nGenera Insight invites you to participate in a new survey designed to gather information on platform usage, impact, and challenges. The survey is open to all levels and functions—if you are reading this we would love to hear from you! It only takes about 7 to 10 minutes and as a thank you for your time we will provide you with a summary of the findings.

The survey is designed to gather information and help answer questions like:

• What popular platform technologies are being used for business purposes?
• What progress is being made on creating customer, as well as internal, communities?
• What are the major enterprise challenges faced by those using collaborative platforms?
• How do business leaders view the future of collaborative platforms and their long-term impact?

To participate, please click on one of the links above or simply click here. To learn more about why platforms are important and what makes them successful, read Nick Vitalari’s posts Apple and the Rise of Competitive Business Platforms – What Other Companies Must Know and 12 Critical Success Factors for Business Platforms.

Thank you in advance for your participation, we look forward to sharing our results with you.

For every Like that is made, Facebook is able to correspond a product affiliation to demographic information such as sex, age, geography, and education, as well as social graph data about relationships and influence within a group. With Places, Facebook can even correlate product activity to mobile location data. If mobile payments ever take off, they could get actual sales data as well.

Ad Age recently asked the very poignant question: What Happens When Facebook Trumps Your Brand Site? (alternate title for the article is: How Facebook Became the Biggest CRM Provider). The online article was accompanied by the following graphic showing the top ten brands on Facebook (in terms of total Likes):

Top brands are garnering millions of Likes, yet only driving a couple hundred thousand visitors per year to their branded sites. What this all means is that Facebook has better data about customers than most consumer products companies do. As Ad Age notes:

For many marketers, their Facebook fan bases have become their largest web presence, outstripping brand sites or e-mail programs either because a brand’s traditional web-based “owned media” is atrophying or because more consumers are migrating to social media.

While fan pages may work a lot like a marketer’s traditional “owned media,” they’re not actually owned by the marketers. Facebook hosts the pages and provides analytics for free, but growing marketer dependency on the network for CRM programs, combined with simultaneous declines in traffic for many of their own brand websites, could give Facebook a valuable revenue opportunity.

Of course, it would be difficult to sell granular individual data about users (people would object); however, Facebook could sell aggregate data (trend analysis and market research) and act as a “black box” CRM (Customer Relationship Management) solution whereby companies offer targeted promotions and messaging to individuals with select profile characteristics, mediated through Facebook. Already some companies are using basic Like data to hone their retail strategies. In one example, Urban Outfitters is arranging clothing in its online store based on Like activity and offering select promotions to all those who have liked products. Additionally, Facebook is making information about the Like activity on ads (i.e. “social context” data) available to advertisers on its site. Armed with this data, advertisers can decide to further optimize campaigns by targeting people who have expressed a Like for the ad.

With the Like button, Facebook is benefiting from the power of weak tie relationships (Facebook calls it “lightweight sharing”). Many markets point to the fact that people that Like a product aren’t real fans or brand advocates in the traditional sense. This is s feature, not a bug. By lowering the bar for Liking something, Facebook has opened a channel to—and is gathering data about—ordinary consumers of the brand who otherwise would have no formal connection to the company or its products other than isolated, anonymous purchases. This connection can be potentially valuable in terms of loyalty programs and promotions, market research, and customer support.

A number of factors suggest that the number of Likes will probably continue to grow, including: the continuing growth of the Facebook user base (see chart above, which shows no indication of plateau), expansion in global markets (70% of Facebook users are outside the U.S.), the recent proliferation of the Like button on a range of products and services (the Like button is now on over 350,000 sites), and the growing use of mobile technologies that allow users to Like physical products and experiences. With this in mind, it’s by no means hyperbolic to think that Facebook could be the largest single CRM provider in the world.

“The control the Web took from the vertically integrated, top-down media world can, with a little rethinking of the nature and the use of the Internet, be taken back.”

The main way we’re seeing this happen is through pay walls and locked down services, mostly in the form of apps and specialty devices that emphasize convenience over control. Most users aren’t savvy enough to dig into the nuts and bolts of technology—geeky techno-details be damned, they want what they want, immediately. Unfortunately, the consequence of this apathy towards technology is a future where a select few companies will control a significant portion of the content we consume. Apple would be the key culprit here with sleek must-have devices that, although tremendously well-designed (full disclosure: I own an iPad myself), lock users into a convenient, ‘black box’ mentality of computing.  Powered by iTunes, the App Store, and iDevices Apple controls the flow of content (the new TV network), monetizes the media you consume (the new record company and music store), has final say over which apps you can use on your devices (a new software monopoly), and controls the end user experience via extremely inflexible devices (recall Ma Bell owned all the actual telephones at one point as well).

Mobility is also a big factor. With more people creating and accessing data via mobile devices (e.g. smart phones, Kindles, and iPod Touches), we see more niche uses of the net that don’t include browsing and the open distribution of content. As the Wired article notes:

“Within five years, Morgan Stanley projects, the number of users accessing the Net from mobile devices will surpass the number who access it from PCs. Because the screens are smaller, such mobile traffic tends to be driven by specialty software, mostly apps, designed for a single purpose. For the sake of the optimized experience on mobile devices, users forgo the general-purpose browser. They use the Net, but not the Web. Fast beats flexible.”

The emphasis on convenience over control has built other media empires as well, including services that use templated experiences to simplify the web (think web presences on Facebook or even Blogger, as opposed to sites created by individuals and designers). Author and web pioneer Jaron Lanier derides many such efforts as dehumanizing and anti-intellectual, and cautions us against lock-in to design principles that were conceived by those more interested in advertising and data aggregation than people and intellectual property. His recent book, “You Are Not a Gadget” serves a manifesto for those unhappy with the current direction of most web 2.0 initiatives. He notes,

“Lock-in removes design options based on what is easiest to program, what is politically feasible, what is fashionable, or what is created by chance.”

It’s nice to think that lock-in and disempowerment is happening to help consumers and create better experiences, but it is also happening because it’s profitable. The ‘ease of access’ versus ‘freedom’ argument is a false dichotomy; you can have both, it’s just more work and more costly. But, it can (and should) be done. However, for companies, it’s easier to cite reliability and security concerns and far more profitable to keep things locked down. Lock-in allows for monetization via proprietary formats, advertizing, and device replacement. In a poignant, yet fairly targeted jab at Google, Lanier goes on to say:

“If you want to know what’s really going on in a society or ideology, follow the money. If money is flowing to advertising instead of musicians, journalists, and artists, then a society is more concerned with manipulation than truth or beauty.”

Herein lies the problem. Apple is creating its own walled garden, but has effectively created a way to monetize content and distribute money to artists and application creators. Google on the other hand has taken a much more open approach, but monetizes content via advertizing, which is not distributed to content creators. In both cases, the individual consumer feels cheated.

I wrote about much of this before in a Wikinomics post about Jonathan Zitrain’s book “The Future of the Internet and How to Stop It.” The main issue: locked down appliances like the iPhone that could eliminate the PC, and with it the “test bed and distribution point of new, useful software from any corner of the globe,” and “the safety valve that keeps those information appliances honest.” The move towards appliances also dumbs-down the user experiences. When appliances break you don’t open them up yourself to fix them, you call the manufacturer. This is exactly how the Apple approach varies from the PC approach. The internet dystopia that Zittrain feared could be upon us, and most users (even tech savvy ones) don’t even perceive this as an issue. As one commenter to my Zittrain post mentioned:

“The death of the PC should not be an issue. It’s like caring about the death of the CD, who cares, something better has replaced it. There will probably always be PC’s for those who prefer optimal performance in certain hardware and want large visual displays. But the majority of the population makes a waste of all that good hardware just by only using a PC to go on Facebook or chat with friends. Let them have their mobile devices and gaming consoles.”

Ah, but PCs do matter and here’s why: Without PCs we lose control over how we experience media. Media that is streamed at us through apps and gamining consoles treats us as passive recipients in a similar way that TV or radio did; it hosts purely sanitized content, it is controlled by companies not individuals, and it’s infused with advertising. The reason we don’t notice (or don’t care) is that it is social and so gives us the perception of control and creation. But what we perceive as control is data entry into predefined fields and forms that limit our expression. We need PCs to truly create new content.

Social media has debased intellectual engagement and self representation by making it effort-free. The cognitive load required to type something on Facebook, comment on a blog post, or even post a video on YouTube is small because these sites are designed to mimic a stream of consciousness. Although little creative energy is expended to interact, time is still spent, and information is still created and consumed. Much of the content includes off-the-cuff remarks that would traditionally have dissolved the way idle chatter does; however, repurposed using social media they are often compared in the same light as actual article writing or high-quality productions. Most apps don’t encourage the thoughtful creation of content, whereas using a full blown desktop is all about creative freedom. In a fairly balanced response to the Wired piece, Shane Tilton from the Center for Society and Cyberstudies Journal says:

“Creation vs consumption: Most of the promoters of the death of the open web are looking at it from the viewpoint that we go online to get our information, check in with our friends and maybe post a picture or video. If this was the case, the closed web would have won years ago. However, we like the ability to do create works from time to time and love having a way to share it with a larger community. The app based system of uploading content is relatively simple, which is the good and the bad point about the system. You can share content as it is in the real world, however it is moderately hard to edit it and add a creative mark to the content. An open web system gives access to online editors and content creation tools. The close system, for the most part, lacks these qualities.”

The multi-billion dollar question is, are locked-down devices, apps, and internet pay walls the future? Or, will it be open devices powered by the likes of Android and Symbian, and supplied with open content via web search and peer-to-peer networks? As long as the web is free and open and accessible to all, companies like Google can index it and derive value from it. And, with the launch of Google Instant, it seems that Google is trying to move people away from integrated search bars back to the home page (where it can better monetize its ads). Whether it’s Google’s web or Apple’s apps, one thing is clear, the next phase of the internet will be monetized by a few key players and far less free than it used to be.

Now, Cataphora is in the business of modeling “informal networks” within the enterprise for HR and operational efficiencies, as well as to monitor compliance with internal policies and external regulations. By analyzing the relationship between e-mail data, documents that are shared, calendar information, call logs, and people, Cataphora can assess employee productivity, uncover shadow networks, and map collaborative behavior. Digital Mirror offers some of these capabilities to the public for free by analyzing your archived data from Microsoft Outlook.

I ran Digital Mirror on my own Outlook data and came up with some pretty interesting results. A caveat I would add is that you need to have a lot of archived data for this to work well—several outputs such as “Blow-Off Scorecard,” “Buck-Passing,” “Temperature Gauge,” and “Loud Talking” didn’t work for me due to lack of sufficient data. Some of the other interesting outputs that did work are shown below:

Who have you spent quality time with?

Who have you talked with, about what, and when?

Who has been stressed out, and about what?

The goal of Digital Mirror in its current incarnation is to illuminate relationships, topics of interest, tensions, and hidden processes in the workplace. I think over time, digital reflections and analytics such as these will become increasingly important and, in many cases, baked into our both our personal computing as well as corporate processes.

As more data (beyond simply data from Outlook) is incorporated, I can imagine much richer, higher-definition mirrors. Aspects of our rich digital selves that are open for analysis include things like education; employment, and resumes; health records and government documents; search history; profiles on social networks; comments and posts on forums and blogs; and location information from cell phone signals and GPS-enabled devices. They could include aspects that we actively update like registrations for groups, associations, and publications, or aspects that we are not aware of like un-tagged photos of us on other people’s Facebook or Flickr profiles and images from closed-loop IP-enabled surveillance cameras.

Today most of this information is disassociated, residing in many different databases and in many different organizations. More often than not, the information is not under the control of the individual. In the future, we can envision a composite digital picture of the individual that will augment and accompany each human from cradle to grave. As the world becomes more instrumented, multiple machines—some under our control and others not—will be slicing our data and making observations about our activities billions of times each minute, in parallel.

Beyond optimizing processes and sparking what is likely to be heated debate about privacy and data ownership, digital reflections will also help people understand how they are perceived by others. With this knowledge in hand, we can go forth in the online (and offline) world making conscious decisions about how we want to represent ourselves in different contexts. Most people don’t step out into the real world in the morning without—at least briefly—consulting a mirror. Why should the online world be any different?

Interestingly, the same could be said for many of the platform design projects currently underway in the Enterprise 2.0 space. In many cases, IT teams are designing and implementing collaborative software without the benefit of collaboration. Yet, collaborative business platforms suffer from many of the same challenges as sustainable building projects. They try to optimize for interactions across complex business ecosystems, usability of digital tools, and aesthetics. They also involve multiple stakeholders and risk costly lock-in if poor architectural or design choices are made early in development. I’m convinced design charrettes can enhance the performance of platform projects as well.

Here’s how it works. In a typical project, as time goes on, the ability to make changes that greatly impact resource allocation and design diminishes. At the same time, the cost of implementing changes increases as successive design choices create inflexibility and lock-in. The effort consumed by stakeholders and the allocation of resources typically follows a bell curve, so a large portion of the project unfortunately takes place during a time of diminishing impact and rising costs. Moreover, if you don’t get people involved early you tend to have a long tail of resource expenditure on after-the-fact customizations, modifications, and revisions.

In a design charrettes, the idea is to shift the project curve to the left so that more time and resources are devoted to planning and a significant portion of the decision-making occurs when the impact of changes are high and the cost of implementing changes is relatively low. Ideally, at the end of the project, the need for modifications would much lower since input from relevant stakeholders was baked into the original design. Since I’m a pretty visual learner, creating the graphs helped me understand how beneficial this approach can be.*

One of the arguments against charrettes is that they result in “design-by-committee” outcomes (usually meant as a derogatory statement, invoking images of the Homer car, designed for the “average” American) and design delays brought on by conflicting egos. In fact, we’ve seen some leading examples of where design-by-committee works great, including Local Motors and CrowdSpring. A good, recent post advocating for design charrettes is “A Camel designed by committee is a camel,” by LEED architect Rob Fleming, where he argues that, given the current state of the World, what we need is design-by-committee “camels,” not “race horses” by impresario architects. In terms of managing conflict, independent, third-party moderators and mediators also play an important role in steering collaboration for productive charrettes.

* Kudos to Jeff Ranson for his leadership in the area of design charrettes and his back-of-the napkin graphs that helped inspire this post.

In some cases, there may be benefits such as using an iPhone for interactive kid’s games, or using cell phones to keep track of older children on-the-go. In other cases, the activities raise new issues and question about appropriateness such as creating digital identities for children the moment they are born, or using Google and other online sites to diagnose children and chronicle their development. The New York Times last week published an article titled “R U Here Mom?” or “The Risks of Parenting While Plugged In” for the online edition. The article profiles work done by child development researchers looking at how parental addiction to technology affects communication with children and early childhood learning. Sherry Turkle, director of the MIT Initiative on Technology and Self has done extensive research on the topic for over five years, including 300 interviews. As quoted in the NYT article, she says:

“Over and over, kids raised the same three examples of feeling hurt and not wanting to show it when their mom or dad would be on their devices instead of paying attention to them: at meals, during pickup after either school or an extracurricular activity, and during sports events.

There’s something that’s so engrossing about the kind of interactions people do with screens that they wall out the world. I’ve talked to children who try to get their parents to stop texting while driving and they get resistance, ‘Oh, just one, just one more quick one, honey.’ It’s like ‘one more drink.’ ”

(Additional insights can be found in the comments section of the article where Dr. Turkle is very active.)

The NYT article also profiles an informal test conducted by Dr. Dana Suskind from the University of Chicago which looked at the effect of smart phone use on verbal interactions between parents and children. In most case, verbal communication dropped when devices were present. This is an important indicator because verbal communication is seen as a key indicator of how well children develop language skills and vocabulary.

Source: New York Times, June 9, 2010

As we look to the future generation of parents, the trend is a bit troubling. And, lest I get accused of throwing rocks at glass houses, I will admit my own faults as well: While certainly not the worst offender, I count myself among those guilty parents that sometimes tune-out to technology.

My last post was about social media addiction and highlighted how young people, as well as older people exhibit signs of technology addiction, including messaging during meals (49% for those under 25), while in the bathroom (24%), or even during sex (11%). “While feeding my child,” “while my child plays,” or “while taking a child to daycare” were not options in the Retrevo survey, but I’m sure there would be a substantial percentage of those people as well. I would also expect the numbers to get higher in the future. A recent report from Pew Internet and American Life Project found that one-in-three American teens sends in excess of 100 text messages per day (more than 3,000 per month). The typical (median) teen sends and receives about 50 text messages a day (30 per day for boys and 80 per day for girls), although the average (mean) is much higher at 112 messages per day. A quarter of all American teens ages 16-17 text while driving. And while texting is certainly the worst offender with respect to device-immersion, other activities are also contributing to teen technology use for communication versus face-to-face communication.

 Do we expect this type of device-oriented behaviour to stop in adulthood? Or, left unchecked, will it simply get worse with added work-related responsibilities (i.e. the “Crackberry” trap) and the proliferation of screens, communication channels, entertainment gadgets, and social media?

 What are the signs of addiction? According to Mayo Clinic, “As your drug use increases, you may find that it becomes increasingly difficult to go without the drug. Stopping may cause intense cravings and make you feel physically ill (withdrawal symptoms).” I’m guessing that includes ‘taking’ fist thing in the morning, or even in the middle of the night.

In fact, nGenera’s Net Generation study from a couple of years ago backs up what the Retrevo study is implying. We asked teenagers to depict visually how they would feel if technology was taken away from them for a month. The results are what you might expect. If not addicted, young people (and I imagine people of all ages) are certainly dependent on technology for entertainment, inclusion, belonging, and sense-making:

In a related study on parenting and social media, Retrevo finds that the vast majority of parents—over 70%—allow their kids to text during family meals. Personally, this is a problem for me, but then again I came from a generation of kids that was told to ignore the phone (the corded one attached to the wall) during mealtime and ask to be excused from the table after dinner. Still, if you think social media addiction is bad now, this type of behaviour means it’s probably just going to get worse from here on in.

For more stats and info-graphics, check out Retrevo Gadgetology and Retrevo Pulse.

In 2007, nGenera Insight began writing about what we call “hothouse innovators”—a new breed of global enterprises in Asia, South America, and Eastern Europe that are being built under fertile conditions for accelerated growth, including: A vast pool of low-cost, and increasingly highly-skilled labor; a rapidly growing group of wage-earning domestic customers with few preexisting expectations or brand loyalties; active government involvement in the private sector; and greenfield IT infrastructures that lack legacy complexities.  By exploiting these conditions, global hothouse innovators are developing business models that allow them to move up the value chain, compete with firms in mature markets, and threaten the profit structure of incumbents in almost every industry.

The Economist article touches on all of these elements, but expands the argument by introducing additional factors worth discussing. Specifically:

• Superior positioning for M&A: Rapid growth has made resulted in cash-rich enterprises with access to highly-developed, public and private capital markets. Additionally, large conglomerates benefit from consolidated ownership which helps diversify risk and increase flexibility. The combination of these two forces is allowing large emerging market companies to use M&A not only to reduce cost or achieve economies of scale (which they have in many cases), but rather as a way to acquire name brand recognition, skilled workers, and global distribution channels.
• “Frugal” innovation: We tend to think of innovation as “more bells as whistles,” but this mentality is base on the notion that consumers are going to shell-out more money to get the latest-and-greatest gadgets. In contrast, hothouse innovators are dealing with a population with highly-constrained budgets. In many cases, innovation is aimed at the bottom of the pyramid and focused not on growing wallet-share but rather turning non-consumers into first-time consumers. With low margins, the emphasis is on volume and utility; “new and improved” really means “simpler and cheaper,” as well as “tough and easy-to-use.”
• New Western investment: Think of it as fertilizer for the hothouse. Companies like Cisco, General Electric, Microsoft and many others are investing heavily in emerging markets, and China and India specifically. As The Economist report notes, “Companies in the Fortune 500 list have 98 R&D facilities in China and 63 in India.”

To understand how truly competitive some of these emerging companies are, it helps to study some leading examples. Consider the following, pulled from nGenera’s previous research on the subject:

• Foxconn: Taiwan’s Foxconn, a once lowly parts manufacturer, is challenging the traditional value chain model by designing and assembling entire products¾often leveraging new innovations and intellectual property to achieve this. In 2007, Foxconn earned $2.6 billion on $38 billion of revenue; its top five competitors combined lost $1.6 billion on sales of $57 billion. Foxconn’s success was bolstered by a record high 81,820 patent applications in 2007; 49,007 of which were granted. By comparison, IBM – the leader in US patent applications –at the time held approximately 40,000 active patents worldwide (out of over 2 million granted since 1974).
• ICICI Bank: In less than 10 years, and from a standing start, India’s ICICI Bank ICICI has become India’s second largest retail bank, leading in every retail product market that it targets. Using e-lobbies and customer self-service, it drives over 70% of its transaction volume through electronic channels in a country where Internet and mobile phone penetration are below five percent. The company’s IT systems—which are generally based on servers instead of mainframes—are free from complex legacy issues and cost less than one-tenth of developed-country benchmarks. Moreover, the bank operates on 90-day business plans and established its UK subsidiary in only 65 days.
• Tencent: Although few people outside of China have heard of it, Tencent QQ, China’s premier integrated platform for social networking, media, and mobile gaming currently boasts 400 million active users (the same as Facebook) and 2009 annual revenues of over USD $1.8 billion (more than double the between $600 and $700 million 2009 revenue estimated for Facebook). Perhaps the most remarkable figure around Tencent’s financial model is that over 75 percent of revenues stem from value-added services (paid for by users), not advertising. The company’s virtual currency, Q-Coin, which is used to purchase products (such as shows, online “pets,” games, and music) has become so widely accepted that it is also being used to purchase physical goods from other online retailers.

But opportunity is not without risk. These waters be uncharted and filled with pirates. As The Economist notes:

“These markets are among the toughest in the world. Distribution systems can be hopeless. Income streams can be unpredictable. Pollution can be lung-searing. Governments can be infuriating, sometimes meddling and sometimes failing to provide basic services. Pirating can squeeze profit margins. And poverty is ubiquitous. The islands of success are surrounded by a sea of problems, which have defeated some doughty companies. […] This combination of challenges and opportunities is producing a fizzing cocktail of creativity. Because so many consumers are poor, companies have to go for volume. But because piracy is so commonplace, they also have to keep upgrading their products.”

What this means is that not only are emerging market companies benefiting from hothouse conditions, but they are also operating in markets that pose fairly substantial barriers to entry for Western firms. At a macro level, what we’re seeing is not only a new wave of innovation, but also a global redistribution of wealth. The Economist says, “The emerging world is enjoying the most spectacular growth in history. Its share of global GDP (at purchasing-power parity) increased from 36% in 1980 to 45% in 2008 and looks set to grow to 51% in 2014.” Another source, Worldmapper, provides a visual representation of how this might play out:

World wealth: 1990 to 2015
(map area shows relative wealth)

So, is the West doomed? Not by a long shot. But, we probably have a lot to learn from what’s going on in emerging markets. Established companies have to rethink innovation in a way that focuses equally on features and branding, as well as cost, simplicity, volume, distribution, durability, and accessibility. Enterprises can also develop strategies for open innovation that broaden the scope for disruptive ideas and increase the potential for high-yield opportunities.

“Open data unleashes the creative potential of citizens and private enterprise to create new services, software applications, and insights that the government cannot do by itself. The shear numbers tell the story. Millions of citizens and hundreds of thousands of companies of all sizes uniting to independently create value and enhance the common good.”

This is exactly what you are seeing below. Bachman breaks down the 2011 Federal budget in a surprisingly simple graphic, showing total spend per category, percent change, and size relative to other spending priorities (click the image for the interactive chart).

Admittedly, I’m a little late to the party on this one. Bachman has being doing this since 2004 and has been featured in numerous publications as well as on national television. Still, if you’re like me and haven’t had a chance to check it out, definitely set aside some time to do so. There’s a new graphic every year, so even if you saw the chart a couple of years ago, it’s probably worth a revisit. This will be particularly relevant for U.S. readers who can calculate where their tax dollars are going at a fairly granular level. For example, in 2011, the average American tax payer will give $3,565 to the Department of Defense, $249 to the Department of Education, $218 to Homeland Security, $93 to NASA, $53 to the Environmental Protection Agency, $35 to Nuclear Weapons Activities, $27 to financing foreign militaries in Israel and Egypt, $22 to Postal Service, $14 to National Parks, $12 to HIV/AIDS, $6 to counter-insurgency in Pakistan, and $2.21 to Obama (Executive Office of the President). As Bachman notes:

“Thousands of pages of raw data have been boiled down to one poster that provides the most open and accessible record of our nations’ spending you will ever find. If you pay taxes, then you have paid for a small part of everything in the poster. ‘Death and Taxes’ is an essential poster for any responsible citizen or information junkie.”

The next order of sophistication for something like this would be an interactive budget chart a la Freiburg model, where citizens could use the visualization to propose their own balanced budgets as a way to provide feedback about their priorities. Imagine sliders that would allow you to ratchet-up or ratchet-down the relative size of spending categories.  These individual budgets could then be aggregated into a ‘citizen budget’ that would go beyond simple ‘suggestion box’ initiatives to provide a truly useful piece of information for policy makers.  I’m guessing a collaborative citizen budget would look a lot different than the one shown above.

OKCupid gathers personal information based on community-submitted questions that users answer. This allows the company to better match couples based on the unique values of each person. Their slogan is: “We do math to get you dates,” which includes going so far as to create decision charts that visualize the formulas leading to love (or at least a date) for various individuals. With their data, OKCupid reveals information about the effectiveness of various romantic approaches, male and female attitudes and biases, insights on what behaviors result in conversations, behavioral changes based on age, and a variety of other findings.

From a research perspective, OKCupid is a fascinating subject. I’ve references them before in a post about labor incentives. Today’s post is about the potential for data-enabled business models and new markets for user data. Even more insightful than some of the racier findings from OKCupid (such as the sexual appetite of the average 40-year-old Floridian woman), user activity on this site generates a tremendous amount of data that extends beyond the realm of dating and could be useful to other groups and industries.

For example, OKCupid is able to generate detailed demographic and geographic data about political views, social issues, and public opinion on issues ranging from contraception to First Amendment rights to acceptable means of protest.

What OKCupid is doing not unique—often the collection of data can yield new insights and provide additional contexts beyond its intended purpose. As more and more customer and user processes become digitized, what we’re going to see over the next few years will be the growth of data-driven strategies that gather, interpret, and present data for new uses and new audiences. The abundance of data and relative scarcity of reliable sense-making information will create a flourishing market for data and analytics. In a recent nGenera survey we found that already over 40% of respondents say that data from external sources leads to competitive advantage.

Two years ago I wrote about how the idea that online social networks will make money selling eyeballs (advertising) or products is missing the entire value proposition of a social network. The real opportunity is in harnessing the rich data that is created by those participating in conversations and interacting with each other. Companies that have social platforms are increasingly seeing a business model around providing free services and aggregating anonymized customer and user data for sale.

OKCupid has a very open approach to data, but it’s easy to imagine a variety of groups—lobbyists, politicians, economists, sociologists, and so on—that might be interested enough in this type of information to pay for it, especially if presented in interactive charts that let the user filter based on factors such as age, race, gender, employment, and so on. If you think about the possibilities available when data extends beyond the realm of online dating, you see that companies in a variety of industries could use customer-generated interaction and polling data to gain a deep understanding of what drives purchasing behavior, brand loyalty, and even the desire for new products.

But a much bigger gap exists, often unnoticed by companies: The amount of money contact centers and support organizations spend on social media is nominal compared to the percentage of customers’ queries that hit these emerging channels. This is the “support gap.”

Social media has become a new service and support channel that customers employ to raise concerns about products and services, describe their experiences, seek help from others, post new product insights, and consult for advice on features and functions. For companies, this is not trivial. It means that conversations about your products that would traditionally have occurred in your customer contact center are now occurring in public places. The Consortium for Service Innovation estimates that fully 90% of customer conversations about a company never touch the organization. What’s more, a mere 1% of all customer conversations are assimilated as organizational knowledge.

nGenera’s own Customer Interaction Management solution recently added social media support for its contact center product, but many companies have yet to consider this option. In most cases, if a social media strategy is being implemented by the support organization, it’s on an ad hoc basis with a few employees manually monitoring Twitter and branded Web spaces and responding to customers where appropriate. These interactions, though helpful, are not strategic in that they are not integrated with enterprise systems or contact center processes. Building processes and accountability around these activities is the first step that companies can take today—connecting to CRM systems in a robust and meaningful way will be the next horizon.

The plight of marketing and the plight of the support organization are linked and the two organizations need to work together, using shared information, on a common platform. Traditionally, marketing has been about communicating brand messaging, while the customer service department deals with problems and complaints. But a customer that you’ve engaged through social media for marketing purposes doesn’t see the separation – and customer service is becoming a key aspect of managing customer relationships online (I’ve talked before about how “customer service is the new marketing“). This is not trivial. It means matching the tremendous amount of time and energy spent on other official support channels such as e-mail and phone in order to meet customer expectations for social media and deliver a consistent and authentic customer experience. Those companies that think social media is just a cost-effective way to get the message out are in for a surprise. By marketing on social media you inadvertently open a new support channel as well—get ready!

A blues or jazz band—or any ‘jam band’ for that matter—operates using many of the design principles we’d like to see from a collaborative enterprise. Unlike an orchestra, a band is much more fluid in their interpretation of the music. They are able to improvise on the spot, blend sounds, and often play to the mood of the audience. In other words, they innovate, create mash-ups, and are responsive to users.

I’m not the first person to use the band analogy. Barry Rabkin of the Market Insight Group asks whether technology analyst firms are more like a jazz band or symphony orchestra. He alludes to the fact that the jazz band style is more agile and responsive to customer demands—another important outcome of collaboration:

“Another area where jazz musicians differ from their symphonic counterparts is that jazz musicians, sensing their audience, can and do take liberties with new selections not identified during their rehearsals. They can do this because they have a broad library of music and musical explorations in their knowledge set and, as importantly, they know how to blend their sounds together to get the best outcome possible for their audience.”

What’s more, in a symphony orchestra the conductor alone is responsible for guiding the entire team, whereas with a distributed, ad-libbing crew, anyone can start pushing with a new riff or mood and the others will follow suit. In this way, the benefit of each player’s perspective and expertise is baked into the model.

One of the factors that allows a band to operate in this manner is the existence of very well defined roles (i.e. guitarist, vocalist, drummer, base, keyboards, etc.) and somewhat open tasks (i.e. what songs to play, when to riff, what chords to use, etc.). This is another important learning for the enterprise. As Lynda Gratton and Tammy Erickson note in the HBR article Eight Ways to Build Collaborative Teams:

“Cooperation increases when the roles of individual team members are sharply defined yet the team is given latitude on how to achieve the task. [...]Assign distinct roles so team members can do their work independently. They’ll spend less time negotiating responsibilities or protecting turf. But leave the path to achieving the team’s goal somewhat ambiguous. Lacking well-defined tasks, members are more likely to invest time and energy collaborating.”

In addition to looking at how bands are structured, we might also consider how band members collect largely unstructured customer experience ‘metrics’ in real time and use the feedback to adjust their approach. These metrics provide a useful analogy for the type of approaches leading companies should take when developing customer strategies, including:

• The applause of the crowd: What kind of noise are customers and prospects making online and in social media channels? Using sentiment analysis companies can find out if it is positive (cheers) or negative (boos) and change their tune accordingly.
• Number of people dancing: How engaged is your audience? Metrics might be based on active participation on forums, comments online, rating of content, and re-broadcasting of brand messages, or more passive (i.e. head bobbing) activities such as subscribing to feeds, friending, and following.
• Song requests: What kinds of requests are coming into your contact center and support organization? In many organizations, the contact center is an untapped wealth of customer feedback, largely ignored by groups like marketing and product development. Listening to this channel and other prosumer input can lead to dramatically improved customer experience.
• Duration of stay in the bar: How long do customers hang out in your online properties? Using Web analytics, companies can now obtain this information, as well as data about how people got there, what path they take along the way, and how influential various ‘promoters’ are at bringing in prospects.
• CD and merchandise sales: How are Web interactions translating into sales? The performance is about creating an experience, but ultimately, in order to be profitable, you need people to buy your stuff.

As companies continue to seek best practices and metrics for collaboration, I firmly believe that some of the more innovative solutions will come from non-traditional fields that have deep roots in collaboration, but that have eluded formal study and analysis. (If I’ve managed to spark an interest in enterprise lessons in collaboration from other disciplines, also see my previous post on Measuring collaboration: Lessons from Shane Battier and the NBA and the related Collaboration box score.)

When we study prosumers, one of the reoccurring themes is how to create incentives to get people to contribute their valuable time to an initiative. Increasingly, consumers are challenging the notion that the Internet is recruiting ground for free labor that will willingly engage with your brand, contribute ideas, and co-innovate with you—consumers want some sort of value in exchange for their time. Contrary to what is being proclaimed in popular social media echo chambers, most consumers actually don’t want to co-create with companies; the vast majority of Internet users are happy to be passive consumers and observers, with only a small fraction opting for prosumerism.

This brings be to the main point of this blog post, which is the notion of playbor. I first came across the term—a combination of ‘play’ and ‘labor’—on the Web site for a conference on digital labor hosted by The New School in New York. The Internet as Playground and Factory notes that, “Today, communication is a mode of social production facilitated by new capitalist imperatives and it has become increasingly difficult to distinguish between play, consumption and production, life and work, labor and non-labor.” The simple idea driving the playbor discussion: What happens when we collapse the often conflicting interests of work, personal ambitions, and entertainment into a single activity?

We already see examples of this happening on the Web. Consider Google’s Image Labeler, which creates a game out of the legitimate task of tagging and creating metadata for Web images. A less contentious example is Free Rice, which hosts a word game and has sponsors donate 10 grains of rice to the World Food Programme for every right answer submitted by players.

Recently, my colleague Jeff DeChambeau wrote a case study about the dating site OKCupid. Unlike eHarmony and other dating sites where the company determines the question set used for matching couples, OKCupid relies on questions submitted by its users. The notion that the users know best about what characteristics make a suitable mate makes sense. Why this is novel from a playbor perspective is that users, through actions that are apparently self-serving, are also contributing to the growth of the site and the effectiveness of its proprietary matching algorithms. What’s more, the actions of users create value for the company in the form of new data and analytic possibilities (for fun examples, see how OKCupid number crunchers use member data to determine what makes a good profile picture, or why dating an older woman might make sense).

For enterprises, these examples raise the question: How can we make work more interesting, more curious, and more playful so that users willingly play to create value? How can we align incentives in a way that lets us harness free labor? What is the appropriate division of labor across a diverse and fluid ecosystem that includes customers, prospects, partners, and competitors?

There are troubling consequences as well. How can consumers be sure of the authenticity of their experiences? Child labor is a discouraged practice, but what about video games that could be designed so that game-play elements actually contribute to the production of a commercial product like a new chip, program, or piece of software? As our environments become highly-instrumented with and capture data from our activities, how are users compensated for, or even made aware of the commercial value of their data? What does it mean for the broader economy when waged and unwaged labor collapse and are often indistinguishable? What does it mean for society when we debase the notion of pure, innocent play? The Internet as Playground and Factory has a great Vimeo page with clips from leading thinkers that are considering and debating these and other issues that arise from playbor. It’s a lot to digest, but this is a great starting point for people interested in the topic form a social studies perspective.

A recent article by Nate Anderson in Ars Technica highlights professor Mayer-Schönberger book, Delete: The Virtue of Forgetting in the Digital Age. The message: “Technology has now made ‘remembering’ the default approach to information, and in doing so, threatens to make ‘forgetfulness’ obsolete.” This is not only a profound change from 20 years ago, it can also be detrimental to our ability to think and analyze information. The article goes on to say: “Selective forgetfulness is a boon to humanity; it keeps us from drowning in our own recorded data. It allows us to sift and sort, then to think at a higher level of abstraction instead of wallowing in detail.”

But, this may all soon change.  Perhaps, computers can learn to forget too.

Researchers led by doctoral candidate Roxana Geambasu, at the University of Washington in Seattle are working on project called Vanish. The idea is to encapsulate data such as e-mails, selected text in messages, or documents that are sent over the Internet. The system would create corresponding keys for decapsulation that are widely available online, but that would deteriorate over time so that the data in readable form would only be available for a certain period of time. The overview page of the Vanish project states, “We strongly believe that realizing Vanish’s vision would represent a significant step toward achieving privacy in today’s unforgetful age.” Mayer-Schönberger suggests a similar solution that uses metadata to tag data objects with expiration dates and cites the work of Lawrence Lessig who has proposed a broader approach to combine policy and software to force privacy compliance.

nGenera’s research project Leading in an Age of Unbounded Data is looking at new sources of data available to the enterprise and how these will lead to new insights, opportunities, and challenges, as well as change enterprise processes and decision-making. One of the assumptions we make is that data will continue to grow and companies, through analytics, will develop a type of ‘sixth sense’ or situational awareness about the organization thanks to information captured from across the business ecosystem. We have already found that the growth of personal information and digital identity data will lead to rich digital profiles containing social graph information. These rich profiles present opportunities to better engage with customers and employees, improve customization, and facilitate knowledge management by anticipating user needs and connecting them to relevant people and information.

Projects like Vanish force us to think about data, not as an asset with an indefinite lifespan, but rather as something that depreciates over time, just like physical assets do. This would effectively reduce the amount of data that we need to manage and improve signal-to-noise ratio as more important facts and information would be retained while less significant information would be deleted. By eliminating the perfect memory of computers, we might also feel less pressure to maintain digital facades and manicure our online profiles. Additionally, the idea of adding expiration dates and metadata to data could accelerate the shift in power away from marketer towards consumer as it would allow individuals to dictate what personal data is used, who has access, for how long, and for what purpose.

But, self-destructing data would also diminish the value of many of the ‘big data’ opportunities that we talk about such as using large data sets to infer the truth about various situations, and using sentiment analysis to mine online customer comments and status updates for market research and product insights. It would confound companies and marketers that store petabytes of information to generate longitudinal trends and rely on usage data to drive Web analytics and build reputation and ratings, as well as improve information management through technologies such as collaborative filtering (e.g. the technology used by Amazon to recommend books to you based on the activity of people with similar behaviors). By collectively deleting our less-than-favorable digital trails, would we also be doing a disservice to future generations of anthropologists that could benefit from a complete digital history and behavior map—both good, bad, and questionable actions—of their ancestors?

The idea that all data should live on forever is a relatively new concept that many people have already taken for granted. In general, I think enterprises, governments, and individuals would benefit from more discussion on the topic instead of seeing it as a foregone conclusion. The idea of having an information lifecycle for all data is a powerful one. Personally, I would welcome more initiatives such as those by the Vanish team and professor Mayer-Schönberger that broach the topic and reintroduce a little forgetfulness into our digital lives.

When I originally started looking State of the Union addresses, I simply found transcripts online and did a manual count of words in text documents. This was laborious, but provided some interesting findings (note sites like Speech Wars can now automate this process). Last January I highlighted Wordle and used tag clouds to create a visualization of State of the Union addresses from notable past Presidents. This year, I’ve been spending a fair bit of time researching sentiment analysis, so I was pleasantly surprised to see that vendor Crimson Hexagon and CNN had teamed up to analyze public sentiment towards the 2010 State of the Union in real-time. Check out the video after the jump.

The impact of the new technology was not lost on the news media. The Huffington Post picked up the story and reported that, “The moment that ends up being most pivotal in changing the way the media covers big, live events may well have happened on CNN, where John King used the ‘Magic Wall’ to analyze almost 150,000 Twitter responses to President Obama’s speech.” In the article, CNN’s Senior Vice President and Washington Bureau Chief, David Bohrman is quoted as saying, “Twitter is all noise, but to be able to harness it and group it and actually intelligently cluster it and derive moods and opinions from it is very interesting.”

Whatever you might think of Twitter (Jon Stewart used the Magic Wall as an opportunity to make fun of both CNN and Twitter), this is exactly the type of technology companies are starting to think about for managing their brands, conduct market research, and pre-emptively deal with customer issues. The next level of granularity that sentiment analysis vendors are starting to offer is the ability to go beyond positive and negative sentiment to look at why sentiment is the way it is. Why are people pro-Obama? What types of issues are most often related to “Obama is too liberal?” This type of analysis is available, and I’ve seen demos from some vendors that offer fairly sophisticated drill-downs. However, some people remain sceptical about the general accuracy of this capability, as well as the limitations of most systems to crunch this type of data in real-time. Maybe we’ll see this for next year’s State of the Union—I’m hoping so.

Unlike information taxonomies that catalogue data or data security protocols that either allow access or deny it, context is dynamic; it changes. Advertisers have been thinking about this for many years. An ad for Bud Light Lime might not be all that relevant in the commuter newspaper, but it makes perfect sense behind a urinal in the men’s room of the pub, or on a billboard in cottage country. But that’s still a very 1.0 view of context. What’s missing is the granularity that takes this type of generic contextualizing (e.g. if you’re at the bathroom in a pub, you’ve probably been drinking beer; maybe you’ll like our beer) to a personalized one (e.g. we know you only drink at the pub after work, not during lunch, so at lunch we’ll offer an ad for coffee; after work, we know you’re favourite drink is gin and tonic, not beer, so we’ll suggest a new premium brand of gin).

While advertisers are leading the way, for most enterprises, this type of granular ‘what you need, when you need it’ approach to information is still far from reality. The good news is that the tools to sense and record context—rich user profiles, presence awareness, geolocation data, status updates, and lifestreaming information—are exploding all around us. You might think of much of this as information exhaust—the incidental, or ambient data that is created as a by-product of simply carrying on with our daily lives.

The anthropological view (circa 2007) of ‘exhaust data’ is that it has little information content, but lots of emotional and social content that contributes to identity, intimacy between individuals, and a deeper cultural understanding. However, as tireless machines work 24/7/365 to mine this exhaust data, the information content becomes apparent as well. The data will reveal important trends about individuals and their preferences, thus enabling context-aware machines to sense our needs and respond. What this means for enterprises is greater employee productivity as users spend less time looking for and filtering information, and better customer experiences as contextual information leads to greater customer intimacy and personalization. As Edo Segal notes on TechCrunch:

“These are streams of information bubbling up in realtime, which seek us out, surround us, and inform us. They are like a fireplace bathing us in ambient infoheat. I believe that users will not go to a page and type in a search in a search box. Rather the information will appear to them in an ambient way on a range of devices and through different experiences. [...] Humanity is constructing its own synthetic sixth sense. An ambient sense that perceives the context of your activity and augments your reality with related information and experiences. Increasingly, we will be sensing the world with this sixth sense and that will change the way we collectively experience the world.”

Indeed, Gartner believes context-aware computing will provide significant competitive advantage. I agree. The firm predicts that “By 2012, the typical Global 2000 company will be managing between two and 10 business relationships with context providers.” Technologists, enterprises, and academics are beginning to understand the importance of context and we’re starting to see products and services that reflect this. Consider the following examples:

• MIThril at MIT Media Lab is working on wearable computers that gather contextual information and provide feedback; projects include the Real-Time Context Engine and the Context Aware Cell Phone Project.
• Also from MIT, SixthSense by Pranav Mistry provides the “synthetic sixth sense” alluded to by Segal. This is some of the coolest technology I’ve seen in a while. For a demo, check out the TED Talk video below:

• Cisco Context-Aware Software is a mobile solution that integrates contextual information (including location, temperature, and availability of an asset) with business process applications.
  
• Marcopolo for MAC OS is an open-source, early example of context aware computing that triggers actions based on changes in location or activity.
• Research In Motion (RIM) appears to be pursuing context-aware security for the Blackberry. The company was granted a patent in August of 2009 for a mobile device that can change security settings based on its environment.
• Earthmine uses 3-D mapping to tag the physical world. Imagine having Terminator vision, but displayed on your iPhone. Tags, such as the ones shown in the picture below, could be customized to reflect any context.

 Feel free to share any other context-aware examples you know of.

The boon for small operatives and peer-to-peer commerce is obvious. It’s a technology that will enable what we at nGenera are calling “the new localism,” or the opposing force to globalization spurred by both a struggling economy that has encouraged protectionism and “buy local” campaigns (e.g. see 3/50 Project), as well as environmental pressures to reduce transportation-related waste and third-world exploitation by supporting local production (e.g. see 100 Mile Diet).

Using devices like Square, anyone—from street artist to backyard food grower—can be a legitimate vendor accepting credit card payments. According to Om Malik (who calls it the beginning of the iEconomy):

“This is truly disruptive. The reason Square exists is because of three macro trends: the pervasiveness of the mobile Internet, the increase in the use of electronic payment systems and most importantly, the availability of low-cost, always-on computers (aka smartphones) that allow sophisticated software to conduct complex tasks on the go.

The marriage of computing and connectivity without the shackles of being tethered to a location is one of the biggest disruptive forces of modern times. It is (and will continue) to redefine business models, for decades. Square is simply riding these waves.”

But competitors are quick to point out flaws in the Square model. VeriFone CEO Douglas Bergeron (VeriFone plans to launch its own offering, PayWare Mobile in 2010) believes “that encrypting data on the iPhone itself—instead of before the data is loaded to the device—presents a security risk. Beyond that, he’s wary of Square’s decision to have a merchant account for the company itself but not requiring individual businesses to have their own. Bergeron said VeriFone’s offering will require merchants to have separate accounts. ‘It would be like sharing bank accounts with your neighbor: It just doesn’t work,’ he said.”

The URL for Square is squareup.com, as in “square-up with friends over a dinner tab,” which opens one’s imagination to the multitude of scenarios where one might need to square-up a debt or pay for a service. Dorsey and team’s target market is small business owners frustrated with credit card payments.

But what of illegitimate businesses? I’m currently reading SuperFreakonomics, which deals largely with how incentives drive human behavior. The book got me thinking about some of the more subversive uses of such a device. Specifically, the chapter, How is a street prostitute like a department store Santa? describes Chicago prostitutes that charge between $300 and $500 per hour, which may not be the kind of cash you want to be carrying around in the type of neighborhoods that support that trade. Would it be reasonable for a John to ask, “Do you take Visa?” What about cell phone-enabled drug dealers? Illegal firearm sales? Bribes? Gambling debts? Black-market goods? I wonder what the receipts would say. Much like reports of prostitution on Craigslist, social media (software and hardware) appears to provide an upside for both the legitimate and the insidious.

Of course, cash carries its own benefits as well. Most notably, it cannot be traced—a characteristic that the underground economy depends on. The cash transaction protects both the buyer and seller that require discretion. Also, for many legal, yet “under-the-table” transactions such as paying small vendors, contractors, or laborers, cash payments are preferable because they leave the door open for fudging income tax reporting.

Still, while it’s fun to muse over the possibilities, I believe the downside will be vastly outweighed by the upside. I’m excited to see how this plays out and how quickly such devices will be adopted (or how quickly they will be supplanted by software versions).

In my mind there are two approaches that companies seem to be taking to generate recommendations and help users navigate various types of information: trust networks and collective intelligence. Trust networks are based on your social graph (i.e. your online relationships) and therefore use your friends to help recommend content. Facebook Connect is a popular method of leveraging trust networks by porting your contacts into other applications (or other applications to your contacts). Collective intelligence, on the other hand, is a much more data-driven method of rating and recommendation.

The Twitter vs. Facebook debate provides another example of the relative merit of these two approaches. People often lump both social networks together because they both have status updates that detail the minutia of our everyday lives. But how these services are used is very different. Twitter is a platform for weak ties (i.e. more suited to collective intelligence) where Facebook is a platform for strong ties (i.e. by definition, a trust network). As Denis Hancock notes, “Facebook if for people I’d actually let into my house.” My Twitter contacts, on the other hand, are dominated by people I have never met and will likely never meet.

The important point is that both types of platforms are useful. While Facebook lets me keep in touch with friends and organize social events, on Twitter I find all kinds of interesting links to articles that I never would have found by polling my friends. Similarly, Twitter is a valuable platform from a market research standpoint because companies with the appropriate listening tools in place can now mine the collective opinions of millions of customers, prospects, and influencers. The argument that Twitter is a waste of time because, “why would I care what everyone is doing at every moment of the day” is valid, but the emergent trends and memes resulting from ‘what everyone is doing at every moment of the day’ can be extremely valuable.

This brings me to an interesting article I read on TechCrunch a few days ago: Location’s social paradox, which states that the main problem with location services is, “The more people you follow on them, the less useful the service is.” This is essentially recognizing the fact that for certain services—particularly those where you give up your physical location—the exclusivity of your close network of friends trumps the benefit gained from having access to many individuals. As such, most of the recent offerings (such as Foresquare and U.K.-based Rummble) rely on trust network – login and we’ll tell you what your friends are up to and what they recommend. In these cases, I would agree with MG Siegler from Tech Crunch who suggest the more friends you have on this type of network, the less useful it can be.

However, the potential to use these same services for collective intelligence applications is huge. For example, when you are considering a particular restaurant for dinner, you would be remiss to only consider the opinions of the few friends in your network that have been there. More powerful would be real-time access to the aggregated reviews of as many patrons as possible. Similarly, when driving on the highway, it may not be terribly useful to know that three of your friends are on the same highway, but it might be extremely useful to understand the traffic patterns of all the other cars on the road via the GPS signals from anonymous drivers’ cell phones. In short, social network applications that incorporate elements from both trust networks and collective intelligence (perhaps with the ability to toggle between the two or shut one off based on context) will be most valuable.

Earlier this week I spoke at a conference hosted by the Royal Flemish Society of Engineers on the topic of complexity. The keynote speaker was Prof. Geoffrey West, former President of the Santa Fe Institute that pioneered the study of complexity science using a combination of economic theory and biology/physics (the founders were an economist and a physicist – both Nobel Laureates).  The end goal of complexity research is to develop new integrated conceptual frameworks for understanding the interdependence between various complex adaptive systems that define our world, including cities, financial systems, and the environment.

West’s research suggests we may be able to use the same rubric to study both cities, and forests, and maybe even economies. Complex adaptive systems share certain characteristics. Among other things, they: have many nodes, are interconnected, are adaptive and resilient, have many participants that create bottom-up disruptive change, result in emergent phenomena, and are often subject to unintended consequences. Sounds a lot like the type of emerging business ecosystems we talk about here on the Wikinomics blog. Collaboration between large groups of disperse and diverse individuals is extremely complex; when you add in financial systems, various incentives, supply chains, and a global information network, it becomes even more so.

West also talks about different types of networks—often layered on top of each other—as a characteristic of complex adaptive systems. The better we can understand networks and their interdependence, the better equipped we will be to understand complexity. He believes that underlying all complex systems are simple rules or patterns. For example, if you look at the metabolic rate, size, and lifespan of various organisms, you can determine that every biological organism grows in the same fundamental way. Here West asks some compelling questions: Are cities and companies just very large organisms satisfying the laws of biology? If so, why do all companies eventually die, while almost all cities survive? To this end, I think there’s probably great value in studying the evolution and “biology” of collaborative networks, informal networks within enterprises, business ecosystems, information flow and knowledge networks, and the multitude of other networks that collectively define Wikinomics-enabled business practices.

So, what are the best types of structures to deal with complexity? If we base our answer on how the Santa Fe Institute is structured, we find that the solution to complexity requires a multidisciplinary approach that involves participants that can bring different perspectives and diverse expertise. It also necessitates an open, distributed, and collaborative approach, a willingness to take risks, and a relatively small executive team that is able to meet face-to-face in order to build consensus and drive decision making at the highest level. This sounds remarkably similar to what we prescribe for next generation enterprises that want to thrive in today’s dynamic business ecosystems.

Another interesting thought at the conference came from Prof. Francis Heylighen who spoke of the Internet as a global brain that may act to combat complexity at a macro level by reinforcing strong signals between parties and building “synapses.” Tied to the he global brain theory is his theory of human stigmergy—a mechanism of spontaneous, indirect coordination between agents or actions (think of the way ants and other insects develop collective intelligence that enables coordinated and fairly complicated activities).

All of this is very much related to the research we’re conducting here at nGenera regarding what we call the highly-instrumented enterprise where actions are increasingly digitized, sensors and software track and analyze new sources of data, and create new understanding of complex systems and emergent phenomena. Some examples of these types of tools in a Wikinomics context might include reality mining tools that track the behaviours of individuals; automated sentiment analysis of text, voice, and even video; platforms that generate data and offer venues for consensus-building; and enterprise monitoring tools that map the informal networks and measure productivity within organizations. In fact, I’m sure there are many more connections to be made here, and look forward to thinking more about this one and hearing thoughts from our Wikinomics readers.

Collecting and analyzing customer stories – the impact you have on specific individual – is one of three approaches that I think makes sense when calculating overall ROI in a customer experience context. The other two are mission and reach. Mission is easy – most organizations have some sort of mission statement, or if they don’t, they usually at least have a CEO or a board with a well-articulated vision. Reach is also pretty straightforward: How many people to you touch? What reach does is calculate the influence of an organization or an individual. It acts as a proxy for several influence measures including: authority, frequency (how often you create the opportunity to influence consumers), independence (lack of bias in their opinion), charisma (in the case of an individual), and persuasiveness. At the OSC, the measure of reach includes both the number of people that came through the physical location and the over five million that engage with them online.

For me, impact is the most interesting measure. When assessing impact, Kevin uses the example of Canadian astronaut Chris Hadfield. Having someone like Hadfield say that going to the OSC as a child helped inspire him to be what he is today means the OSC had a strong impact on him. Most companies have at least a couple of these types of exemplary stories. But at a more pedestrian level, any organization can listen to and analyze the stories of people that come through the doors and that write online in blogs, forums, and social networks every day. There are now several companies such as Attensity, Scout Labs, Radian6, Visible Technologies, Crimson Hexagon (and many others), that have software to help analyze unstructured information like customer stories. These companies can identify basic metrics like the percentage of positive and negative sentiment, as well as provide deeper analytics about specific product and service features that lead to customers having positive and negative experiences. The end goal for those looking for quantifiable ROI numbers around customer experience is to convert all unstructured data to these types of “numeric” representations that are consistent, tracked over time, and can be charted in a dashboard. In short, the systematic gathering and analysis of anecdotes.

However, the Machine in the story also has limitations, “the Machine did not transmit nuances of expression. It only gave a general idea of people – an idea that was good enough for all practical purposes.” The Machine is also standardized and all it domains are identical across the globe, “What was the good of going to Peking when it was just like Shrewsbury? Why return to Shrewsbury when it would all be like Peking? Men seldom moved their bodies; all unrest was concentrated in the soul.” As expected, the story eventually ends with the stopping of the Machine, and by extension the fall of mankind. The decay of the Machine is brought on by hyper-specialization of those responsible for its maintenance, and a complacent and decedent lifestyle:  

“Year by year [the Machine] was served with increased efficiency and decreased intelligence. The better a man knew his own duties upon it, the less he understood the duties of his neighbour, and in all the world there was not one who understood the monster as a whole. Those master brains had perished. They had left full directions, it is true, and their successors had each of them mastered a portion of those directions. But Humanity, in its desire for comfort, had over-reached itself. It had exploited the riches of nature too far. Quietly and complacently, it was sinking into decadence, and progress had come to mean the progress of the Machine.”

In Forster’s future, the Machine is a physical construct that acts as a replacement for reality and inhibits the movement of people and thoughts. However, the virtual layer of machinery that we are creating now has striking similarities.

In the digital world, man is no longer the measure by which progress is measured and the growth of the Web is not limited by our individual ability to create or even mental capacity to comprehend. This allows us to do tremendous things such as create simulations and models that would be impossible in the real world. However, in doing so, do we also lose a little bit of what it means to be human? For example, in the digital realm, there is no physical limit to how many “friends” we can maintain, no practical boundaries defining the amount of information we can acquire or transmit, no emotional context to our transactions and interactions, no appreciation for resources being consumed, and often a limited and sanitized semblance of our real world identities. Social networks are a perfect example.  In a recent project of ours on the topic of Pervasive Personal Identity, the original draft included the following passage:

“Humans, augmented with a rich digital self and related social graphs, experience a richer social reality and a hence a distinct advantage over humans without a rich digital self. As a consequence, organizations that embrace the rich digital self accrue significant advantages over organizations that do not.”

The language has since changed in recent versions, but it brings up many interesting questions: Would we benefit universally if all activity was mediated through social media? Do we have a choice anymore? Moreover, are we at risk of losing sight of the limitations of the machine? How are we measuring “advantage”; is it the progress of the individual, or the progress of the system? In an enterprise context, we no longer need to know everything related to our jobs, but rather simply have access to those who do know. At the same time, the big picture – strategy – is becoming increasingly complex and difficult for any one single individual to comprehend. As we suggest in a recent study on Continuous Business Strategy, technology platforms and machine-driven analytics are now an essential component of strategic decision-making.

We accept the use of technology-mediated interactions as gospel and depend on them for many routine tasks. When we interviewed members of the Net Generation and asked them to describe how they would feel if the Internet stopped. Responses ranged from annoyed to completely lost; without technology, the new generation would feel depressed, disconnected, and in all likelihood incapable of performing many day-to-day functions. Forster’s story provides an extreme example of technology-dependence to the point where the body becomes a detriment and infants born with the promise of undue strength were destroyed: “Humanitarians may protest, but it would have been no true kindness to let an athlete live; he would never have been happy in that state of life to which the Machine had called him; he would have yearned for trees to climb, rivers to bathe in, meadows and hills against which he might measure his body.”

In story, a character is reminded that man is the measure; after having to walk, he rediscovered notions of what it means to be near and far. I was reminded of this myself recently as I’ve been tackling home renovations. All things are originally measured in the context of the individual – the transportability of building materials are limited by my ability to carry them, the amount of work done on my house is measured in the time it takes for me to do it, or (usually) the amount of time for which I have to pay someone to do it. These limitations have of course been reduced thanks to the augmentation of machines and tools, and on a global scale, these limitations are harder to perceive because we have industrialized the production of most everything. But at a local level – i.e. home renovations – they are still very much real.

For me, the overall lesson is this: Technology must be used as a way to augment real world interactions, not replace them. As the antagonist in the story states, “The Machine is much, but it is not everything. I see something like you in this plate, but I do not see you. I hear something like you through this telephone, but I do not hear you.”

Gartner’s Hype Cycle for social media classifies Social CRM (i.e. the integration of social media into CRM systems) as a transformational technology that is two-to-five years away from mainstream adoption in customer service applications and five-to-ten years away from adoption in community marketing. While I agree that Social CRM will be transformational, I think the adoption will (and must) happen more quickly. Specifically, our research at nGenera has uncovered new data, new tools, new channels, and a new mindset that are accelerating the trend towards Social CRM.

Data: The data that is included in traditional CRM is limited to very basic identity and transactional information about customers. It does not typically include the type of rich digital profile information contained in places like Facebook and LinkedIn.  Customer feedback is collected through surveys, a method of data collection that is expensive, time-consuming, temporal, and often annoying for customers. But this is all changing. Customer data can be gathered from many sources, some old – such as the contact center – and some new. With respect to the contact center, the amount of unused customer data that is generated is astounding. One interviewee recently confided that his contact center writes the equivalent of a book every day – a book that nobody reads.  A first basic step is to generate organizational learning from contact centers. Once you’ve mastered this, you’re ready to move on to new sources of data. In this case, I’m thinking about reality mining, social networks, forums, blogs, and other digital venues where customers are engaging in behaviors that affect the company’s brand.

Tools: Listening platforms and sentiment analysis tools allow companies to capture customer preferences, complaints, feedback, and queries expressed online, while social network analysis can provide insight into the connections between individuals and identify key influencers. Companies can also track prosumer activity across branded communities and company-sponsored networks. When integrated with CRM databases, this information helps create accurate, up-to-date, and meaningful customer records. Although the CRM systems that currently offer applications to incorporate social media data only include data from a limited number of social networking sites – of which Twitter is the most common – this will likely change. Data will eventually be collected from all public online discussions as the concept of Social CRM becomes more accepted and companies develop strategies to deal with larger volumes of data. Once customer conversations have been successfully captured and incorporated into CRM databases, one can imagine a future where companies will be able to capture other forms of rich data, such as emotional data, photos, voice, and even video content (i.e. not just video metadata). According to a vendor I interviewed, companies can already correctly identify individuals online using available profile data with up to 90% accuracy.  This allows comapanies to find existing and potential customers online and gather new data about them. The contact center of the future will have a much richer digital picture of customers, allowing companies to personalizing product and service offerings, engage customers in meaningful conversations, and generate sophisticated trend data.

Channels: Many contact centers, such as those at Best Buy and Comcast now support social media channels and have dedicated teams devoted to responding to customers and prospects in public and branded digital venues. The question of whether or not to use social media as a listening platform or a contact center channel is major one for organizations as it affects the number of touchpoints that need to be managed and the complexity of customer support operations. However, as sentiment analysis tools get better, and integrate more readily with CRM, we expect this distinction to become less and less of a concern. In the future, the new sources of data (inputs) will be the same as the channels for customer interaction (output). As these channels mature, I fully expect the data and analytics to help “close the loop” with respect to customer engagement metrics – directly connecting social media investments with customer sales information. In this way, companies will be able to measure the value of customer intention and calculate the ROI of social media interactions.

Mindset: The notion of ‘relationship management’ brings with it a particular bias that data is controlled by the party that is doing the managing, rather than ownership of the data by the individual. So, in the case of CRM, it is assumed that the company is managing customer relationships by controlling the data about them and their interactions. New notions of relationship management seem to embrace the idea that ownership of both identity information and the customer-vendor relationship should reside with individuals, not companies.  Exchange of information should be based a two-way value proposition in which individuals selectively share aspects of their rich digital profiles, as well as their discretionary effort in exchange for useful and targeted messages, promotions, and reputation.  Paul Greenberg from ZDNet discusses this in more depth and notes, “Co-creation and mutually derived value, is at the core of Social CRM.” As an example, The Internet Identity Workshop and Project VRM (Vendor Relationship Management) at Harvard is exploring a highly customer-centric view of identity information where the customer controls their data and manages relationships with various vendors.

I’ve heard the argument that traditional CRM “is dead,” but this is far from the truth. In fact, as Brian notes, Social CRM does not replace transactional CRM systems, rather it augments them. What CRM is in desperate need of is new data sources and tools that help integrate and analyze this data. The future vision of CRM also requires that companies get involved in new channels and cede a certain amount of control to the customer – it’s less about management and more about engagement.

What’s equally impressive is some of the display technology being developed to display this type of data. All vendors have some form of executive dashboard, but these are highly utilitarian. From what I have seen, the bar for sentiment visualization is being set by other innovative thinkers. For example, ongoing projects like We Feel Fine from Jonathan Harris and Sep Kamvar as well as Bio Mapping from Christian Nold aim to visualize emotional data in new, interesting, and useful ways. We Feel Fine is more of an art project than a rigorous sentiment analysis tool, but it provides a useful example for how we might organize, display, and search for comments. Users on We Feel Fine can search by emotion (key word only), gender, age, location, weather, and date. It also connects emotions to associated images that are found within the document.

Bio Mapping (shown below) uses a lie detector connected to a GPS unit to measure location and physiological arousal at the same time. This is then plotted using Google Maps and other visualization software. Note, in this case the sentiment metric is intensity of emotion, not the specific emotion itself. The spikes shown on the map are locations of interest, but that is all we can determine from the data.

Although most of the complex visualization technology is still nascent you can imagine where this is type of analysis is going. Companies could segment customers based on emotional response, plot the spread of viral buzz, identify ideal test markets, and optimize local campaigns based on near-time feedback loops. Employees could gain access to a new lens on customer activity, behaviour, and satisfaction in a user-friendly display that makes analytics fun. Large retailers could use similar mapping technology powered by emotion data to optimize store layout and measure display/product appeal. The biggest challenge to wide adoption of these types of tools is the lack of valid emotional data in significant volumes. Currently, mining user comments online is the best available data source, but some early research suggests promising breakthroughs in the area of voice analysis and facial recognition as well – stay tuned.

Today people are expensive and technology is cheap so only the privileged few have EAs.  Their role has changed considerably, but at the core they still help manage information. For those without EAs, we now have more technology to help us– computer file systems, pre-programmed phone directories, Outlook, instant messaging, corporate intranets, iPhones, and wikis – and yet a large portion of my week is still spent manually organizing, finding, and sharing information.

Where am I going with all this?  I’m not convinced we’re better off.  This isn’t just because Mad Men does a great job at selling nostalgia, but rather because I don’t think our information management technology has been able to keep up with the rate at which information is growing. The point can be made that since we now have a reasonable degree of competence with the technology it makes sense to interact with information directly, rather than have it mediated by another party.  I would argue that maybe competence has become a burden.

Although it seems strange to even think what life with an EA might be like for an average 21st Century knowledge worker, I can certainly think of many modern tasks that many of my colleagues would be only too happy to divest, such as uploading and downloading content to/from shared sites, searching Google for specific tidbits of information, and generally separating signal from noise across all the various channels through which we are bombarded with information.

If mid-level knowledge workers still had EAs, would productivity improve? A lot of tasks have been automated, but a lot of new manual tasks have been created – these are the daily irritants that often prevent knowledge workers from sustained activity in core areas that create value for the enterprise. It’s real work, but it’s rarely accounted for or even acknowledged – it just becomes collaborative overhead.  I’m not suggesting we go back to the Mad Men days of “a girl” at every desk, but I think a shared resource for many collaborative and administrative tasks would be useful. Is it just me or does anyone else feel like knowledge workers could use some support resources?

I leave you with a politically incorrect clip of Peggy and Joan; a view of EAs from the past, as seen through the eyes of the Mad Men writers:

(Embed disabled – click to play video at YouTube.com)