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.

Signal-to-noise ratio: I think the most visibly important metric – analogous to points on a basketball stat sheet – should be one that is focused on the value and quality of content you broadcast.  Using Twitter as an example, measure tweets versus re-tweets.  If your content is getting re-tweeted it’s safe to assume that it’s valuable (signal) and not noise. For blogs, the metric might be comments per post, indicating a compelling or timely argument worth discussion. Using online sentiment analysis tools, companies could add an additional layer of complexity to this stat by also measuring positive versus negative comments.  As a basic example of signal-to-noise, my ratio based on re-tweets/tweets is: 10/91 = 0.11.  For comments/blog posts it would be: 289/79 = 3.7.  Of course in a multi-channel world, the metrics get muddled.  If re-tweet is the new blog comment, how do you calculate the metric?

In/out ratio: How good of a curator are you of information?  We interviewed the company Cataphora a few weeks ago (recently profiled in BusinessWeek) – their software uses social network analysis to identify good content by how much it is shared and passed around and track document originators and curators to assess individual productivity.

Document “originator” stats: Building on the previous point, how much good content do you generate, where “good” is defined by the number of times your content is reused (this is similar to signal-to-noise, but attempts to quantify the strength of the signals). At a more granular level, the originator stat might also highlight certain areas of expertise – e.g. all good ideas related to robots originate from Alan.  A twist on this metric could be “conversation initiator stats,” which would track who kicks off popular conversations on ones that lead to valuable ideas that are implemented.

Responsiveness: This would be a fairly basic stat that looks at how quickly you respond to other people’s requests.  As part of this, you would also have to account for the number of requests an individual gets (which might actually be another stat around collaborative demand or reputation).

Feedback assessment: To temper responsiveness stats, you’d want to have something like the “assist” stat in basketball, where you only get credited for responses that lead to a positive outcome.  As an example, you could base it how much change occurs to documents as they flow through you.  This would have to use software that analyzes document content and tracks versioned documents (independent of filenames, which often change as they pass through different users – e.g. docs that come through me usually leave as filename_nh).

Sociometric factors: These are the intangible aspects of collaboration that don’t necessarily leave a digital trail that is easily measured.  I liken this to measuring an individual’s plus/minus in basketball – it’s based on how other people perform on their stats when they are around you.  Since there’s no “court time,” in enterprise collaboration, you could measure face time through badges, digital connections, or even video.  Using this type of reality mining, a company could also analyze things like the tone of conversations as well as emotional response in order to gauge the impact you have on the morale of those around you (without necessarily measuring content, which leads to privacy issues).  This stat might also highlight diminishing collaborative returns – if too many minuses start showing up, maybe you’re collaborating too much (or are not very good at collaborating).  A company could also develop a “starting line-up” for projects based on sociometric factors that show positive results from certain combinations of employees.

Many of our ideas about organizations and leaders were formed at a time when the primary operational challenge was one of getting people to perform tasks consistently and reliably. We leveraged best practices to achieve a uniform approach. We required that everyone be present in the same place and time, in some cases to get the work done, but at a minimum to allow us to gauge performance by watching in-process activities.

But more and more of the work that differentiates our businesses today depends on divergent or creative activities. Our challenge is one of creating environments that encourage people to become engaged, to take initiative, invest discretionary effort in a wide variety of collaborative activities, and, as a result, develop new approaches and ideas, provide extraordinary customer service, or ramp productivity. Think of this challenge as one of setting the stage, creating an environment that engages players from multiple constituencies. It is a “pull” rather than “push” approach to achieving business results.

Does your organization have the processes and practices, the leadership skills and the relationships among participants that you’ll need? Do you have the capacity to collaborate?

Over the last several years, our research has identified the characteristics of organizations that are successful at collaborative activity. With extensive data from teams from around the world, we identified ten factors that are highly correlated with successful collaboration:

1. Highly engaged, committed participants
2. Trust-based relationships
3. Prevalence of networking opportunities
4. Collaborative hiring, development, and promotion practices
5. Organizational philosophy supporting “community of adults”
6. Leaders with both task- and relationship-management skills
7. Executive role models for collaboration
8. Productive and efficient behaviors and processes
9. Well-defined individual roles and responsibilities
10. Important, challenging tasks

Investing in these ten enabling factors builds an organization’s Collaborative Capacity – its ability and willingness to share information, ideas and insights productively. Conversely, productive collaboration is unlikely to occur is these factors are not in place. A journey to leverage the benefits of collaboration in your business must begin with assessing and, as necessary, building your organization’s Collaborative Capacity.

Think of this like beginning a manufacturing business. One fundamental question you would face is whether you have the right manufacturing capacity. Do you have the right facility? Is it well-maintained? Do you have the right permits and disposal mechanisms in place? And so on. These questions would be the foundation required before you begin any specific manufacturing process.

Or, think of it like assessing the talent in your firm. Most of you probably do an annual review of your workforce, asking: Do we have enough people to deliver? Do they have the right skills and training? Are they engaged?

Assessing your Collaborative Capacity is similar to these two analogies. Do you have the beliefs, processes, behaviors – the things our well-grounded research has shown to have a statistically valid correlation to collaboration – in place as a foundation upon which to build?

In upcoming posts, I’ll share ways you can assess your organization’s Collaborative Capacity and some of the approaches successful companies are using to enhance these factors in their organizations.

Becoming a Collaborative Enterprise won’t just happen. Many of today’s processes and practices – and the culture within many organizations – are not ready to reap the benefits that the new collaboration can provide. The ability to collaborate can be a powerful competitive advantage – but doing it successfully requires the right organizational context.

If you’re interested in learning more about tools to assess or build your organization’s Collaborative Capacity, please let us know.

1. At an individual level people suffer from cognitive overload. As people get busy and collaborate across a multitude of projects, the brain gets distracted, and the quality of the output suffers. In short, one person can only do so much.
2. At a project level where you run into a situation of ‘too many cooks spoiling the broth.’ In short, only so many people can do one thing.

If you put the two of these together, the worst-case scenario is that in an individual could join a project as the Nth person who ‘spoils the broth,’ while the time they dedicate towards doing so distracts them from their other work – which, continuing the cooking metaphor, leads them to burn the toast as well.

The problem is, it’s very difficult to apply a scientific approach to measure exactly how many people per project, and conversely how many projects per person is optimal. The most well-known study around this is Dunbar’s Number, which sets “a theoretical cognitive limit to the number of people with whom one can maintain stable social relationships” at 150. In terms of collaborative overhead, Dunbar speculates that “as much as 42% of the group’s time would have to be devoted to social grooming.” Now that might be acceptable for the hunter-gatherer societies described in Dunbar’s anthropological study, but I would imagine this amount of “grooming” time would be extremely unproductive in an enterprise context.

In his book Collaboration, released this month, Morten Hansen, a professor at the University of California, Berkeley and INSEAD, identifies two costs related to enterprise collaboration. The first is the opportunity cost collaborating (i.e. the opportunities individuals could have been pursuing had they not been collaborating), the second is the cost associated with fostering co-operation. In both cases, as the number of projects or the number of individuals grow, so too does the potential for diminishing returns.

At the project level, I feel as though most people have general understanding that there is a certain point at which there are simply too many stakeholders and collaboration breaks down.

However, at an individual level, I think we are less cognizant of – or less willing to admit – our own limitations. I’ve seen many cases where an enthusiastic and eager collaborator was clearly overburdened and well past the point of optimal effectiveness. Incidentally, my personal hypothesis is that this point of optimal effectiveness is a fairly small number of projects per person. My main “proof” for this is anecdotal, but I notice that the busier one is, the more likely they are to quickly skim a topic and provide feedback in short (sometimes valuable) chip-shots without contributing to a better in-depth understanding of the topic space. Worse, in some instances perceived value comes from dissenting, so instead of constructive feedback, you get wildly varying opinions with no one working towards a coherent solution.

On the subject of cognitive overload, a recent Deloitte report notes, “Even a Sunday newspaper contains more information than the average 17th century citizen encountered in a lifetime. Add to that the stress of decision-making amidst uncertainty, corporate change, and a tidal wave of tasks. Never before in history have workers been asked to absorb and make sense of so many data points.” One more sensational study even suggests that information overload is more damaging to the brain than smoking pot. I think we can certainly make an argument that where collaboration is most likely to break down is at the individual level.

This brings up another point: What about the virtues of solitude? Are we losing our capacity for individual decision-making? Moreover, who’s actually doing the deep thinking needed to solve complex problems? We talk about the multitasking Net Gen brain that is not actually doing multiple things at once, but rather switching more efficiently. Does constant switching allow for deep analytic thought?

So what is the solution? Overall, I’m wondering if there’s a Dunbar Number for the optimal number of simultaneous projects per person (small and large). How is this number affected when you take into account broader ecosystem participation and places where quick feedback from multiple participants is actually desired over in-depth participation?

As a start, I think collaborative technologies can help by streamlining different types of feedback. So, for example, a project can have 1,000 collaborators if they are providing feedback via a prediction market. Conversely, if only three people are collaborating on a document, perhaps a wiki is most effective.

One possible model for managing cognitive overload is letting individuals self govern – i.e. everyone decide where they can add the most value. Of course, this also raises many issues, including: people, especially in high-performance cultures, tend to overextend themselves; people tend to pick project that interest them, but that may not add the most value to the organization; and people tend to be social and so will gravitate towards the same projects, thus contributing to project inefficiency.

In order for this to work, you would have to architect a system that would allow people to allocate their own time in a structured way (similar to the Freiburg budget example). I’m envisioning a system where resources are finite but can dynamically allocated; where employees are guided by decisioning logic that identifies the projects that provide the most value to the organization; and where limits are set that prevent projects from being staffed by too many people and that stop people from taking on too many projects.

The Wikinomics team has many posts discussing the real potential of Twitter for business value. This is how I predominantly use the tool: As an efficient way to find new stories relevant to my research, as well as discover interesting or amusing content, and occasionally peek into the personal lives of interesting people. A good signal-to-noise ratio is critical for this type of usage.

I use Tweet Deck to help filter my updates, but it still becomes a challenge because filtering is by individual, not content. That means if I add someone to my “Main Feed,” I have to somewhat trust that they will act responsibly. What do I mean by responsible? The other day I opened Twitter and notice 39 tweets in a row from the same person, most of which were links. Literally 39! That’s irresponsible. It’s also not a way to get noticed on Twitter, but rather ignored.

Responsible Twitter users abide by four simple rules:

1. You learn something new everyday
2. Twitter is not chat
3. Don’t be a needy jerk
4. Ignore rules 1 to 3 if you are in marketing

RULE #1: You learn something new every day
There are two key parts of this rule: 1) “Learn” – Share a new discovery, something novel or interesting you learned today. There are a lot of people I’d like to hear from more often, but who rarely post (I’m somewhat guilty of this myself). Everyone has something they can contribute to Twitter. At the same time, it sets the bar slightly higher than posting a stream of consciousness. 2) “Something” – not everything. How much is too much? Think top-10, max. I gave Denis a hard time about this a couple of weeks ago.

In truth, Denis was actually at a conference tweeting insights from various presentations. It’s an exception to the rule that I haven’t figured out how to deal with yet, so I took him back. He hasn’t betrayed my trust since ; )

RULE #2: Twitter is NOT chat
There is a reason that the inventors of Twitter had the good sense to include a Direct Message function. No one wants their feed spammed with out-of-context snippets of conversation like, “Sorry I missed you @mybuddy, next time for sure.” This is a very simple, yet often forgotten rule.

RULE #3: Don’t be a “needy jerk”
If you missed it, Vanessa Grigoriadis has a fantastic article in New York Magazine about Facebook. In it, she talks about the declining usefulness of Facebook updates:

“This was the beginning of the end. Suddenly, Facebook began to irk me—the way friends always posted about procrastinating, being stuck in traffic, needing a nap or a vacation, or seemed to formulate their updates in declarative yet vague form, like ‘Michelle is upset’ or ‘Roya is pouting,’ thus coming off like a needy jerk and making us take time out of our day to plead with them to answer the burning question: ‘Why are you pouting?’”

I couldn’t have put it better. There is a lot of this on Twitter and it’s exceedingly unproductive. Worse; it’s noise that obfuscates the valuable signals we’re all trying to get at.

RULE #4: Ignore rules 1-3 if you are in marketing
The future of Twitter – especially in marketing – may well be bigger than my “rules.” I talked to a company Crimson Hexagon this week that makes a business out of mining user opinions online (including from Twitter) for market research. In fact, marketers love the stream of consciousness because to them it’s data, and valuable data at that: “Post all you want about what you had for lunch, just be sure to mention the brand of mustard you used and how it made you feel…” (Note; Rule #4 should have excused Denis from his misstep on Rule #1, since he is our program manager for our Marketing 2.0 program.)

Marketers also don’t want to use Direct Message. If you answer a customer question publicly, it not only makes you look good in terms of being responsive, it also potentially saves you from having to answer the same question multiple times, as well as helps boost brand mentions. For more on how to think about your brand on Twitter, see: A potential framework for how different brands are using Twitter.

Unable to pinpoint all the barriers to widespread adoption of EMR, I got in touch with Dr. Nadine Gebara, Resident, currently at University of Toronto. I asked her why our health care institutions have been so slow in digitizing their info - something libraries started doing almost 20-years ago. I quickly learned that the barriers to EMR are greater and more deeply engrained than I had imagined.

Initially, I suspected that resistance on the part of physicians had something to do with hospitals’ slow move to EMR, but Dr. Gebara’s experience suggested otherwise: “I have seen very little resistance on the part of health care workers. There’s actually some frustration among physicians about not having adequate EMR systems. Overall, attitudes towards electronic records are overwhelmingly positive given the clear benefits to efficiency and ease of access to critical information.”

While EMR promises clear long-term benefits to efficiency and quality of care, government is reluctant to spend on the technology. The investment in EMR is seemingly a no-brainer. Sure, it’s expensive in the short-term, but soon enough we’ll see increased efficiency, more care to more people, and health care providers happy not to have to sift through piles of paper records to find critical information.

The “no-brainer” status I assigned to an investment in EMR was challenged as I continued to explore the issue with Dr. Gebara.

“There are a handful of companies offering EMR products and platforms [like this one and this one, or just look here for a comprehensive list]. Hospital A, for example, might use one platform, but Hospital B down the street uses a different one. It’s great that they are using EMR, but the problem we run into is that you generally cannot share data between the two systems,” she said. 

The fact that different platforms cannot be integrated creates uncertainty around what the outcome and ROI of any wide-scale (and expensive) EMR initiative might be. Inability to clearly estimate and articulate the benefits of EMR hampers efforts to secure government funding. And, quite simply, ”lack of funding is the greatest barrier to universal EMR.”

One (partial) solution would be for government and health institutions to select a single platform so that data can be shared in any hospital in any region.  But, if the government selects one service provider, do they not create an illegal monopoly in the industry?

Without government intervention, leaders in EMR technology will just have to emerge over time. With fewer players in the field and more hospitals adopting the same platforms, scalability and the ability to share data between hospitals will increase. In turn, the case for EMR funding will strengthen. Unfortunately, this implies simply waiting for the fittest platform to emerge. If that’s the case, then we shouldn’t hold our breath waiting for universal EMR. 

Adoption of Wikinomics principles on the part of the health informatics industry (referring to companies building and providing EMR platforms and services), however, would speed the journey to easily integrateable platforms. My guess is that the first company from within the EMR space who breaks down silos and produces a platform that integrates with others will be dominating the field when EMR arrives in full force – sooner, rather than later.

For the last few weeks I’ve spent a fair bit of time thinking about the Keynesian “Paradox of Thrift“, which has become particularly relevant in today’s turbulent economy. As everyone knows by now, one of the driving forces of the problems revealed in 2008 was that consumers took on too much debt. The natural anecdote for this is for consumers to stop borrowing, and start saving – but that’s where the paradox lies. If everyone does that, aggregate demand will fall, the economy crashes, and the savings rate falls further still (also noting that when one saves by putting money in bank, it has to become debt for someone else in order to earn interest). Thus, we have a problem.

So it’s a case where doing what looks like the right thing for the long-term success of the economy has some perilous implications – at least in the short-term. In turn, it got me thinking about whether there is a similar, and potentially much larger, “Paradox of Wikinomics” as well. What I mean by this is that while application of the wikinomics principles might appear to  be the right thing for many companies and industries acting in their own self-interest, everyone adopting them at once could have similarly dire consequences – again, at least in the short-term.

In order to explain, let’s start again (also used in the wisdom of crowds vs. uniquely qualified minds post) with the first story in the book – GoldCorp. The gist was that the company ran a contest to find the best methods for identifying gold on their property, to great success. In theory, the methods they identified are probably the best for many such potential mines around the world. A logical extension would be that there are probably thousands upon thousands of people employed trying to discover ore deposits, that might very well now be redundant, if all similar companies adopted such approaches – transparency, information sharing, etc. – simultaneously. The old model, while less “efficient”, created more jobs.

So fine – one small subset of workers in the world potentially losing their jobs would barely cause a ripple in the global economy. But as you extend the principle of what made the GoldCorp story a success to other industries, such job loses can pile up. Other ideagoras (like Innocentive) would be an easy example, as companies start only paying for successful results (and a winner-takes-all economy takes hold) in R&D, while numerous people can no longer earn a living. But on a much larger scale, transparency and information sharing within the enterprise could make an extraordinary number of jobs redundant – jobs companies might be less resistant to cutting in the current economic climate than before. One easy example is “white collar grunt work” replaced by more effective, collaborative technologies – but there are many others.

And it of course doesn’t stop there. We’re already witnessing the demise of many newspapers, with the hyper-efficient Craigslist model being held responsible by many people. While I’m confident that the creation and dissemination of news will figure itself out again in the long run (and check out this excellent Clay Shirky interview for more thoughts on this), we’re seeing tremendous pressure on all creators of content tied to an advertising supported model. As the popularity of social media continues to increase, I expect that this trend will continue – and a lot of current jobs will be threatened.

I could go on, but I think you get my point by now. In the long run, what drives the wealth and success of an economy is productivity and efficiency. In my opinion, many of the principles of wikinomics continue to hold the promise of an extraordinary amount of efficiency and productivity to be unleashed, which should/ could have amazing long-term benefits. But in the short to medium term, I see the potential for a very difficult paradox – what makes the economy more efficient and productive as a whole causing a major dislocation of workers, who as we all know are also the consumers, and as they have less to spend the economy potentially shrivels up in a way similar to the paradox of thrift.

Given that the tagline of wikinomics is that mass collaboration changes everything, this dislocation could be on such a scale to make it a much tougher paradox to deal with. In such a case, the challenge is to ensure that the wave of innovation that can be unleashed through applying the wikinomics principles creates enough economic growth, and jobs, to compensate – and make sure the displaced workers can be re-trained to do them.

If the traditional metrics are counting widgets and widgets aren’t falling off the assembly line, managers get irate. They wonder why their widget numbers are low, or why their shipments are late – it is, after all, their ass on the line. Knowledge workers, on the other hand, are no slackers. They know widgets are important, and they’re working hard to make the shipment. But they are also engaging in collaborative activities, like maybe working with Chinese widget-makers to figure out how to lower costs, or working with German widget-makers to figure out how to improve quality. Moreover, thanks to collaborative efforts, the widget itself seems to have somehow evolved into a gadget which is far more complex a thing.

So what’s my point? This is where old-school management techniques collide with new-school ways of working. Under the old-school way of thinking, the manager will wonder why the widget order is late and will blame the worker for slacking on the job. He may trust the worker as a person, but as an employee, he doesn’t trust that the employee is working as hard as he could – a quick slap with the stick should get the work mule back on track. The employee will be frustrated and feel underappreciated because, from his point of view, he’s just put an inordinate amount of time and effort into company projects with no thanks. Worse, he got the stick.

Now let’s apply a trust model. The alternative manager trusts that the employee is a hard worker. He recognizes that the gadget may actually be a better product than the widget and that future returns on quality and cost initiatives might pay off (or might not – that’s the gamble with collaboration). In fact, the new-school manager could even take an interest in the additional value the worker is trying to create and collaborate with them on their initiatives, offer feedback, or give praise and recognition. Now this type of model tends to get old-school managers up-in-arms. They may not say it, but the underlying belief is that serious business can’t be managed by trust any more than companies can be run by a bunch of Kumbaya-crooning, no-work-ethic, granola-crunching slackers (or if it’s Net Gen employees; coddled, self-entitled, no-work-ethic video game junkies).

However, I would argue that until the value of collaboration is truly measurable, trust is the only reliable model we have. Plus, it’s not like checks and balances don’t exist. In collaborative environments, if someone’s not pulling their weight, it becomes quickly apparent by others’ unwillingness to work with that individual. The future vision is one where we can apply metrics to a worker’s collaborative activities and buttress the trust model with some hard numbers—but until then, trust, respect, and recognition will be better motivators in collaborative environments than traditional ‘carrot-and-stick’ methods that are based on metrics that are either false or misrepresentative of value. I’ll also say that the trust model is especially important in the current economic climate where carrots may be in limited supply – just sayin’, you know, from the perspective of the donkey ; )

Part I, Part II, Part III

• Collaboration will help us do more with less. The idea is that collaboration can lead to greater efficiencies and reduce the amount of internal resources required for projects. Collaboration leads to better information which helps make better decisions in terms of how to allocate resources. Collaboration also takes advantage of prosumers and social networks where individuals can help co-innovate with companies at a lower cost than using exclusively internal resources. There’s an assumption underlying this scenario that there’s some surplus capacity in the economy because a) companies are cutting back on projects, but not necessarily eliminating all of the corresponding staff, and b) those employees that are cut will be available for contract work and targeted initiatives. In fact, this may even be a good time to stock up on promising young talent. The pro-collaboration folks suggest that collaboration can lead to new growth opportunities that will help companies differentiate themselves in difficult times. Extra cycle times may also be directed at innovation and R&D; “there’s a lot things you can do during rainy days” and building a pipeline of products and services may be one of them.

• There’s no time for collaboration; doing more with less means we’ve got to hunker down. With an economic downturn companies will not be willing to appoint resources to collaborative projects. Since the return on investment of collaborative initiatives is not always apparent, employees will also not be willing to allocate their time on activities that are not measured in performance reviews or not seen as directly contributing to the bottom line. From an employee perspective, it may make sense to hoard knowledge in tough times and be less collaborative in order to make yourself indispensable (i.e. prevent being eliminated). The “hunkering down” managers believe that collaboration is risky and can lead to wasted resources on dead-end projects. Moreover, they frown upon self-organizing behavior; suggesting that it will result in unfocused initiatives. It’s time to run a tight ship and that means scrutinizing all discretionary pursuits, focusing on internal resources, cutting salary expenses where possible, and getting remaining salaried employees to shoulder the extra weight.

Two opinions; two very different strategies. What do you think?

Most managers still measure knowledge worker productivity based on the number of widgets they pump out (i.e. How many papers/reports/models/spreadsheets/client engagements/etc. did you create/take part in this year?). The truth is; a tremendous amount of value is being created through collaborative initiatives that are not measured in traditional performance reviews or budget line items.

The best existing method used to capture the value of these collaborative initiatives is 360-degree performance reviews where managers solicit feedback, not only from direct supervisors, but also from employees, peers, customers and anyone else the employee might relate with in the course of doing his/her job. However, the information collected is subjective, exposed to the personal bias of co-workers, and not terribly timely (i.e. reviews are given annually). What’s needed are more scientific, real-time, analytic reporting tools that can capture collaboration and productivity metrics in the same way that we gather metrics about manufacturing processes, supply chains, IT networks, call centers, sales, and other facets of the business.

While the current state of software tools around collaboration metrics is far behind the reality of what’s going on in most knowledge work environments, there are glimmers of hope. Our office recently started piloting a tool called Snowflake (currently in alpha launch). The software is taking steps towards providing the type of “collaboration ledger” that I’m envisioning, at least for some activities like providing feedback. It allows co-workers to solicit/offer anonymous feedback and advice from each other at regular intervals. More importantly, the program captures metrics around who provides feedback and how often, how feedback is valued, what topics are most popular, who has expertise in different areas, and so on. The tool is still in alpha launch, but I can see great potential if it expands to develop reporting capabilities for managers, allows for the sharing of documents (also tracked by metrics), and can be integrated with other applications so that metrics are gathered for all types of collaborative activities.

Part I, Part II, Part III

There’s an assumption among Web 2.0 evangelists and mass collaborationists that the real problem with knowledge work is e-mail (e.g. see our own blog here and here). I’ve been thinking about this a lot, and the more I think about it, the more I’m convinced that e-mail is more a symptom, not a root cause. It’s often said that the challenges associated with enterprise collaboration are 90% cultural and only 10% technology-related. Is e-mail really the problem, or is it the nature of work? Giving up e-mail sounds fine and good, but it doesn’t remove the number of discrete tasks you have to complete. The real problem is the workflow model for enterprise collaboration.

This methodology is by no means scientific, but I’ve analyzed my own e-mail as a test case (I would encourage everyone to do the same). To provide some context; I work within a relatively small team of about 20 people and we are a very collaborative group. For the majority of people in the office, e-mail is the primary means of electronic communication. We also rely on phone and face-to-face communication, but in terms of sending documents and assigning tasks, e-mail is used in most cases.

Looking at an average week, I received a total of 214 e-mails over a seven day period (I don’t use e-mail for RSS feeds or wiki updates, otherwise I’m sure the number would have been much higher). Of the 214, fully 91 were immediately deleted because they were spam, FYIs, or ‘thanks’ (minimal time was spent in this culling exercise). That leaves about 123 in my inbox, or about 23 per day that don’t get deleted right away (only 7 came in over the weekend). Of these 123 relevant messages, there were:

• 29 tasks that fall outside my “normal” workflow.
• 14 tasks which take an hour or more to complete; 3 of which were delegated to others.
• 24 e-mails related to scheduling meetings/calls.
• 56 e-mails where I am the primary or only recipient.

I think the strongest underlying message that I can draw from this is that collaborative work, by its very nature, creates a significant number of tasks that are above and beyond regularly assigned work. This generally includes things like: providing feedback to others; reviewing documents and presentations; joining meetings, conversations, and online discussions; documenting best practices; and identifying new problems and solving them on an ad hoc basis. E-mail often gets blamed for all of this “extra work,” but the reality is, these tasks and activities won’t go away simply by switching to other technologies like wikis, IM, or intranets. Moreover, these tasks often result in new value for the enterprise and need to be recognized as “important work” not “extra work.”

Part I, Part II, Part III

Dr. Mihaly Csikszentmihalyi, author of “Flow: The Psychology of Optimal Experience” describes Flow as:

“Being completely involved in an activity for its own sake. The ego falls away. Time flies. Every action, movement, and thought follows inevitably from the previous one, like playing jazz. Your whole being is involved, and you’re using your skills to the utmost.”

Benjamin B. Bederson, “Interfaces for Staying in the Flow” uses the following diagram to explain Flow as the channel between challenge and skill; the tension between anxiety and boredom:

Contrast this with what we now know about multi-tasking; specifically, that multi-tasking is not so much about doing multiple things at once, but rather efficiently switching between tasks. From the 2001 NY Times article “How to Multitask“:

“Don’t think you can actually do two things at once. Even when you think you’re doing more than one thing simultaneously—say, driving and talking on a cell phone—you aren’t. Unlike a computer, the brain isn’t structured as a parallel processor. It performs actions, even very simple actions, in a strict linear sequence. You must complete the first task, or part of that task, before moving on to the next. What we call multitasking is actually task switching.”

It strikes me that businesses and technology vendors have done a lot of thinking about how improve productivity through multi-tasking, but little thought has gone into the importance of Flow in a knowledge work environment.