This Week in Crowdsourcing – 3 Things to Know

August 28, 2014 Ben Kerschberg


At Appirio we’re excited to be on the cutting-edge of the crowdsourcing revolution in enterprise IT. Here are three articles from this week on the subject that we found insightful and interesting:

(1) Can we penetrate eBay’s regular use of Hackathons?

This is a nice Wall Street Journal interview with Guy Schory, who is responsible for identifying and driving strategic initiatives for eBay. Schory explains eBay’s multi-pronged approach to inculcating a culture of innovation. This includes Acquisition, Incubation, Talent Acquisition and Hack-a-thons. Schory states:

Many companies have begun to stage hack-a-thons as a way to seed innovative ideas and identify top tech talent. eBay Inc.’s Battle Hack is a series of 10 hack-a-thons held in 10 major cities around the world. During these 24-hour coding challenges, teams of developers create applications that have a positive impact on their communities and use PayPal in some fashion. The winning team from each challenge competes at the world finals, held at eBay’s headquarters, for a chance to win $100,000.

We’ve had thousands of developers participate and had hundreds of mobile applications and websites built. Battle Hack gives developers a lot of exposure to eBay’s development tools and capabilities, and shows them how easily they can create applications using our technology.

Ben’s Take: This seems like a golden opportunity. I wonder whether we could leverage our client relationship with eBay to scale their Hackathons by orders of magnitude (both in quality and potential extreme value outcomes). eBay travels the world to 10 cities to host its Hackathons, which I understand from a PR perspective, especially given the developers’ charge in these particular contests of building community-focused applications. I still think this could be done virtually, and at a PR advantage for us if we focus on the global and distributed nature of open innovation. A potential partnership here? Or maybe (more likely) a model for working with someone else with similar goals?


(2) IBM says that 80% of organizations are turning to the crowd for innovation.

First, let’s keep in mind the source, which doesn’t mean we should distrust the results. Trust and verify.

The study examined common traits of “pacesetters,” leading organizations that are achieving tangible business results from cloud, analytics, mobile and social technologies. Pacesetters are finding creative new ways to narrow the skills gaps in their organization, including gaps in general IT skills, application development or data analytics. One way pacesetters are filling these gaps is via partnerships with citizen developers.

In addition to turning to citizen developers, these pacesetter organizations are twice as likely to turn to academia for product development . . . .

Ben’s Take: The underlined section interests me most. I read articles daily about how universities are starting crowdsourcing initiatives. I posted an article earlier this week in our internal Crowdsourcing News about a Stanford Professor who hopes to take crowdsourcing to a different level.  The next day there was an article about the National Science Foundation’s pioneering work with Cal Tech and UCLA. This is now standard daily fare. My first reaction is that universities want “save humanity” projects. My second reaction is that coupled with strong MBA and departmental expertise, these universities can help us understand how crowdsourcing can solve field / vertical problems, which would be huge for us.


(3) What’s the point of clustering beer?

Good question. As Randal Scott King demonstrates over at Data Science Central (a great source), it’s far more salient to business processes than you think. King gives the example of his favorite beer. In this case, he is fond of an Atlanta-based pilsner. When he heads to Santiago, he discovers an “impending crisis” — his hotel doesn’t carry that brand. But a top-notch bartender starts to ask all the right questions. What color is the beer? How does it smell? Does it have a lot of hops? Alcohol content? And so forth. By doing so the bartender classifies the beer and creates clusters from which he can recommend something similar. Crisis averted. (If you find it difficult to imagine this happening with beer, think of a sommelier.)

King asks what application this has to the business world.

There are many potential applications of classification and clustering, but a common one is identifying the characteristics of a company’s best customers and then searching a pool of potential customers for ones that meet those characteristics. If your best customers have between 1000-2500 employees, are in the manufacturing and retail verticals, and are located in the New England area of the US, that’s good information to know.

Ben’s Take: Fred Brooks, in his key book The Mythical Man-Month (1995), states that over 90% of the costs of a typical system arise in the maintenance phase, and that any successful piece of software will inevitably be maintained. Wow. Now consider Software-as-a-Service, where client-side maintenance is eliminated. Consider further the idea that within the next 5-10 years, everything will be able to be consumed as a service. McKinsey calls this the “anything-as-service” phenomenon. Imagine the savings. Companies will call it bliss, and in order to compete in what will we be an elastic price market, they will wisely transfer savings to their clients, unlike the opacity of current models, which Clayton Christensen identifies as the primary reason that traditional consulting models haven’t been disrupted. (At least for now.) But SaaS and other service providers will still need to know their markets cold, often by clustering and finding sweet spots. These offerings won’t sell themselves, and new service market paradigms will be as competitive as ever.



Previous Article
Business Intelligence: What To Do When Your Big Data Isn’t Big Enough
Business Intelligence: What To Do When Your Big Data Isn’t Big Enough

Business Intelligence: What To Do When Your Big Data Isn’t Big Enough Gartner predicts that the cumulative ...

Next Article
4 Steps to Master Predictive Analytics and Business Intelligence
4 Steps to Master Predictive Analytics and Business Intelligence

In the past several years, one of the seminal issues regarding Big Data has been storage, especially with r...