, a graphics tool and a database.  I know some of our Solvers are using OpenOffice.org as some of our submissions come in with a .odt extension. While Open Office defaults to saving documents using the .odt extension, it also can read and write files that are created by many existing software products.  By using the Save As command from the  tool bar, a document can be saved in various formats that can be read by all the popular office suites. It’s easy to learn and can be used for any purpose by just about anyone.  There is one interface where you can start what you want ….a new document, new spreadsheet, new presentation, new drawing or new database from the same dropdown list. In addition to offering great products and applications, there is a whole open source community developing improvements and modifications to the code. Anyone can report a bug or offer enhancements.  It all seems to be a meritocracy so start contributing and you will get recognized.

Did I mention, it’s free?

When I first started using computers I used “free” software to sell the expensive hardware we were offering so I am sort of attached at the hip to “free” software.    The price is right. You can find the download at http://www.openoffice.org.   OpenOffice.org is in its third version, has always been reliable when I have used it, and is currently celebrating its ninth birthday!  It works on multiple platforms and is available in 80 different languages!

Another great free tool

Before writing this blog I was familiar with the OpenOffice.org document writer and spreadsheet.  I didn’t know they had a graphics tool and so I was also going to suggest XnView, which can be downloaded at http://www.xnview.com/en/download.html.  XnView is also a free download for private, non-commercial, or educational use. I really enjoy using this graphics tool for my photographs and would suggest it to all, even if you’re already using the  graphics tool in OpenOffice.org.  It’s a pretty cool tool to have in your pocket.  However, I am now going to explore the Draw tool in OpenOffice.org to see what it has to offer!  I’d love to hear from other people who have tried this application – please let me know if you are an OpenOffice user and if you use the Draw feature!!!

Marilyn

In an effort to improve your InnoCentive experience and get you the answers you need, we have made some upates to our Message Center.

You’ll be able to see the difference when you open your next project room – specifically, a logical division of sent vs. received messages.

To see the new Message Center, click on the Messages Tab or the Messages button on the right side of your project room. You will then be able to view messages for this Challenge from your Inbox or Sent tab. We think you’ll find this interface much easier to use.

As always, we value your feedback – please let us know if there is anything else we can do to enhance your InnoCentive Solving experience!

This is the first in our Blog Series “Help a Solver Succeed” (HASS), where we ask InnoCentive experts to provide resources that they think might be helpful to you in solving Challenges.  Today’s post is from Innovation Development Manager Gabriel Eichler, who is a member of our Client Services team.

Our blogging team has asked me to write a piece for the first issue of the “HASS – Help A Solver Succeed” blog series. This section is dedicated to profiling enabling technologies, services or information that may help our Solvers be more successful at either Solving our problems or be more productive at doing your own work on a daily basis.

Since my educational background and Challenge writing specialty is almost exclusively focused on computational, bioinformatic or statistical Challenges I find it apt to write about a programming language.  I have decided to dedicate my HASS entry to the programming language R.

I came to know R during my PhD research at the US National Cancer Institute.  Previously I had written extensive amounts of code in Matlab – my previous programming language of choice for rapid prototyping or computational experimentation. Though Matlab has a more sophisticated look and feel, and I knew it quite well, I was instructed that learning R would be essential to my graduate studies. Digging in I learned that R was first distributed in the spring of 1997 by Robert Gentlemen and Ross Ihaka and it resembles the closed source, commercial language S in many ways.  However, from the beginning Gentelmen and Ihaka have made R an open source language that thrives off a community of volunteer developers. From nearly the very beginning, R has maintained the Comprehensive R Archive Network (CRAN) resource for everyone to publish their own R extensions or libraries. This brilliant step quickly made R a force to be reckoned with.

I find R to be the best way to quickly model statistical questions, create powerful graphs or even super compute a difficult but parallelizable problem. The interface and kernel are extremely lightweight so your computer is left with maximal resources to compute on what you want.  Beyond that, the CRAN resources make R an even more powerful resource because thousands of people have created hundreds of packages meant to assist you in performing complex tasks.  In fact, in my nearly 3 years of continual use of R, I have rarely (if ever) encountered situations in which I actually had to write complex procedures for any standard statistical or machine learning algorithm.  For example, I was able to develop a multiprocessed, Random Forest based algorithm using mostly code pulled from CRAN.

In summary, I’m a huge fan of the R programming language. If you haven’t already done so I would encourage you to download a free copy and play around with it. I’ll be the first to admit that it’s not as slick as a commercial package such as Matlab or S, but the power of open source has elevated R to be one of the most useful and valuable languages around.  Plus, isn’t it kind of cool to participate in InnoCentive’s Crowdsourcing process by using a resource that is, in and of itself, a product of Crowdsourcing?

Thank you, R.

Gabriel Eichler, PhD.