Wendi

Blurred Vision

June 29, 2010 By Wendi Leave a Comment

“This ‘telephone’ has too many shortcomings to be seriously considered as a means of communication. The device is inherently of no value to us.” (Western Union internal memo, 1876)

“Heavier-than-air flying machines are impossible.” (Lord Kelvin, president, Royal Society, 1895)

“I think there is a world market for maybe five computers.” (Thomas Watson, chairman of IBM, 1943)

“There is no reason anyone would want a computer in their home.” (Ken Olson, president, chairman and founder of Digital Equipment Corp., 1977)

“640K ought to be enough for anybody.” (Bill Gates, CEO of Microsoft, 1981)

“The hubris of Tesla is ‘We’re not going to fall into the trap of being like Detroit – we’re going to be the Silicon Valley guys, nimble and innovative,’” Lutz said. “Everyone who tries to reinvent this business believes that auto companies are populated by dummies who don’t understand Moore’s Law. But, unlike a silicon chip, the modern automobile has to be a certain size, and carry a certain number of people, at a certain speed. Over thirty-five hundred parts sourced from around the world have to come together at the right place and the right time to produce sixty to seventy of these things an hour. These things are called cars. And to make them you need a large engineering staff, a workforce that demands retirement benefits, a tax staff, a fleet of accountants, and an unbelievable amount of reliability testing that Tesla can’t afford to do right now – and we can’t afford not to do. Inevitably, Tesla will discover that the only way to succeed on the scale we have is to be exactly like us.” (Bob Lutz, vice chairman of GM, 2009)

While not as pithy as the other famously wrong quotes unless you reduce it to the last sentence, the amount of hubris in Bob Lutz’s statement about Tesla is astonishing. The number of statements that assert absolutes in terms of how people will travel from place to place using vehicles is telling. I count 12 and that’s being charitable (not breaking some of the statements into even smaller statements and not counting the culmination of the argument which itself is an absolute).

I suppose that’s what we look for in leaders – confidence and vision. But it seems to me that it is precisely this kind of confidence that is the enemy of innovation. In fact, this doesn’t seem like confidence to me at all – it seems more like overconfidence. One way to understand overconfidence is as the “illusion of control: confidence spills over from areas where it may be warranted…to areas where it isn’t warranted at all.” This is how overconfidence leads to bad decisions and an inability to imagine a future outcome that is different from what you currently believe it will be.

At the same time, social scientists view human overconfidence as an adaptive trait. “’In conflicts involving mutual assessment, an exaggerated assessment of the probability of winning increases the probability of winning,’ Richard Wrangham, a biological anthropologist at Harvard, writes.” As human beings, there is a benefit to seeing things with rose-colored glasses or what “social psychologist Roy Baumeister [calls]…an ‘optimal margin of illusion.’” The real trick, of course, is knowing where to draw the line. Nobody seems to have a good rule of thumb for that. However, “all we can say unequivocally is that overconfidence is, as Wrangham puts it, ‘globally maladaptive.’” If everybody is overconfident then a kind of colossal one-upmanship results in which the stakes become higher and higher and if everyone is wrong, losses can be catastrophic.

How does one reconcile the notion of vision – having a passionate belief or conviction about the future – with overconfidence? We want our leaders to have vision. Innovation is in large measure a visionary act. And yet, as we read famously bad predictions from visionary leaders, we see that their success in certain areas has spilled over into confidence about making predictions in areas that seem to be, but turn out not to be, related.

The personal transportation industry is just one example of how difficult it is predict the future. Andrew McAfee’s blog that relates the evolution of cloud computing to the evolution of electrification describes why it is difficult to predict the path that a profoundly transformative technology will take as it evolves. To a large extent that is because the evolution occurs over a long period of time and the development process itself contributes to the direction that the evolutionary path takes. However, at some juncture, a combination of factors leads to an inflection point that draws a bright line between before and after.

Very smart people have a very hard time imagining that the future could look different from an extrapolation of the present. Maybe it isn’t as important to be able to imagine that kind of future as it is to admit that a future which is discontinuous with the present is possible and even probable. Holding that perspective might provide leaders with a wider, if not clearer, view and encourage them to make a few more small bets on a future that is nothing like the past.

Sources:

Famously Wrong Quotes from: http://wilk4.com/humor/humore10.htm

Tesla quote from: “Plugged In,” Tad Friend. The New Yorker, August 24, 2009. An article about Tesla Motors and its founder, Elon Musk, and the electric cars that the company is producing.

Comments about overconfidence from: “Cocksure,” Malcolm Gladwell, The New Yorker, July 27, 2009. An article about how overconfidence leads to bad decisions and its possible contribution to the collapse of Bear Stearns.

Andrew McAfee’s blog: http://andrewmcafee.org/?s=Cloudy+future+of+IT

Filed Under: Innovation, Leadership, Strategy

Is Everybody Unhappy?

May 26, 2010 By Wendi Leave a Comment

When groups are engaged in difficult decisions, the standard refrain that facilitators use to gauge the soundness of the decision that has been reached is along the lines of “Can you live with it?” or “Are you comfortable with this decision?” As a result, people are left with the impression that they should feel reasonably good about the decision that the group has reached – comfortable, satisfied, maybe even a little bit happy. But, a better gauge that a group has reached a good decision when faced with a very difficult problem to solve might be that no one is happy.

While the circumstances of the difficult decision that I am about to relate rise to the level of life and death that fortunately most corporate decisions do not, the lessons learned translate to many of the difficult corporate decisions that face executive groups. About 15 years ago, in 1994, members of the Hutu majority in Rwanda slaughtered more than a million members of the Tutsi minority. About a decade later, the government in one of its many efforts to rebuild the country, instituted a judicial process known as “gacaca” in which those who were responsible for the genocide confessed their crimes before the community and sought forgiveness. The proceedings were formal and resulted in a judgment which could include time in prison (Rwanda abolished its death penalty in the period following the genocide).

The “gacaca” provided a way for the mixed communities of Rwanda to move forward together toward a common future. Rwanda’s President, Paul Kagame, said of the process “’Not the victims, not the perpetrators – nobody will tell you he is happy with the gacaca…’ Kagame didn’t want either side to be happy – ‘because whichever way we go we are left with nothing. Gacaca…gives us something to build on.’”[1]

Successfully dealing with difficult decisions, the outcome of which is that no one is happy, reveals the critical importance of an explicit vision, mission, and guiding principles (or beliefs or philosophy). To cope effectively with temporary dissatisfaction, groups must be united in a commitment to a future state that is supported by a clear common cause and bounded by agreed upon rules of the road. Too often, groups are unable to make difficult decisions because they do not invest the requisite time in formulating or understanding their vision, mission, and guiding principles. As a result, when faced with a difficult decision, the yardstick that individuals fall back on is their personal comfort level. However, for truly difficult decisions, the outcomes of which propel organizations toward achievement of their goals, individual comfort may be the last thing that reflects a good group decision. In these cases, individual unhappiness that everyone accepts may be a much better indicator that the group decision supports the organization’s vision, mission, and values.

[1] “The Life After,” Philip Gourevitch, The New Yorker, May 4, 2009, p. 43

Filed Under: Decision-making, Strategy

Innovation: Complicated or Complex?

May 14, 2010 By Wendi 2 Comments

“It’s Complicated” in Sunday’s New York Times Week in Review suggests that it is essential to distinguish between complexity and complication because each requires a different management approach. In making this distinction, the author (David Segal) is not supported by dictionary definitions which do not distinguish one from the other.

See for yourself. The only way to know that one definition is for complex and the other is for complicated is that no self-respecting dictionary would reference the word it is defining in its own definition.

Composed of many interconnected parts, compound, composite, so complicated or intricate as to be hard to understand or deal with. (Complex)

Composed of elaborately interconnected parts, complex, difficult to analyze, explain or understand. (Complicated)

Dictionary definitions notwithstanding, Segal lists a depressing litany of complex and complicated scenarios (Roman civilization, US accounting standards, possibly contemporary US civilization) which either have resulted or seem likely to result in failure. He then introduces us to Brenda Zimmerman of the Schulich School of Business in Ontario who views both the complicated and the complex as capable of being dealt with successfully, but requiring different approaches.

Complicated challenges are those that can be solved through engineering – good process, sound tools, and capable people.

Complex challenges are those that can be managed through a set of shared, explicit beliefs and values (what Zimmerman calls simple guiding principles).

An example of a complicated challenge might be creating energy from wind. A complex challenge might be enacting a national energy agenda. In the latter example, good process, sound tools and capable people are not enough. Asking a set of basic, principled questions, is essential: Is this good for the country? What are the risks of acting? Of not acting? Are there other options? What do we think is important? What do we believe will happen if we act or if we don’t act?, etc.

In my experience, we like to approach our challenges as complicated ones because the solutions, even if difficult, are tactical and we can do something. Complexity requires first, not doing anything until it’s clear to those who will take action what will bound their activities. Then, we can apply all the good process, sound tools, and capable people we have to tackling the challenge. In effect, complexity gives way to complication.

My last blog post was about the challenges of decision-making when confronted with an overwhelming set of options and lack of clarity about which goals were truly important to achieve. Frameworks and alignment were the critical factors that improved decision-making under conditions of uncertainty. We see these themes reflected in Segal’s piece. Simple guiding principles support a framework that makes it possible to see the boundaries of the challenge that is being faced and create a rubric that aligns actions.

As organizations look for ways to remain vital and viable, they turn to innovation for answers. However, innovation in most organizations has been developed to solve complicated problems, not complex ones. Like most solution sets, innovation is now viewed primarily as an engineering problem that can be solved through process, people and technology. However, when innovation fails, perhaps it is because the challenge was complex and the framework and alignment that emerge from managing complexity were not sufficiently developed to inform the engineering approach that solves complicated problems.

While the dictionary does not distinguish between complicated and complex, perhaps, innovation efforts would benefit if organizations did.

“It’s Complicated,” David Segal, The New York Times, May 2, 2010, Week in Review, p. 1.

Filed Under: Decision-making, Innovation

More is more complicated than you might think

April 20, 2010 By Wendi 1 Comment

More is better. In the world of innovation, especially at the front end (ideation), that seems to be the prevailing notion. Ideation typically involves behavioral techniques and social network technologies that are deployed to unleash powers of association that generate large numbers of new ideas and then winnow them down to a more manageable amount. Open innovation casts a problem out into a vast sea of potential problem solvers. Radical collaboration invites large networks of individuals and groups to band together to collectively get things done. But, while more might be better, it also seems to be a difficult state in which to make effective decisions.

”The Art of Choosing” by Sheena Iyengar was reviewed this past Sunday in the NY Times Book Review.* Iyengar, a psychologist and professor at the Columbia Business School, is the designer of an experiment that is often cited when people want to preach the benefits of moderation. The “jam experiment” gave one group of grocery shoppers a large set of jams (24) and another group a smaller set (10) to taste and then monitored their decisions to purchase or not. The group with the smaller choice set was much more likely to actually purchase jam than those with the larger choice set (ten times more likely). Is it simply too overwhelming to make a decision and act when there are so many options? This is one conclusion that might be drawn from the experiment. It also points to the reason that simple frameworks are often useful when making a decision among many options. Frameworks place large sets of options in bigger buckets, creating a meta-set of options, effectively reducing the number of options at a higher level to facilitate the decision-making process. In situations where a large number of options are likely to be generated, having a framework that everyone buys into as valid for winnowing down the options might be the key that leads to effective decision-making and subsequent action.

Which brings us to the question of what factors contribute to group buy-in of frameworks which are essentially a set of constraints or boundary conditions? In her book, Iyengar also explores the cultural context that appears to influence how we feel about constraints on our choices. The reviewer notes another Iyengar experiment that involved children, word puzzles, and colored markers. Some children were allowed to choose any puzzle or marker they wanted, others were told by the person running the experiment which puzzle and marker they were to use, and the last group was told by the person running the experiment that their mothers had specific instructions for them regarding the puzzles and markers. In the cases where the children were given direct or indirect instructions, they tended to follow the instructions in their choices. But the experiment had another dimension, the children came from two different ethnic groups – Anglo American and Asian American – and their performance on the word puzzles and length of play was evaluated. The Anglo children performed best on the word puzzles and played longest when they were able to choose freely. The Asian children performed best when they allowed their choices to be influenced by what they believed to be their mother’s preferences. Apparently Asian children experience their identities as highly aligned with their maternal relationships while Anglo children do not. Neither group of children performed well when the guidance came from a person they perceived as an outsider.

While there are many intriguing dimensions to this experiment, the one that stood out for me was the alignment factor. If you think of alignment as a vector, a quantity that has both magnitude and direction, the degree to which individuals or groups experience alignment with a given framework seems to be directly proportional to the degree of buy-in and validation they ascribe to it. For some groups, having a hand in developing the framework might increase alignment; for others being aligned with the person or group that has developed the framework may increase alignment. Clearly, one size does not fit all, but alignment seems to be essential. One obvious conclusion that can be drawn from Iyengar’s experiment is that nobody is likely to feel aligned with a framework that comes from what is perceived as a disconnected outsider.

The organizations in which we work exist in an information rich world that presents us with many options, the sum total of which can short circuit our ability to choose and act. At the same time, being able to make decisions and execute quickly is increasingly essential for organizational viability. The power of frameworks and the impact of alignment to improve decision-making and group commitment to implementation are two keys to effectively dealing with an option-intensive world.

*”Indecision-making,” Virginia Postrel, The New York Times Book Review, p. 16, April 18, 2010

Filed Under: Decision-making, Innovation

Rusty Gates, Nonsense Mutations, and Interference

April 5, 2010 By Wendi Leave a Comment

Cystic fibrosis, a fatal disease caused by a genetic mutation that interferes with the cell’s ability to transport chloride ions, is essentially caused by three major types of cellular malfunctions. Without trivializing the gravity and heartbreak of this condition, the way in which these malfunctions impede essential renewal in the human body seems analogous to the ways in which organizational malfunctions choke off renewal and growth. And, the approaches that are emerging to repair the damage might hold clues for organizations that wish to regain their potential for innovation.

The symptoms of cystic fibrosis are a build-up of bodily secretions that cannot be effectively cleared from the gastrointestinal tract and the respiratory system. Over time impaired digestive and respiratory functions lead to death. The genetic mutations that cause this dysfunction fall into three major categories: nonsense mutations, interference, and rusty gates.

Nonsense mutations cause the process to stop mid-stream, producing a useless protein fragment. Interference blocks the protein from reaching the cell membrane. Rusty gates allow the protein to enter the cell membrane, but prevent the damaging chloride ions from escaping the cell. I see the organizational analogues as follows:

Nonsense mutations — Organizational efforts to develop new products that go nowhere, leaving negative histories of failed attempts, are legion.
Interference — All organizations can recite stories of new products that are never successfully integrated into the daily operations of the business.
Rusty Gates — Those new products that manage to gain a toehold but cannot quickly clear the financial hurdles of the established business that measure success frequently falter.

At first, cures for cystic fibrosis focused on swapping a healthy gene for the diseased one. However, it turned out that the human immune system rejected the healthy gene. Researchers had to pursue a different path – one that would repair the malfunctioning gene. A combination of seeing the similarities among more than 1,600 mutations that are defined by the three major dysfunction categories and the advent of a new technology that permits screening of hundreds of thousands of compounds in relatively short periods of time, has begun to yield drugs that show promising results in clinical trials.

Similarly, organizations need to see beyond the specifics of each new endeavor to the systemic and common challenges that all of them face. Openness to trial and error, rapid failure, and sustained focus on the end goal will undoubtedly yield promising approaches. Tools for mining ideas and betting on them are widely available and much cheaper than ever before, making it possible to identify options and gather insights that improve the odds of success. Collaborative approaches for spreading the risk of development and sharing in rewards can be used to rapidly test new ideas in the market. The approaches that medical researchers use to seek improvements in human systems hold many insights for those who wish to improve organizational systems.

This blog post is a reflection on “Open Channels” by Jerome Groopman in The New Yorker, May 4, 2009.

Filed Under: Innovation

Garbage Out, Gold In

March 16, 2010 By Wendi 1 Comment

Back in my knowledge management days, I used to think that the kind of organization that would succeed in the future would be the organization that sat on a lot of interesting data, understood that it was sitting on a lot of interesting data, and, most critically, was able to do something to transform that data into knowledge. I still believe that what will separate organizations that are successful from those that are not is the ability to transform data into knowledge – actionable information. However, ownership of the data itself appears to be a lot less critical than being able to lease access to the data, understanding how to learn from data, and engaging communities in the learning process.

The Economist’s recent report on managing information introduced me to a few concepts that are clearly going to impact wealth creation for an increasing number of organizations in the near future beyond those that have already grasped these concepts and applied them to the creation of new business models. Craig Mundie, head of research and strategy at Microsoft, is quoted as saying, “What we are seeing is the ability to have economies form around data….” So, the notion that success in the future will derive from data might be in the early stages of taking shape. Here are my favorite new concepts:

Data exhaust – The detritus of digital interactions. Example: When I make an online purchase, the ultimate goal of the vendor is to get me to the object I desire at a price point that I’m willing to pay. Along the way, I might enlarge photos, change colors of the object, check out reviews on another site. Some vendors might consider all of that data to be garbage. Others are zealously collecting it and using to better understand how to present purchase opportunities to me, Wendi Bukowitz. And some of them are selling that knowledge to other vendors who want me to consider their products but lack the data and the ability to analyze it.

Big data – As the amount of data skyrockets to a mind-boggling size, analytics on very large data sets is revealing previously hidden connections – some of the most important may be the ability to detect early indications of pending system failure, whether in machines or in human beings. The store of data surpassed storage technologies in 2007, and is growing at a compound annual rate of 60%. And it coming from more and more sources, not just the old standby’s of computers, cellphones, and cameras but increasingly sensors that are embedded in everything – products, buildings, pets, roads, people.

Crowd-sourcing is better than smart algorithms – Okay, it’s good to start with a smart algorithm, but if you can get lots of people to tweak that algorithm by using it, it turns out that you will achieve a better outcome. For example, rather than trying to write the best translation rules, it’s proving more successful to write good enough ones and then let people who use translation systems select the translation option that is most accurate. The combination of judgment and number crunching, rather than an exclusive reliance on one or the other, is asserting itself as the best way to make really good decisions about almost anything.

Most organizations treat the majority of the data they generate as garbage – data exhaust. They routinely fail to investigate the possibilities of mining their data for new opportunities and, under the knee-jerk banner of data security, routinely lock anyone except a (relatively) small stakeholder group of employees and customers out of the data creation playing field. This is not to say that there aren’t good arguments (and sometimes requirements) for sequestering some of the data, but I would like to see a more thoughtful and strategic approach to exploring the possibilities of gigantic piles of data. Because, more and more, it’s looking like garbage out, gold in.

Filed Under: Business Model, Knowledge Management, Strategy

Sample Size of 1

March 1, 2010 By Wendi 1 Comment

Why do we cling to beliefs even when we know that they are wrong? That is the persistent question that thrummed in my thoughts as I read The Ascent of Money, Niall Ferguson’s financial history of the world. Ferguson’s book is a highly readable study of financial innovations that transformed economies, intertwined themselves with politics, and shaped world history. As the reader is taken on a tour of financial history, the characters and scenes change, but the potential for disruption continues unabated. Each innovation has unintended consequences which are sometimes more profound than the innovation itself. In almost every case, the inventor believes that he has a complete understanding of how the change that he initiates will play out. And, invariably, his understanding which is based on history is shown to be inadequate.

While the past may be prologue, it is not predictor of the future. And yet, we treat the future as potentially knowable and are endlessly startled when what happens seems to border on the impossible. Ferguson cites Frank Knight: “Again and again an event will occur that is ‘so entirely unique that there are no others or not a sufficient number to make it possible to tabulate enough like it to form a basis for any inference of value about any real probability.’”* We want to believe that the sample size on which we are basing our forecast of the future is sufficiently large and the events are sufficiently independent of one another to help us assess risk. But in most cases, the events occur in a sequence and are related to one another, reducing the sample size effectively to one. So we are left with uncertainty – that which is unknowable and unpredictable – but we cling to our belief that we are managing risk.

These notions are incredibly important because the universal language of business is finance and a key concern of finance is managing risk. Innovation – bringing something new into existence – is more than inherently risky, it is a profoundly uncertain exercise. That is why it makes more sense to invest resources in experimentation rather than developing detailed business cases based on historical data. Small, ideally scalable, experiments can be stopped if they fail or expanded if they succeed. Because it is very hard to predict in advance what will succeed at any given time. To believe that you can may be comforting, but it is wrong because the data that is used to support most business cases turns out to be based on a sample size of 1.

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A few weeks after writing this blog post, I was reading yet another analysis of financial risk. This time, it was The Economist’s special report in the February 13, 2010 issue. One of the pieces quoted John Maynard Keynes: “It is better to be roughly right than precisely wrong.” Somehow this idea seems related to the challenge of clinging to beliefs that we know to be wrong. Perhaps it’s because we also believe that we are roughly right at the same time. But being roughly right also implies that you are roughly wrong. However, we tend to discount the roughly wrong and inflate the roughly right. The article also quoted Chuck Prince, head of Citigroup in 2007, who described why the bank pursued activities that, as it turned out, were precisely wrong: “as long as the music is playing, you’ve got to get up and dance.” We have a very poor understanding of how to simultaneously hold the notions of roughly right and roughly wrong, which seems to lead us into situations where we are blind to precisely wrong.

* Frank H. Knight, Risk, Uncertainty and Profit (Boston, 1921)

Filed Under: Innovation, Risk Management

The Big Convergence: Sustainability and Innovation

February 16, 2010 By Wendi 1 Comment

Usually, I take a dim view of the possibility for true innovation at big, successful, established organizations. I’m not talking about innovation that is really incremental improvement, I’m talking about business model innovation that is highly disruptive. My sense is that most organizations cannot muster a strong business case for being the force behind innovation because they cannot create a scenario in which they are certain that the outcome will be net positive (economically) for their own organization. So, most organizations, like most people, wait for change to seek them out.

However, last night I found myself expressing exactly the opposite opinion. In a conversation with an old friend from college, I said that there was evidence that some big companies seemed to be capable of true innovation. I told her that I had recently come across two data points that have caused me to revise my generally pessimistic stance.

I believe that the trend of sustainability is behind this developing capacity for innovation and that more data points will emerge over time as this trend becomes established practice. By sustainability, I am talking about an approach to wealth creation that seeks to yield more than economic value. It also seeks to achieve positive environmental and social impact. The idea that there is more to wealth creation than purely economic gain has been knocking around a long time. Almost a decade ago, in The Knowledge Management Fieldbook, my co-author and I pondered what wealth might look like when the model for wealth creation included forms of social and intellectual capital and the boundaries of the organization blurred so much that customers and even competitors were jointly building wealth. I believe that these glimmers of change which were visible years ago have coalesced or converged in the movement that is known as sustainability.

Data Point 1: Nike’s Green Xchange (GX) Nike along with several partners has established an open innovation collaborative through which the partners will share intellectual property (some will be offered free for further research, others will be licensed) that has the potential for significant positive environmental impact if the technologies are widely used. For example, Nike’s Environmentally Preferred Rubber which has 96% less toxins than previous versions is being offered for licensing on the exchange with the hope that other manufacturers who use rubber might opt to incorporate this technology. Here we have a case in point of inviting your competitors to collaborate on industry changing advances because it is better for everybody.

Data Point 2: GlaxoSmithKline’s commitment to making its drugs affordable in developing countries.* In addition to promising that prices for drugs in poor countries will not exceed 25% of the price in rich countries and donating 20% of all profits in poor countries to building their health care systems, the company has started to post chemicals that might be effective in treating malaria on web sites for anyone to research. Some of the company’s competitors are not happy about this “…undermining a critical piece of the business model.” But the CEO, Andrew Witty says, “I’m in charge of an organization that can actually make a difference for people in the third world, and I’m not going to be the person who, after x years, sits back and says, ‘On, I wish I’d done more.’”

*from “Ally for the Poor in an Unlikely Corner”, Donald G. McNeil Jr., The New York Times, February 9, 2010.

Filed Under: Innovation, Sustainability

Collective Intelligence Genomics

January 30, 2010 By Wendi Leave a Comment

Is there a genomics of collective intelligence? If there is and you understand its genomes, can you put collective intelligence to more effective use? That’s the argument coming from the MIT Center for Collective Intelligence in a paper titled: “Harnessing Crowds: Mapping the Genome of Collective Intelligence.” The authors (Malone, Laubacher,and Dellarocas) state that the Internet has enabled new forms of collective intelligence which they define as “groups of individuals doing things collectively that seem intelligent.” (Not too tough to wrap your mind around that definition, but it does seem a bit wanting — after all who decides what is intelligent?) They point to such Internet phenomena as Wikipedia, Google, and Threadless as cases in point. Then, they model the working elements of the collective intelligence ”gene” based on organizational design theories.

The diagram above shows the basic elements of the collective intelligence genome and options for how it is expressed. The Who and Why elements are pretty straightforward. However, the authors segment What into two sub-elements — Create and Decide — and mirror that segmentation in the How element. Create is associated with two approaches — Collect and Collaborate — and Decide is associated with two different methods — Group and Individual. The authors further decompose Group decision-making into voting, consensus, averaging and prediction markets. They note that two variations exist for Individual decisions — markets where a formal exchange in involved and social networks – both of which influence individual decisions.

Why go to all this trouble? The operating assumption is that if you can understand what genome is best suited to a particular situation, you can get the best outcomes from collective intelligence. A quick example: Ebbsfleet United which proudly advertises itself as “the world’s first and only web-community owned football club.” For £50 a year, you can be an owner and vote on all key team decisions and the financial budget. Ebbsfleet United is an example of an Internet-enabled Crowd gene, where member-owners decide in a group and share in the love and glory of being a team owner. The paper presents other examples which bring the theory to life and demonstrate which expressions of the collective intelligence genome succeed under different circumstances.

Filed Under: Innovation, Knowledge Management

Scrappy Start-up versus(?) Corporate Giant

January 21, 2010 By Wendi Leave a Comment

When corporate giants lose their way, many seek to return to scrappy start-up mode — the type of organizations that people seem to think have no choice but to really understand what their customers want.

In today’s New York Times, we read about just such an approach at Starbucks which brought back its founder, Howard Schultz, to lead a turnaround. “…we went back to start-up mode, hand-to-hand combat every day. And with the kind of discussion and focus that probably we had not had as a company since the early days — the fear of failure, the hunger to win.” In its efforts to recapture relevance with customers, Starbucks is striving to provide a truly authentic, local coffeehouse experience. Its challenge, of course, is that a large corporate culture seeks to standardize to control quality and make a profit. Will Starbucks be able to deploy the resources of a large corporation to develop a mass-customized experience in the physical world where the cost associated with diversity is high? If they can figure this out, what a competitive advantage they will have achieved compared to McDonald’s (which is trying to grab breakfast and snack break coffee market share).

In a completely unrelated arena, even a company that most think of in the most superlative terms when it comes to innovation — Google — has had to resort to scrappy start-up mode for its next great breakthrough, Google Wave. Lars and Jens Rasmussen, two brothers who invented the idea for Google Maps, are the inventors of Google Wave which many hope will be the next generation of email. To translate their idea into reality, the Rasmussen brothers have tried to recreate the high-stakes stress incubator in which Google Maps was born.

This was their formula:

Google Wave would operate as a start-up company within the corporate giant of Google.
The 60-person Wave team would be based in Sydney, Australia, far away from Google’s corporate headquarters in Mountain View, California.
Google employees who wanted to work on Wave would have to take a risk to join the brothers — taking cuts to bonus pay in hopes of a big payout if Wave succeeded.
The project would be secret. None of the project files, codes or other documents were accessible to anyone else at Google.

Perhaps, the world of corporate and other institutional giants is just so vast that it is hard to know when you are serving the customer and when you are just making your way through the bureaucracy. In both of these examples, the ultimate goal is to shorten the path between the organization and the customer, speeding the delivery of value — whether it’s a cup of specially brewed coffee or specially crafted information.

Sources:

“Now at Starbucks: A Rebound,” Claire Cain Miller, The New York Times, Thursday, January 21, 2010, B1.

“ The Genius Brothers Behind Google Wave”, John D. Sutter, CNN, October 27, 2009.

Filed Under: Innovation