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Archive for the ‘Innovation’ Category

It’s been a while. Did you miss me?

Since I started this blog as a creative outlet to complement my day job, I have always tried to keep up a cadence of roughly one post per month, but I always let inspiration be the real metronome. If I was feeling particularly contemplative, I might queue up a couple posts at once to help keep the pace over slower periods.

Until recently, I have been decidedly uninspired, hence the lack of posts. There were just no topic coming to mind that I wanted to explore further in written word. Although I was reading as many books and articles as ever, nothing was all that provocative. Even after SXSW in March, zip (ok, maybe a few notes on topics I wanted to revisit later).

Writers block. Where does it come from and how can it be dealt with?

Creativity in all its forms seems to be a matter of pattern formation, all the way down to the neuronal level. Recognizing patterns where none are readily apparent. Constructing patterns that are both nuanced and pleasing. So why is it the patterns seemed to have been escaping my attention recently? What’s changed?

When I looked back at the date of my last posts (including one I never published), the dates seemed to coincide with when I decided I was ready for a change of scenery and prepared to move from the Bay Area to Southern California. Maybe there was more than coincidence or correlation at play here; maybe there was a cause.

Influenced by some work I did recently on telematics and the Connected Car, I came to suspect writers block might have something to do with cognitive load. Turns out cognition is a scarce resource. It stands to reason, then, that preoccupation with one thing or another would have a crowding out effect.

I hypothesize that concerns about my move – clearing things with my employer, finding a new apartment, moving out of my old apartment, packing up and transporting my life from one city to another – left very little to get creative and write about.

At the risk of extrapolating personal experience out too far, this hypothesis would seem to be consistent with social and cultural evolution. The arts and sciences have flourished in societies and periods of relative stability. If you’re worried about where you’re going to get your next meal, there’s no point pontificating on your navel.

This would also support practices such as Google’s famous 80/20 rule. If you want your people to innovate, you need to leave enough slack in the line for them to (mentally) explore a bit.

Bottom line, stress is the enemy of creativity. A happier workforce is going to be a more innovative workforce.

Well I’m back and feeling much more inspired.

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While spending some time recently in NYC, having gotten through all the reading I brought with me more quickly than expected, I decided to do a quick re-read of The Innovator’s Solution.  It is the first place I can recall seeing  “value networks” used in place of “value chains,” and so I began reflecting on the notion of value networks further.

“Value networks” suggests new complexity to creating what Geoffrey Moore calls a whole offering.  Value isn’t created sequentially, in a chain similar to the assembly lines of the factory system.  Value comes from multiple partners working in parallel value chains that assemble together into a larger network of value for customers.  Think about the value of the iPhone that is derived from the phone itself, the infrastructure provided by mobile carriers, and the population of app developers.

Companies need to be comfortable operating in these sorts of large and complex value networks.  They need to know where they sit within a given value network, and understand the relative position of their competitors and partners in theirs and other value networks.

Partner network development and management has become a business critical capability, enabling companies to take advantage of both open innovation and outsourcing and alerting companies to emergent opportunities and threats in adjacent markets.

It occurred to me that relative to its importance, little is available to aid firms in developing this competency.  Managers and executives need tools and frameworks to help design strategically advantageous partner relationships and actively manage partner networks.  While originally intended to help companies craft an open innovation strategy, the strategic openness matrix I started working on a few weeks back could help solve this problem.

Replacing research areas with capabilities, managers and executives could use the matrix to determine the capabilities most important to differentiation and competitive advantage (the so-called core) and those that are necessary but undifferentiated (sometimes referred to as context or periphery).  Core would be targeted for internal investment while context might be outsourced to a more capable partner.  Should the focal firm already be a leader in a “context” capability, offering that capability as a service to others may present an opportunity to launch and grow a new business (e.g. think Amazon Web Services).

What about selecting partners and designing (yes, willfully designing) the dynamics between partners?  For that I have drawn inspiration from Osterwalder’s business model canvas and customer value map.  I created what I’m calling (for now) the partner relationship map (referred to as “the map” for the remainder of this post).  As with the strategic openness matrix, I am making this early version available under a creative commons license so that others might build off of and improve what I started.

The map is separated into three sections.  On either side of the map are the sections representing the focal firm and the partner firm (which side is which is an arbitrary decision really).  Each firm/partner is endowed with a unique set of resources and capabilities that it employs in the service of specific customers and according to identifiable business imperatives, all of which is reflected in the map.

The middle section represents the interface between the two firms.  The interface is characterized by the level of interdependence and integration between the two firms.  Two firms might be highly interdependent with a customized interface that approaches vertical integration.  Of course, tight integration does not require mutual dependence (is Apple really dependent on any one developer for its iOS platform?), and such asymmetries  create business risk that must be recognized and actively managed.

The inverse of a tightly integrated interface would be a modular interface, characterized by a high level of standardization that allows firms to easily swap out one partner for another (or be swapped out).  Returning to the example of iOS, its noteworthy that from the perspective of the app developer, the app it is creating will only run on iOS and must be modified for Android, implying tight integration between the app and the platform.  From the perspective of Apple, the interface is standardized so that any app developer can plug into the iOS platform.  At the interface, perspective and directionality matters.  To understand how, it helps next to consider the arrows going in either direction labeled value proposition.

Firms present a value proposition not just to customers but also to partners.  In a partner relationship, there is an exchange of value, as reflected by the reciprocal value propositions in the map.  The partner uses its resources and capabilities to offer some value in service to the focal firm’s business imperatives or in service of its customer segments.  In return, the firm offers the same, perhaps most commonly in the form of a payment that serves the partners imperative to grow revenues and make a profit.

The value proposition of the firm may be appealing to lots of partners of the same sort (e.g. SFDC, Wal-Mart, and Amazon all offer valuable platforms to their partners) or only a few.  The value proposition of the partner may, too, be appealing to lots of firms (think of outsourcers all the companies they serve) or only a few.

Let’s consider another case of a focal firm that has created a valuable platform which its partners can leverage to reach their customers.  Facebook is one such case, with its partner Zynga.  From Facebook’s perspective, it has created a modular interface, accessible to all sorts of partners, Zynga included.  Facebook offers access to a large population of users who frequent the site.  From Zynga’s perspective, the interface is highly customized; offerings have to be built or adapted in some way to plug into the Facebook platform specifically.  Zynga enriches the Facebook user experience with fun games, but lots of other companies could offer a similar value proposition.

The partner relationship map can be used to identify these sorts of power asymmetries.  They can also be used to come up with win-win partnerships that balance out the power and value propositions on both sides.  I’d invite anyone who has gotten to the end of this post to try out the map; draw out one of your partner relationships or the partner relationships of an example company and reply in the comments with your thoughts on how useful the map is and how it might be improved.

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In my last post I discussed aspects of the execution gap at most companies when it comes to innovation.  At the end, I posed a couple thought questions because these were questions I myself have been thinking about for some time, albeit in other contexts.  In particular, I’ve wondered a lot about how companies can better define an open innovation strategy.

Open Innovation Fallacies

I’ve noticed a lot of confusion around the topic and meaning of open innovation.  The term has become misconstrued over time, often mistaken as being simply synonymous with crowdsourcing.  Open innovation is actually a two way street, with ideas flowing into and out from a firm’s boundaries.  Open innovation is characterized by that permeability, not the directionality.

Another mistake is to conclude the increased flow of ideas implies a loss of strategic focus.  To the contrary, open innovation should allow for greater focus.  Rather than waste resources trying to squeeze a square peg into a round hole, innovative ideas that do not fit well with a firm’s strategy and/or business model can be made available to others to monetize.  Similarly, firms can also focus limited resources on the most important, differentiating R&D and open themselves up to outside ideas in areas further from their core competitive advantage.

Most importantly, open innovation is not an innovation panacea.  It should not entirely supplant other sources of innovation – specifically, innovation driven by traditional R&D.  Open innovation, in all its various forms (e.g. crowdsourcing, M&A, joint ventures, innovation contests, etc.), should be used in combination with R&D investing to reach a firm’s innovation goals.

Each of these fallacies leads to a misapplication of the principles of open innovation.  So how do you know what innovation projects you should be opening up to others outside your firm and what you should continue to protect and incubate inside a walled garden?

The Strategic Openness Matrix

That is the essential questions I have tried to answer with an initial version (call this v0.1) of what I am referring to as the Strategic Openness Matrix (please, help me come up with a better name!).  I’ve really just repurposed the House of Quality (HOQ) matrix from Quality Function Deployment (QFD) to build a tool that will help companies craft an open innovation strategy.  (Note: I’ll assume some basic familiarity with the HOQ matrix in the explanation that follows.  A good primer can be found here.)

To build this matrix, I’ve started with a strategy canvas from Blue Ocean Strategy rather than the voice-of-the-customer as I would with the HOQ matrix (go here to learn more about the strategy canvas itself).  For expediency, I used a canvas that I created for a previous discussion of online music services as an illustrative example.  Each of the competitive factors in the canvas along with its corresponding value are listed on the left hand side on the y-axis.  For reference, I’ve also included some generic competitive benchmarks of the same factors (don’t get hung up on the values; they’re all just illustrative).

Relationships between Competitive Factors and Research

At the top, along the x-axis, are all the top level areas of research – what I’m calling L1 or Level 1 research areas.  As you’ll see in a bit, each top level area of research can be broken down into a number of more specific, component research areas – Level 2 research and then potentially further into Level 3 research and so on to the desired level of granularity (much like a process decomposition).  If the competitive factors are the “what’s” from the HOQ matrix, these are the “how’s” from the HOQ.

I’ve also indicated at the top an estimate of the firms current research capabilities in a given area.  This could be a somewhat objective estimate – based on access to specialized lab equipment for instance – or highly subjective.  With only three possible values – leading, lagging, and pacing – even a guesstimate will suffice.

For each competitive factor, we rate its relationship to each research area on a scale of 1-10, to be consistent with the scale used in the strategy canvas.  Really the scale is somewhat arbitrary since what will ultimately matter are the relative scores calculated from these numbers, not the absolute scores.  In any event, these relationship values are entered at the intersection of the rows and columns, with 1 being a weak relationship and 10 being  strong. (Note: zero is not used for reasons of mathematical practicality.)

The Research Importance Score

For each research area I calculated a research important score by first multiplying the strategic importance rating with the relationship rating for each competitive factor (so 8, the strategic importance rating for Undirected Listening, multiplied by 10, the relationship score with Automated Song Selection Algorithm). I then summed up all the values in a given column and divided by 10 to get my research importance score (dividing by 10 is, again, because scale is arbitrary here).  This follows the same procedure as the HOQ matrix.

To illustrate with an example, for the first L1 Research Area on the left, Automated Song Selection Algorithm, the math comes out to (8×10 + 3×3 + 6×0 + 5×6 + 3×6 + 9×6 + 7×8)/10 = 24.7.  In the beginning of the polynomial equation, 8 is the strategic importance rating for Undirected Listening and 10 is the relationship score with Automated Song Selection Algorithm.  24.7 is the total research importance score.

If the math is confusing, try looking at the spreadsheet by clicking on the image and downloading it from Box.  The used logic is that the more important the competitive factor and the stronger the relationship, the higher the research importance score should be.  The more relevance a given area of research has to the various competitive factors, the higher the score should be as well.  I’ll explain how the score is used shortly, but for now, just think of it as a proxy for the strategic importance an area of research, as the name implies.

Synergies and Trade-offs

There is another relationship to be considered also which I haven’t mentioned yet, the relationship between research areas.  This is the “roof” in the HOQ matrix.  The correlation adjustment is intended to increase or decrease the research importance score to account for synergies and trade-offs.

The math may get confusing again here.  For a given research area, I assigned a correlation coefficient with each of the other research areas (this appear in a separate table on a different tab, “L1 Research Correlations”) and multiplied those correlation coefficients by the corresponding research importance score for each research area.

Confused?  Here’s the logic.  The higher the positive correlation a research area has with other research areas, the higher the adjustment should be.  If a research area creates a lot of good synergies, it’s going to be more important to a firm.  If there’s a negative correlation – a trade-off with another research area – that is a negative adjustment.

Adding the strategic importance score and the correlation adjustment, I can now calculate a net score for each research area.  Next, I stack rank these net scores – the higher the score, the higher the ranking, and I identify the top, 2nd, 3rd and bottom quartiles (these cells are hidden in the spreadsheet I created).

The Strategic Recommendations

The final step is to apply some logic to recommend how the firm should handle a given area of research.  The logic I’ve used probably needs to be more finely tuned but works fine for a proof of concept.  The logic looks at the relative strategic importance score (specifically where in the stack ranking and quartiles it falls) and where the firm’s current research capabilities are.  Here it is laid out in plain-ish English:

  • Lagging and relatively unimportant, Partner Openly to innovate
  • Lagging and relatively important, Over Invest (internally) or Acquire to close the gap
  • Pacing and relatively unimportant, Partner Strategically to amplify minimal internal investments
  • Pacing and relatively important, continue to Invest internally to keep from falling behind but don’t share too much, which might allow others who are “drafting” behind your research to actually jump ahead
  • Leading and relatively unimportant, consider additional ways to Monetize the research externally (generating more funds for internal innovation) or Reallocate some fund away to more important areas
  • Leading and relatively important, Protect the R&D investments supporting your competitive advantage

These recommended actions are not strict rules but rather suggestive indicators, guidance for management to consider along with other perspectives.  I was pleasantly surprise by some of the recommendations the logic actually makes.  For instance, in this example, I have placed a high priority on Sociality (see the original blog post to understand more about what Sociality means).  The research most strongly related to Sociality is Synchronous Sharing and Communication.  You might think this would be an area to Protect if the firm is Leading or an area to Over Invest/Acquire if it is Lagging, but that’s not the recommendation the logic produces when you consider all the other competitive factors and research areas.

Synchronous Sharing and Communication gets a relatively lower importance score because it has a weaker relationship to the other competitive factors and only modest correlation with the other research.  If the firm is leading, it might consider alternate monetization options (firms not in music that could use technology for adding communications features to their products) or reallocate resources away to other deserving areas.  If the firm is lagging, it should consider partnering openly, perhaps integrating with instant messenger or VOIP partners rather than going it alone because closing the R&D capabilities gap would be to large a drain on resources.

This is precisely the kind of thought provoking results a tool or framework should produce, overriding our mental biases and forcing us to think different!  Another interesting result from the tool is that if everything is lagging, the resulting recommendations are a very focused innovation strategy.  Put everything into the few most important research areas, partner openly with others to close the gap for all the rest.

Getting Granular

I’ve begun to build out the L2 section of the tool as well.  It normalizes the net research importance score to get back to a value between 1 and 10.  From there, it is more of the same except starting with the L1 research areas on the left hand y-axis.  The resulting importance scores will allow the higher level recommendations to be overridden for a more precise handling of the lower level innovation projects.  Different logic and rules may also need to be developed and applied as you get more granular.

I am really excited about this tool  (or does this qualify as a framework?), but it is still just v0.1.  It can only get better with feedback from others so following the example of Alex Osterwalder and the business model canvas, I’m making it available here under a Creative Commons license.  Please cite this blog if you use it and make any derivative works available under an equivalent Creative Commons license.  Share this post on Twitter or Facebook, and please post comments for ways you think it might be improved.

Creative Commons License

Strategic Openness Matrix by openopine.wordpress.com is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

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There is no shortage of books and consultancies purporting to help companies be more innovative, but I have found a great deal of the focus is on generating ideas.  There still seems to be unmet demand for help selecting ideas, turning those ideas into a portfolio of strategically aligned projects, and managing that portfolio of projects overtime – particularly as it relates to incorporating new learnings back into the portfolio, reallocating resources across the portfolio when appropriate, and understanding when and how to scale good ideas to transform the business.

I find this “job-to-be-done” particularly interesting because (relatively) recent research from Jim Collins suggests that being innovative, while important, is not the most important factor to the overall competitiveness and success of a company.  As PARC Chief Business Officer Tamara St. Claire put it in an MIT Technology Review article, “You learn early on that execution is often the hard part—execution and timing . . . You almost have to be as innovative in the commercialization—especially when you have game-changing technologies—as on the technology side.”

A number of technology solutions have emerged to help manage the innovation process, such as Spigit and Brightidea, and other social enterprise solutions are sure to add more innovation management feature sets (indeed Jive already has).  These software companies are also staffing consulting teams to drive adoption  within client teams and teach  clients how to get the most value from their solutions because the technology, in essence, has outpaced the development of people and processes to use it.

Letting technology drive the development of people and processes reverses the normal order.  I firmly believe whenever possible technology should be fit to behavior and not the other way around; otherwise, disappointing adoption could completely undermine the value of the new tool.

That’s why relying on captive consulting teams at software providers to develop your innovation management capabilities is so misguided.  Firms need a more objective third party to drive the necessary changes in people and processes first, then recommend the technology solution that fits best.

It seems to me this would be an excellent opportunity for management consultancies to partner with design and innovation consultancies to combine their complementary domain expertise and go after this market opportunity.  What if Six Sigma frameworks could be re-purposed to help companies understand not where they need to improve quality but where to innovate?  How might the PMI body of knowledge look different when applied specifically to innovation projects, valuing validated learnings over vanity metrics and immediate financial return?

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Like many others, last week I waded into the emotionally charged debate set off by the blog post from NPR intern Emily White and the subsequent response from David Lowery.

I found myself conflicted. As a music fan, I was somewhat alarmed and really disappointed by Emily’s admission to piracy, but as a self-professed member of the Free Culture movement and fan of the Creative Commons, I despised the way David developed his argument, manipulating the truth and using scare tactics.

In both cases, I found the authors distracted from their central points with ancillary comments that set off the fire storm. I won’t get into the problems I have with David’s arguments, but I agree with his conclusions – artists are due some compensation for their creative works; piracy is wrong and illegal, and music fans should feel a moral obligation to pay for music, not pirate it.

As a reflection of the attitudes of her generation, Emily’s lack of attachment to the the physical mediums of music (e.g. CD’s) and preference for a limitless stream of digital content a-la Spotify was telling. The music industry should be listening; this is the markets telling you its demands. Her point was lost, however, because she admitted to piracy while remaining fully unaware of her actions and their implications. Ripping a CD you didn’t pay for is as much piracy as downloading from Pirate Bay. It’s just another form of P2P.

It would seem Emily has rationalized her actions as something other than piracy; I don’t think she or anyone else (reasonable) is saying piracy is ok. The real debate, as I understand it, centers on enforcement, not unlike the illegal immigration debate in thew news of late. Does it make sense to go after individual fans for file sharing or might everyone be better off spending less on lawyers and more on developing legitimate paid alternatives to piracy?

The music industry is in disruption; it’s not the only industry that has been disrupted by digital distribution. Unfortunately, sometimes “creative destruction” destroys more than it creates. At the macro level, this is how the free market works. At the micro level, real people are hurt in the process. As Keynes said, in the long term, we’re all dead.

Everyone affected by the music industry disruption would be better served to invest resources in the productive pursuit of new sources of value rather than expend resources on unproductive attempts to hold onto the past. I feel for the artist that has to work a second job or give up entirely on a career in music because he or she can’t make money, but none of us are simply entitled to make money from following our passions. It’s a lucky privilege for a very few.

Lots of people toil away at jobs they dislike, and this notion of career mobility, that is a modern phenomenon. It used to be that you became a farmer because your father was a farmer. Choice (too much of it anyway) has contributed to the general ethos of complacency and entitlement that is now catching up with generations of Americans (mine included). (Of course, people still aren’t taking responsibility; it’s the economy.)

Finding the new business models for making money from music requires first acknowledging that there simply may not be as much money in recorded music as there once was. I don’t know that this merits any great alarm or mourning; digital distribution changed the value chain, and other value added services are now in demand instead. The same thing has happened across countless industries throughout economic history.

Keep in mind that the recorded music industry is also a modern phenomenon. Musicians have been making music for thousands of years without worrying whether someone was buying or pirating their CD’s. Music will survive even this industry disruption. (Come to think of it, shouldn’t the purists be welcoming this change as expunging some of the corrupting influence of commercial interests on art?)

I still believe musicians have a right to control their works and realize a return on their investment of creativity. No one, however, is in a position to say how much compensation they are owed (at least not in a free market), and one cannot just expect recorded music to contribute the same returns as before.

The quickest and surest way to combat piracy at the individual level is to offer legitimate alternatives at an attractive price point given the consumer’s “job-to-be-done.” (Of course this still means using the legal system to prosecute large scale piracy where the cost/benefit makes more sense.)  Although I only have anecdotal evidence to prove it, I believe the majority of piracy happens at the edges of nonconsumption – individuals that otherwise would never have bought a particular CD at the regular price but might be willing to pay a price closer to the actual marginal cost of distributing another digital copy, which is to say something near zero.

What might artists and musicians learn from TED, where so much of the content is given away for free? Instead of fighting fans on piracy, how might artists take control over their creative works back from labels and record companies as OK Go and Louis C.K. did?  Opportunities to make money are still out there, if you don’t let yourself be distracted by the ones that have already passed us by.

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A conversation at work recently got me thinking a lot about the value of openness. The irony of that statement should become clear by the end of this post.

Since open innovation entered business parlance, one of the unsolved challenges has been intellectual property rights.  In a more open world, who owns the rights to an idea?  How can we more efficiently share rights and returns on our ideas?

Lawrence Lessig has written eloquently on the subject in books such as Free Culture: The Nature and Future of Creativity, and solutions such as Creative Commons have emerged to address the issue.  Still, this problem of who has the rightful claim to an idea seems to persist.

Undoubtedly, in a capitalist system, great thinkers need to be assured of some return on their ideas to maintain the incentive for sharing their great thinking.  On the other hand, “no man is an island.”  The myth of the sole inventor is just that – a myth.  The instances of simultaneous innovation are numerous and well documented, despite the predilection of popular consciousness to selectively remember only inventors such as Edison over Tesla.

The true origins of an idea are diffuse and imprecise.  The “aha!” moment is as much as fallacy as the myth of the sole inventor.  Insight comes from a pattern of multiple connections in more than one very real sense.

In neural activity, it is characterized by the gamma-waves commonly attributed to the formation of new neural connections in the brain.  The brain holds all sorts of disparate ideas in memory, and the “aha!” moment of insight and innovation is the experience of finally connecting those ideas together in some novel way.  (For a more accessible discussion of the neuroscience than the Beeman and Kounios article, I recommend a quick read of Imagine by John Lehrer.)

Steve Johnson was spot on when he said, “Chance favors the connected mind.”  Of course, Johnson was also referring to social connections, which is the other very real sense in which insight comes from connections. Social interactions expose us to ideas, the same ideas that are held in the memory of our brain.  There they sit, lying in wait for a pattern to emerge when one idea connects to another and still another – what Johnson terms, “the slow hunch.”

I think about innovation as a chemical reaction.  A chemical reaction isn’t something you do; rather it’s more like something that happens.  You can manipulate the conditions for a chemical reaction, by adding catalysts or energy in the form of heat or motion, but you cannot will it into being.  A chemical reaction occurs when the molecules and elements collide, breaking old bonds and forming new ones.  The more collisions are created, the faster the chemical reaction.

Innovation is the serendipitous collision of ideas.  Those ideas originate from different places and at different times – intense brainstorming sessions at work, a good read on a long flight, a relaxing stroll on the beach.  If one of those ideas came from a conversation with a coworker, does my employer have claim to my insight?  (Now do you see the irony?)

If we want to innovate, it seems counter-intuitive that we would also want to reduce the number of collisions by talking only among ourselves in soft tones.  The Internet has been such a fabulous engine of ingenuity because it is such a transparent and highly visible medium.

I suppose some compounds are more reactive than others and don’t need to be spurred on; some problems can be solved in the walled off gardens of R&D labs or stealth start-ups.  I’m not convinced that’s the case with the really big game changers though.

AnnaLee Saxenian wrote a very interesting book on why Silicon Valley is in California today instead of along Route 128.  Her argument that the openness of Silicon Valley (e.g. non-proprietary standards, decentralized organization, and cooperative exchange)  was its advantage is very compelling.

Look at the example of the Homebrew Computer Club.  Apple wasn’t invented in secret; the technology that Wozniak and Jobs used to revolutionize the computer industry was shared freely among like minded hobbyists.  Indeed, if you read Walter Isaacson’s Steve Jobs it seems much of what contributed to the early success of Apple did not actually originate within Apple but PARC.

Apple out executed.  That’s why being the most innovative doesn’t actually matter.  It’s getting the business model right that wins.  If you can’t do that, it doesn’t matter how long you hide your light under a rock. When you finally do start marketing to customers, another fast follower is going to eat your lunch.

Let me reiterate: I am not denying the risk that someone else could be more successful with your idea than you.  I am questioning if that is a bad thing – exposing people that risk and letting the best person win.  Wouldn’t that make for a more efficient market?  It’s the only way you are going to get out of the building and be sure you are onto something.  It’s the only way you are going to avoid group think and positive illusion about your own ideas.

Stop wasting time and energy trying to keep your ideas from other.  Focus instead on unlocking the value from those ideas – before someone else does.

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Since about October 2009, I have been trudging along with a skunkwork at my current firm to layer social networking features on top of our existing knowledge management system.  Progress has been slow for reasons I attribute to  innovation myth #5, but my resolve was bolstered recently upon uncovering (by chance) an HBR blog post that’s about a year old now from the Deloitte LLP Center for the Edge.

To build the business case for giving my initiative official sanction (and by extension funding), I put a lot of time into articulating the benefits of social networking in the business context – the value to the firm in our parlance.  My starting premise was that our knowledge management system was great for codified knowledge but it lacked any functionality to manage the arguably larger pool of tacit knowledge stored collectively in our consultants.  One of the potential sources of value that I foresaw John Hagel and John Seely Brown put in these terms:

“We’re moving from a world where value is created and captured in transactions to one where value resides in large networks of long-term relationships that provide the rails for much richer “knowledge flows.” For open innovation to realize its full potential, it will have to navigate a similar course from a narrow focus on transactions. It will have to provide much richer support of long-term, trust-based relationships coalescing around joint initiatives to address real problems or opportunities.”

Of course, I was applying this logic to the work of business consultants, not open innovation, but the challenges are the same: overcoming spacial, temporal and organizational dispersion.  Most impersonal, technology enabled interactions do an imperfect job of overcoming these challenges.  Newer technologies, such as TelePrsence and enterprise microblogging, hold the promise of promoting for more trust, familiarity and social capital to the benefit of innovation.

Turns out, I wasn’t the only one thinking about the shortcomings of existing knowledge management systems at the time I started my little initiative.  In another, earlier article that I discovered from my favorite HBR Blog feature (it recommends similar blog entries as you scroll down to the end of the one you are currently reading), Hagel and Seely Brown write:

“Knowledge management traditionally has focused on capturing knowledge that already exists within the firm — its systems rarely extend beyond the boundaries of the enterprise.  Creation spaces instead focus on mobilizing and focusing participants across all institutional boundaries. Sure, there are lots of smart people within your enterprise, but imagine the power of connecting with and engaging a more diverse collection of smart people beyond your enterprise.”

Social networking technologies might be considered an enabler or constituent of the amorphous concept Hagel and Seely Brown refer to as creation spaces.  The improvement they offer to traditional knowledge management is the ability to identify and direct emergent information flows.  Companies like Box that are seeking to disrupt the enterprise knowledge management space, might take note and seek to develop their own social functionality while partnering with other social business software companies to find new ways of turning tacit, emergent knowledge more easily and seamlessly into explicit, codified knowledge.

Indeed, I think companies like Yammer actually need these partnerships more than the makers of knowledge management systems themselves.  Yammer’s viral strategy is good for adoption in the sense of signing up new users, but that definition of adoption paints the wrong picture.  What matters is that people are returning to the tools and using them with frequency.  That’s real adoption.  I suspect (largely on anecdotal evidence) that Yammer adoption by that definition looks very different.  Right now, Yammer is a solution in search of a problem.  To drive real adoption it needs to solve an acute pain point or improve a business critical function, to produce quantifiable impacts on financial and operations metrics.

For my own initiative, we sought to improve the staffing process, something central to every business consulting firm.  The rationale was that participation would be assumed since every consultant either needs to be staffed on a project or needs to do the actual staffing, and the value of finding the best person for each project faster was clear – for both our firm and our clients.  To measure impact, we pitched a Lean Six Sigma project using the familiar DMAIC framework.  It is only by driving true adoption and accomplishing these initial measures of success that we can hope to attain some of the larger goals and bigger prizes, such as those derived from the collaboration curve that Hagel and Seely Brown described (see, I told you I liked that HBR feature).

On a final note, I must acknowledge that I have been a reader of John Hagel’s blog for some time.  So before writing this entry, it occurred to me that maybe some of his thinking had subtly influenced my own, making it a little less original, but isn’t that how innovation really happens anyway?  It’s an outcome of cross-pollination and the slow hunch, and my excitement at having found my own ideas echoed by a thought leader – perhaps in greater detail and with more eloquence – is diminished none as a result.

To find out more about how I made the case for my skunkwork just a few short months before these HBR blog posts appeared, check out this selection of slides from the deck I created to solicit more development resources.  It’s been “sanitized” to remove anything that might be misconstrued as proprietary, confidential or competitively sensitive.  Consultants communicate in slide decks so the medium chosen was intended to fit the audience.

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