Perspectives on Innovation

Bogdan and Stephanie Yamkovenko won The Economist-Nielsen Data Visualization Challenge, which asked the World to review Nielsen consumer data, generate insightful conclusions with broad implications, and present a compelling visual presentation of the most interesting ideas from the data. Over 4,000 Solvers from 101 countries signed up to participate in the Challenge. To view the Yamkovenko’s winning submission, a video of them presenting it at The Economist World in 2013 Festival, and profiles of all the Challenge finalists, please click here.

We saw an advertisement in The Economist for the Data Visualization Challenge sponsored by Nielsen and The Economist. The focus of the Challenge was to analyze a data set provided by Nielsen and to tell a story using data visualization. I am a journalist and have also done graphic design in the past, so I knew I could handle the visual story telling. Bogdan is a researcher and assistant professor with an affinity for statistics, which means that he could easily handle the data analysis.

Bogdan and I have been married for six years and had never previously collaborated professionally on a project. This Data Visualization Challenge was a great opportunity for us to combine our skills and, ultimately, be competitive.

We began our work on the Challenge with a brainstorm about the Nielsen global dataset, which consisted of the Nielsen Global Consumer Confidence Index and other data about consumer spending and purchasing habits. We decided to supplement the dataset with other widely available economic indicators (such as unemployment rates). We noticed that countries that had high confidence in their economies were not necessarily the best performing economies.

When working on my master’s degree in journalism, I developed an appreciation for my profession’s role as the “fourth estate.” As we looked at the confidence index, we noticed that countries such as Saudi Arabia and Egypt had high confidence, but their economies weren’t doing that great. We wondered whether democracy was playing a role in the citizens’ confidence. We decided to include the Reporters Without Borders Press Freedom Index in our analysis, and found that countries with the highest confidence also had the most restricted press. This finding gave us a compelling story to tell and gave the original Nielsen dataset more context and depth. (more…)

Dear Seekers & Solvers –

As we enter 2013, we would like to thank you – our Seekers and Solvers alike – for your continued support of InnoCentive. We achieved great things with your help, and anticipate growing momentum this year. But, before looking forward to 2013, let’s take a few moments to consider the past year and recap our journey together.

This year has been exciting and groundbreaking in many ways: Our global Solver network grew to over 275,000 creative and diverse minds, we crossed the 1,500 threshold in external Challenges posted to our network, and we exceeded $13 million in total awards paid out to our Solvers. Additionally, our Seekers – and their Challenges – grew more diverse in 2012 due to the increased use of Challenges in industries such as aerospace, financial services, healthcare, and the public sector. Further, the adoption of “Big Data” Prodigy Challenges among our Seekers (e.g., Cleveland Clinic and Prize4Life) saw explosive growth. All of these trends mean exciting new opportunities for Seekers and Solvers alike in 2013!

Our mission has always been to help address and resolve problems that matter, so we have been fortunate to have worked with incredible organizations that ask the world to address global concerns and make extraordinary things happen. We – the collective “we” which includes Seekers, Solvers, and InnoCentive – helped Prize4Life to better predict the progression of disease in ALS patients. We announced the conclusion of the “Global Giveback Challenge Series,” a collaboration between InnoCentive, GlobalGiving, and the Rockefeller Foundation to find solutions to dire water-related problems in developing countries. And we helped BeyondPolio find novel ideas for reducing the cost of using inactivated poliovirus vaccine (IPV) to help in the final stages of the global polio eradication effort. There are many more examples, but suffice to say, we still firmly believe in changing the world, one Challenge at a time.

As many of you know, we kicked the year off with our acquisition of OmniCompete, a firm based in London that specializes in Grand Challenges and is best known for its long-running Global Security Challenge. This acquisition also enabled us to expand our presence in the United Kingdom and Europe. Prior to this acquisition, in December 2011, we announced a strategic alliance with Booz Allen Hamilton to bring to market integrated, full-service open innovation and Challenge offerings to both commercial enterprises and public sector agencies. (more…)

Dr. Torsten Hothorn has been on quite a run lately working on Prodigy “Big Data” Challenges. Recently, he won the $30,000 Cleveland Clinic Challenge, Build an Efficient Pipeline to Find the Most Powerful Predictors, and he earned a $10,000 award for his second place finish in The DREAM-Phil Bowen ALS Prediction Prize4Life Challenge. (We recently profiled Lilly Fang, a member of one of the two first place winning teams for the Prize4Life Challenge, and also posted a Seeker Spotlight featuring Prize4Life’s Neta Zach which dives into the background of the Challenge and final results). We’re happy to have Dr. Hothorn here to discuss his experience with these important Challenges.

I am a Professor of Biostatistics in the Department of Statistics at the University of Munich, Germany, and I’m interested in both methodological developments and applications of statistical models in medicine and biology. Research and teaching in Biostatistics ideally brings together practical problems and statistical theory. While I mainly teach students of statistics, I enjoy working with scientists from fields as diverse as oncology, ecology, and forestry. Because a statistical model is only useful when it actually can be applied to gain insights into aspects of data that otherwise would remain hidden, I spend a lot of time developing and implementing new statistical models. Some open source software packages to which I contributed are distributed via CRAN, the R package repository.

Developing statistical software always means pushing forward existing functionality. One of the best and most effective ways to find out where improvements are needed most is to work on the solution of practical problems, apply the software, and look at the results. While I’m not short of collaborators with interesting problems, I decided to give one of the Cleveland Clinic Challenges hosted by InnoCentive a shot when I first learned about InnoCentive in the Fall of 2011. In 2004 and 2006, I authored two scholarly papers about nonparametric survival models that also work in the presence of numerous potentially predictive variables. The Cleveland Clinic Challenge, “Build an Efficient Pipeline to Find the Most Powerful Predictors,” was an exact match for the models that I developed and described in these two papers. Luckily, I had already invested a fair amount of time into a software implementation, using the R add-on package “party,” and thus the solution was (almost) at my fingertips.

I must admit that “The DREAM-Phil Bowen ALS Prediction Prize4Life Challenge” was a little more challenging than I first thought. With the patient data coming from different clinical trials, it took a while to compile the data into a format suitable for statistical analysis. The relatively complex longitudinal structure of the data, the expected weak association between predictor variables and ALS disease progression, and the large amount of missing values in some of the potentially interesting predictor variables suggested that a nonparametric regression approach (e.g., random forests), might be a good candidate for a potential solution. However, the Challenge data gave me a hard time predicting ALS disease progression with good accuracy. Eventually, I went back and started from scratch. First, I slightly reformulated the Challenge objective by using an alternative statistical measure for describing the disease progression of a patient. In a second step, I collected as much information as I could about the disease progression in the first three months in which a patient was under observation. I observed that using these variables as predictors of the new ALS disease progression measure lead to better performing models.

Besides my interest in applying software that I developed and the thrill of competing with people from all over the world in this prediction Challenge – the InnoCentive leaderboard is really something one can get addicted to – I look forward to using the PRO-ACT database (a subset of which the Prize4Life Challenge was based on) in the classroom. Next spring, I’ll teach longitudinal data analysis and I intend to let my students work with the ALS patient data. That way, my students will be constantly reminded what the models and formulae presented on the blackboard are actually good for and what scientific obligation to society actually means to a statistician.

In July 2012, we launched a computational Challenge, The DREAM-Phil Bowen ALS Prediction Prize4Life Challenge, with Prize4Life to better predict the progression of disease in ALS patients. Earlier this month, Prize4Life announced the winners. The judging panel received an overwhelming response, with 1,073 Solvers having signed up, and submissions coming from around the world. Given the quality of the submissions, the judging panel doubled the original prize purse to $50,000. We’re very pleased to have Dr. Neta Zach, Scientific Director for Prize4Life, join us to discuss this Challenge. (Ed Note: Dr. Zach’s colleague, Dr. Melanie Leitner, was interviewed in July at the launch of the Challenge).

Hello Dr. Zach –  Could you take us back to the beginning and help us to understand what motivated you to run this Challenge, and in particular, why you opted for a computational “Big Data” Challenge?

Prize4Life’s mission is to accelerate the development of treatments and a cure for ALS. We have embraced the prize-for-breakthrough model in part because we are interested in attracting new and innovative ideas to ALS research. One area that we identified with great potential is quantitative analysis of ALS data. To that end, we developed the PRO-ACT (Pooled Resource Open-Access ALS Clinical Trials) database in collaboration with the Northeast ALS Consortium (NEALS) and the Neurological Clinical Research Institute at MGH, with funding from the ALS Therapy Alliance. PRO-ACT, which will be launched in early December, contains information from over 8,500 patients from past clinical trials, ten times more than had been previously available.

The DREAM-Phil Bowen ALS Prediction Prize4Life Challenge (a.k.a. ALS Prediction Prize) was a way to utilize, for the first time, the new and promising PRO-ACT database. Specifically, we wanted to use it in order to confront a basic puzzling question in ALS: most patients are like Lou Gehrig, with a rapidly progressing disease course. Some patients, however, turn out to be more like Stephen Hawking, where the disease progression is delayed. What separates the Lou Gehrigs from the Stephen Hawkings?  Understanding the variability of the disease can mean a lot for ALS patients going through diagnosis and can lead to a substantial reduction in the cost of clinical trials for ALS treatments. The unique approach of providing ALS “Big Data” to a global community of researchers speeds up the process while driving down the costs of discovery, which is good news for both the scientific and patient communities we serve.

Having run a couple of high-profile Challenges now, was there anything that particularly surprised you during the course of this Challenge?

We were happily surprised by the level of engagement this prize received. We had over a 1,000 registered Solvers. You can see the engagement in the quality of the winning solutions, but beyond that, the fact that so many Solvers – from 64 countries no less – were actively engaged on the forum and through emails, with over 300 forum messages and 1,500 emails, speaks to the success of this Challenge! Indeed, this engagement bore fruit – even the solutions that didn’t win were so valuable that we encouraged several of the Solvers to submit for publication in scientific journals to share their algorithms with the ALS community at large. This was our first large scale interaction with the quantitative community and we were thrilled by their hard work and devotion to the cause.

I understand that two teams secured first place, each winning $20,000, and a second place winner was awarded $10,000. In fact, the judges decided to double the size of the prize purse based on these submissions. What stood out about these submissions and differentiated them from the others? (more…)

In June 2012, we launched Phase 1 of the My Air, My Health Challenge seeking to spur the development of personal devices that gather and integrate health and air quality data that is usable and meaningful to long-term health outcomes. Sponsored by the U.S. Environmental Protection Agency (EPA) and U.S. Department of Health and Human Services (HHS) [Office of the National Coordinator for Health Information Technology (ONC) and National Institute of Environmental Health Sciences (NIEHS)], four finalists were announced today to proceed to Phase 2, which entails building and testing a prototype sensor device and offers a $100,000 award to the winner. We recently spoke with Dr. David Balshaw, Program Director for Emerging Technologies at NIEHS, about the Challenge.

Hello Dr. Balshaw. Thank you for joining us today and congratulations on the successful conclusion of Phase 1. Taking us back, what were your original goals for this Challenge and how do you envision that the solutions currently being proposed will address the issue of airborne pollutants and their associated health risks?

In the environmental health research community, we always struggle with our ability to make direct connections between exposure to environmental pollutants and physiological responses at the individual level. While there have been a number of emerging technologies for exposure assessment as well as physiological monitoring, we haven’t seen many efforts to integrate these two capabilities. Combining the analyses of these data streams would improve those linkages. Ultimately, we wanted to see what creative solutions the community could come up with!

Over the last few years, crowdsourcing and prize competitions have become an increasingly popular means for government to innovate and promote strong public-private partnerships. What was your impetus for employing a crowdsourced competition model to achieve your goals for this Challenge?

The Challenge mechanism has really demonstrated an ability to bring innovative ideas into a new field. We thought this problem was an excellent fit because there are so many new technologies out there. Groups didn’t need to put a lot of resources into engineering, and there was a high likelihood of getting a useful device out at the end.

Phase 1 of the My Air, My Health Challenge attracted over 500 Solvers and generated dozens of solution submissions. What are your thoughts on the overall quality of the responses that you received? (more…)