August 2, 2010

Why I think OpenIDEO is important

This is a break from my usual blog post format but today is a special day: we’re launching OpenIDEO – a platform designed to bring people together to solve problems for social good. Here’s a quick intro video (those of you that know me may recognise the voice):

I’ve been thinking about and developing this opportunity for a few years and thought I’d jot down a quick personal perspective on why I think it’s important.

Collaboration works. Cities have always been hotbeds of innovation.  Why?  Because high population densities bring together minds and enable collaboration.  Solitary inventors deliver, but the world is getting too complex for individuals to make breakthroughs at the societal level as frequently.

vs  

Take helicopter design: although Leonardo Da Vinci had a good go at designing a whole ‘helicopter’ in the 1480s no individual on the planet would be able to design and build a groundbreaking one today from scratch.  I don’t imagine there’s even an individual that could design and build all of the electronic systems. Today, most breakthroughs require collaboration.

Collaboration works between similar individuals: 1+1 = 2.5. But we have learnt through our work that collaboration works even better between diverse individuals (whether the differences stem from culture, experience, knowledge, approach or all of the above): 1+1 = 3. Diversity of perspectives drives better results.

And it doesn’t need to be ‘physical’ anymore. In the past collaboration has generally required physical meetings, it’s still arguably the most effective approach.  But technology is providing new means to meet and collaborate virtually, for example social networks (including Twitter, Facebook and LinkedIn) provide us all with new tools for collaboration.  Harnessing technology will be essential for impactful collaborations in the future, particularly since we know that diversity is important.

When companies choose to prevent employees from using social networks (usually to keep them focused) I wonder if they’ve thought through downsides – not least that they may be preventing their employees from developing these new collaboration muscles.

And we care. Although voter numbers are dwindling, consumer and citizen engagement around brands and local politics is increasing – partly because these are the areas where we believe we can have impact. This is likely to increase – particularly after a period where we have all been forced to reassess our values (the economic meltdown) and because technology is offering us all greater power to affect that change.

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So, with these big trends in mind, we built a technology platform to enable diverse people to collaborate in solving problems for social good.  We hope that it makes a difference.

July 1, 2010

57) Google Image Labeler

危 wēi  danger

Google owes much of its success to its phenomenal search algorithm, invented by Larry Page and Sergey Brin while they were attending Stanford University as Ph.D. candidates.

Broadly, Google search works in three distinct parts:

  • Googlebot, a web crawler finds and fetches web pages.
  • An indexer sorts every word on every page and stores the resulting index of words in a huge database.
  • The query processor compares your search query to the index and uses the algorithm to recommend documents that it considers most relevant.

Google harnesses a distributed network of thousands of computers to parallel process this information.   This approach has proven incredibly effective, with perhaps one major exception: image search.  Image search is less reliable because the indexer mines pages for words and therefore only labels images based on their context (and most images on the web are untagged).  Many companies have tried to build software to interpret images but it’s tough to do  – that’s why identifying unclear letters remains one of the last ways of evidencing that we are in fact human, not machine.

Proof you’re [sort of] smarter than machine:

Although there is clear value in being able to search for images accurately, even Google couldn’t afford to have people complete the labour-intensive task of tagging images one by one.

机 jī opportunity

Google asked a different question  - how can we have consumers do this for free?  Answer: make it a game.  Google licensed ESP gaming technology, originally conceived by Luis von Ahn of Carnegie Mellon University and launched Google Image Labeler in 2006 as a beta.

In the game users are paired with another and they compete in tagging images.  The game is great fun: some users reportedly play over 40 hours a week.  The game has enabled the company to ensure that its keywords are matched to correct images, building an accurate database for Google Image Search.

Gaming has great potential for good, other recent examples include Matchin (helping build a database of the web’s most attractive pictures) and Solarstormwatch (helping astronomers spot explosions on the Sun to give astronauts an early warning if dangerous solar radiation is headed their way)

How About…

  • Developing a game to harness consumer power economically?
  • Applying gaming ideas & principles to your existing offer?

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Here’s a screenshot from the game:

May 6, 2010

50) McKinsey

危 wēi  danger

In its 2009 report, the UK CIPD’s Employment Survey claims that the average cost of filling a job vacancy is between £4333 and £7750.  This is the average across all sectors and doesn’t even include legal or training costs.  For Management Consultancies this number must be far larger – the firms visit universities in the recruitment drive and often give signing on bonuses.  With this in mind surely McKinsey’s average employee stay of about 3 years is a fundamental floor to the business model?

机 jī opportunity

Far from it, McKinsey cleverly keeps its leavers close to the Firm, recognizing their potential value.  It delivers this through its alumni services – both formal events and informal networking. This dynamic network is widely understood to be a lasting benefit of a career with McKinsey, thereby improving its appeal as a recruiter. The backbone of the McKinsey network is the firm’s alumni directory which lists the details of 3,500 ex-McKinseyites and is more up-to-date than the alumni rosters at Princeton or Harvard.  The strategy of setting up its alumni to be potential future clients must be working – McKinsey has produced more CEOs than any other company and is referred to by Fortune magazine as “the best CEO launch pad”. And you can’t blame the Firm for not publicizing the fact that Enron’s Jeff Skilling was among those high-flying CEO alumni.

How About…

  • Keeping departing employees close: supporting them where possible and viewing them as potential ambassadors for your company?

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April 14, 2010

47) Facebook

危 wēi  danger

Many technology startups, particularly social networks, are heavily dependent on network effects (the phenomenon whereby a service becomes more valuable as more people use it, thereby encouraging ever-increasing numbers of adopters).  This makes it very challenging to attract the first users to the system because they see relatively few benefits until others join. When Facebook was launched in February 2004 by Harvard undergrad students as an alternative to the traditional student directory it faced exactly this challenge, how would it get to critical mass?

机 jī opportunity

Facebook overcame this challenge because it tapped into an existing offline community – Harvard University students.  And that group had a real need – it was difficult for them to meet fellow students outside their social groups.  The most pressing need to meet students came from those searching for dates – the group that became the system’s earliest adopters.  Facebook initially only allowed students with a Harvard email address to use the site, and then opened it up to other Ivy League schools.  This enabled the startup to control its growth and in the process made it more aspirational to the wider population.  Since launch it has grown to more than 400 million active users, with over 50% logging in on any given day.  Profits are believed to exceed $1bn per year.

I noticed that for the week ending March 13th 2010, Hitwise said that for the first time more Americans typed Facebook into their browser than Google.  It made me wonder if Google’s algorithms for search are being disrupted by different types of recommendation engines – if Facebook can harness its community to offer ‘discovery’ it might become the search engine of choice.

How About…

  • Piggy-backing off an existing offline (or online) community?
  • Initially targeting those that are likely to be influencers for other groups in due course?

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April 9, 2010

46) Netflix (again!)

危 wēi  danger

As per my previous post on the subject Netflix has done an incredible job of disrupting the video rental incumbents, particularly Blockbuster.  Part of its success was moving away from the traditional physical retail channel by harnessing internet distribution – providing opportunities to improve the customer value proposition that would never have been feasible in physical stores (including personal recommendation engines and instancy).  However, the former is notoriously difficult to develop and improve – the required algorithms are incredibly complex and require significant development.  They are, however, very valuable to the company (allowing it to predict how much someone is going to enjoy a movie based on movies that they have enjoyed in the past).  Should Netflix risk hiring an expensive team of experts to improve their algorithms, if so how would they possibly select the talent capable of making the most significant progress?

机 jī opportunity

Netflix found a creative way to avoid hiring anyone at all.  Instead, on October 2, 2006 it announced a $1m prize for the first team to better its own prediction software by 10% and a series of ‘progress prizes’ of $50k for the teams that made the most progress each year.  The strategy was immediately effective – within a year over 20,000 teams from over 150 countries had registered for the competition and 2,000 teams had submitted over 13,000 prediction sets.  Over the same period a handful of front-runners with very different backgrounds traded first place.  The competition reached its climax at the end of 2009 when a series of front-running teams decided to join forces to try to achieve the full 10% improvement. On September 18, 2009, Netflix announced team “BellKor’s Pragmatic Chaos” a merger of teams “Bellkor in BigChaos” and “Pragmatic Theory” had won the grand prize of $1m by only 20 minutes (the formula in the title is a very small excerpt).  The winning entry factored in an amazing variety of variables, including the effect that human memory plays in rating and the effect of moods on ratings for different days of the week.

How About…

  • In situations where the challenges or costs associated with solving your toughest problems are highest opening it up to the wisdom of the crowd?
  • It wasn’t until leaders joined forces with also-rans that real progress was made in this contest – how might you drive progress by merging competing teams?
  • The best solutions came from unorganized people who organized organically – how might you allow teams to self-mobilize in your organization?
  • The most extreme approaches that had seemed least effective when initial progress was being made actually made all the difference in the end – how might you harness extreme perspectives to build competitive advantage?

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