Connecting the enterprise – one tool breaks the rule

The world is getting more interdependent, and to get stuff done more people need to coordinate what they are doing with people in other business units. No wonder that collaboration is a hot topic. But what has surprised me is that in spite of an increasing number of tools, most of them are actually not connecting people across business unit boundaries. Sounds like a contradiction? Read on…

Collaboration is still mostly happening within business units

I’ve been involved in more than 100 social network analysis projects over the last 15 years, and most of these projects we’ve found that business units operate very much in silos having only limited interactions with other business units.

Physical location has a big impact in who you interact with. Professor Tom Allen discovered this many years ago and coined this the ’50-meter rule’. According to this rule, most interaction drops off when you are located more than 50 meters away from another person.

Given that employees from the same business unit are still being physically placed near each other i our workplaces, this only makes the likelihood of you interacting with someone from another business unit even smaller.

Tom Allen’s 50 meter rule:

Therefore, when I was running a collaboration analysis project for a professional services firm, I expected to find this same Business Unit silo pattern. But this time we uncovered something new.

Our collaboration research partner Dr Agustin Chevez from HASSELL had cleverly designed the study for the client in such a way that the data sources we analysed (see below) covered the exact same 4-week period. That meant that we could precisely compare interaction patterns across collaboration tools/methods:

  1. Face-to-face interaction (via traditional social network analysis survey)
  2. Email data (Exchange)
  3. Instant messaging data (Lync)
  4. Timesheet data (billable hours analysed to find out who works with who)
  5. Enterprise social network data (Yammer).

One tool breaks the rule

While face-to-face, email, instant messaging and timesheet interaction patterns all stayed mostly within business unit boundaries, one tool broke the rule. Yammer, the enterprise social network, was the only tool in the portfolio that broke the traditional 50m rule. This also meant that it was the only tool that was busting business unit silos.

Screen Shot 2016-12-08 at 10.28.58 am.pngOn reflection that makes a lot of sense. You really don’t bump into someone via email, phone or instant messaging as these tools have a clearly defined list of recipients. However, when you post something on an enterprise social network it is not limited to a set of intended recipients, and the audience is therefore anyone and anywhere in the organisation. We also know from our global Yammer benchmarking study that while enterprise social networks do cater for private conversations, the clear majority of messages (about 80%) are actually public.

These two characteristics (open for all, and fully transparent) that are hallmarks of an enterprise social network, and are completely different from email, instant messaging and phone calls that are by nature restricted to a defined (any typically small) set of recipients. You might find it amusing to know what we discovered that 95% of all emails only had single recipient, and the about 50% of emails sent were to a person sitting less than 6 meters away.

Collaboration within teams or collaboration across business units

We have an increasing number of collaboration tools at our disposal, and these are doing a terrific job enabling people to get work done. But as you’ll now appreciate they are far from equal. Some are making existing teams work more effectively together within the team and that is undoubtedly of tremendous value. But it is up to the enterprise social network to foster new connections across business units.

To drive organisational performance, we must have collaboration tools that serve different objectives, and we need to be very clear about their strengths, weaknesses and fundamental differences. Professor Andrew Hargadon writes in his book How Breakthroughs Happen: The Surprising Truth About How Companies Innovate (Harvard Business School Press 2003) that innovation happens at the intersection of people and ideas. To do this at on a global scale we need enterprise social networks. They play a fundamentally important role in enabling people to connect across business unit boundaries.

Diversity is Essential but not Sufficient

diversity-imageDiversity is a big word in business today. We are preached to continuously about how important having diverse leadership is to improving your performance. HBR in their article on ‘Why Diverse teams are Smarter”, identify studies showing that diversity based on both ethnicity and/or gender can lead to above average returns. In our own work with networks, research has shown that individuals with more diverse personal networks are more likely to be promoted and succeed in their occupations. Although I’ve always thought that my own personal network was quite diverse, I received a wake-up call from the recent US elections. I was not aware of any of my fairly extensive US citizen network that were voting for Trump! So it does take a conscious effort to build and sustain a diverse network of connections. It’s far too easy to fall back to the comfortable relationships with those just like us.

But diversity alone is only a pre-condition to high performance. One must be able to exploit the diversity in one’s network to actually deliver the superior results that it promises. In a previous post we introduced our network performance framework, which identifies a balance between Diversity and Cohesion in networks, for maximizing performance:


In this framework we identify that high performers are those that can effectively balance their diverse connections i.e. identifying high potential opportunities, with their close connections, with whom they can collaborate to exploit those opportunities. From our project consulting experiences these people are either recognised as organisational ‘ambassadors’ or are completely invisible i.e. the quiet achievers. The fact that we find so few people in this quadrant is testimony as to how hard achieving this balance can be.

UGM Consulting explores this tension in their recent article on Innovation and the Diversity Paradox. They nominate the following attributes for those diverse networks that can successfully exploit the opportunities that they identify:

They have a sense of shared common goals and purpose;

  1. They know how to genuinely listen to each other, seeking out elaboration and novel combinations;
  2. They have high levels of mutual trust, so speaking up and disagreements can be had, risk free;
  3. They have the skills to constructively explore alternatives and agree on a direction; and
  4. There exists a strong co-operative atmosphere at both the team and enterprise levels.

For leaders this will mean actively enabling or creating such conditions. For the individual it could boil down to simply developing a diverse network that you actively consult with.  At times you may leverage these relationships by enrolling others in selected joint activities, to bring about positive change in your own areas of influence.

Top Image credit:

Does your Community have a Key Player Risk?

Key Player Blog

SWOOP: Key Player Index

An important characteristic of networks is that some individuals are more important to the performance of the network than others. In fact, if we were to plot the relative influence of individuals in a network, the degradation from the most influential to the least follows a power law distribution. This means that the level of influence between the most influential members and the least influential reduces exponentially; emphasizing the importance of these few selected influencers in a network. Networks that have just a few key influencers are clearly at risk if one or more of them were to leave the network. So how can we tell how open your community is to a key player risk?

This post continues the series of deeper dives into the specific measures included in the SWOOP Collaboration Framework #swoopframework. We have previously addressed individual behavioural personas and the important social cohesion measure.

How is this Measured?

The key player index is a measure of the degree to which a network is reliant on a ‘selected few’. To compare networks we measure the proportion of members that are responsible for 50% of all connections. The higher the proportion, the higher the key player index is and the lower the key player risk is. The range of scores from our 20+ benchmarking sample is between 4% and 12% for online communities, with a mean score of 6.4.


What we have ascertained from our online networking studies is that online communities are much more susceptible to key player risk than off-line communities/networks. This may potentially be attributed to an existing ‘digital divide’, where by only a proportion of community members choose to be active online. Alternatively, it could simply be the online medium makes it easy to attract a larger, only marginally active, membership. That said, we think that the relative scores are still a good indication of key player risk.

What should this mean to you?

If your community/network has a low key player index, meaning a high key player risk, it is important to start to address this by encouraging more members to act as hubs in the network, by actively connecting others. If you notice that selected individuals are doing all the ‘work’ in keeping the community active and vibrant, start trying to lend a hand. If you are one the ‘selected few’ key players, try and encourage others to join you and become more active in connecting others. Perhaps ask others to host online events or initiatives as a way of broadening the community leadership responsibilities and increasing the visibility of others.

In summary, a strong, sustainable community has built in redundancy, so that it can remain active, vibrant and productive, even if some the key players were to leave or be absent for an extended period. By ensuring that your community has many hubs and/or alternative sources for brokering and connecting the community, the longevity of your community will be more assured.



How Cohesive is your Community?

Cohesive community

SWOOP: Mean 2-Way Connections

Social cohesion is synonymous with ‘community’. Intuitively we experience social cohesion when we participate in high performing communities. Experienced ‘networkers’ lead these communities. New members are made to feel welcome. Community objectives are met through active engagement between members. High performing online communities are a fertile field for knowledge sharing amongst its members. While qualitatively we can experience and differentiate a good community from a poor one, what measures are available to assist leaders in monitoring social cohesion in their communities? How can these measures be used to help grow social cohesion?

This post continues the series of deeper dives into the specific measures included in the SWOOP Collaboration Framework #swoopframework. We have previously addressed individual behavioural personas. The Mean 2-way Connections measure is community wide, and our measure for social cohesion.

How is this Measured?

The Mean 2-Way Connections measure calculates the average number of reciprocated connections each community member has.  An example of a reciprocated connection created is if say, you reply to a post by person A and then person A replies to a post of yours. A community rich with members having a high number of two-way connections is going to be a highly cohesive one. On the other hand, a community with a low Mean 2-Way Connections score will have created few sustainable relationships between its members and therefore much less cohesion.


The Mean 2-Way Connections is our measure of social cohesion. For the majority of communities, the higher this score, the better. A high score means that it is highly likely that strong relationships are being developed amongst the membership. We know that strong relationships underpin trust, and with trust we get speed and tangible results, as Stephen Covey elegantly represents in his book ‘The Speed of Trust’. Where we have been able to compare online communities/groups within the same enterprise on this measure, we have found qualitative reinforcement that the more cohesive a community is, the more value that it is creating for its members and the enterprise.

Importantly, the social cohesion measure was the result with the greatest spread from best to worst in our benchmarking studies. The following chart shows the relative spread of results amongst a selected set of 21 enterprises:

Standard Deviation.png

In essence this chart indicates the Mean 2-Way Connections (social cohesion) is the measure that most differentiates good from poor performance. It is also therefore the dimension that offers the most potential for improvement.

There is however an upper limit for social cohesion within a community. This point is where a community reaches, what we commonly call ‘group think’. In these circumstances, highly cohesive communities become immune to ideas from outside the community. Innovation stagnates, and while the community may still be successful, it will find it increasingly hard to deliver further improvements, without introducing more diversity within its membership.

What should this mean to you?

As an individual, one should always be looking to maximize the number of reciprocated relationships one has. Having a high number of 2-way relationship connections should result in your being seen as an ‘Engager’, the most productive behavioural persona. Recall that Engagers are able to effectively manage their ‘give-receive’ balance. They become the glue that binds a community.

As a community leader, a high average 2-way relationship score means that your members are actively engaged in community activities and delivering value for the community and its sponsors. On the other hand, a low score indicates poor social cohesion and therefore much work to do. To build up social cohesion in your community, you need to start with identifying a few important tasks that selected groups of members can actively work and collaborate on. Traction is gained around these activities and value stories are shared amongst the broader community. You will see the 2-Way Relationships score grow, as the membership becomes more engaged in its activities.

In summary, social cohesion and its specific measure of Mean 2–Way Connections is seen as, arguably, the clearest measure of a successful community.  Social cohesion is synonymous with community. Our benchmarking studies have shown that it is also the measure that most differentiates excellent from poor community performance. For those communities exhibiting poor social cohesion, the task is to develop activities that encourage members to reciprocate. There is however price for too much cohesion; and that is a lack of diversity and innovation, which could lead to eventual stagnation, if not managed properly.




Can Collaboration Personas work with Sports Teams?

KEEPIA View from the Top – David Thodey Interview - Part 1- Why Enterprise Social Networking- (3)

Professional sport these days is rife with in depth analyses and statistics on player and team performance. Players are now often equipped with wearable devices to monitor their health and fitness by the minute. Increased betting on sport has added a whole new dimension to the desire for predictive analytics and anything that might assist the punters in predicting the result of a game.

What makes sport such an attraction to a large percentage of the world’s population is that despite the science that is being brought to sport, there is still significant uncertainty in the results. We all applaud the times when the ‘team of champions’ is upset by the underdog ‘champion team’. Who can forget the US amateur ice hockey team overcoming the all-conquering Russians at the 1980 Winter Olympic games? Equally memorable is the failure of the all-conquering US Basketball ‘Dream Team’ at the Athens 2004 Olympics. The search for that ‘X-Factor’ that drives the champion team to overcome the odds is the modern coach’s dream. In this post we will explore an area of sports analytics that is largely under-exploited.

For the novice sports punter the first port of call for team intelligence is the player profiles. The unwritten inference is that if you are well informed about the players and their individual strengths and weaknesses, then you will be able to predict team performances well. For example, if we go to the FIFA statistical support site for the 2014 World cup, this is what we find:

Fifa table

Again, the majority of the statistics profile individual player performance; how many minutes they played, goals scored, passes made, free kicks taken, tackles made, even which parts of the field the player occupied.

Incongruous howeveSwoop teamsr is that since football is a team game, why is there so little rec
orded about how they collaborated with each other on the field? We regularly see the NBA coach using small whiteboards to identify the passing structure wanted.  I had
to dig into the FIFA data to find some evidence of passing records of how the players interacted with each other i.e. connection data. I found it hidden away in the ‘Passing Distribution’ statistics. So what might this largely overlooked data provide us with? Can the network data provide us with the missing intelligence needed to predict that ‘x-factor’ that successful teams are blessed with?

Our analysis technique of choice is social network analysis (SNA). Traditionally, SNA is used to identify relationship networks in communities or large enterprises. Its application to sport is novel but not unprecedented as this academic study shows. The study used FIFA 2010 world cup statistical data and traditional SNA centrality scores to assess team performance. We decided to build on this by using similar data from the FIFA 2014 World Cup site for the game between eventual champions Germany and Portugal. We chose this game as Germany were convincing winners and therefore there would be a greater chance of our analyses identifying an ‘x-factor’ difference. Rather than use traditional SNA centrality scores, we decided to use the behavioural SWOOP personas that we designed to characterize collaboration behaviours of staff participating in enterprise social networking (ESN) platforms. The five personas are Engager (Linking), Catalysts (Energizing), Responder (Supporting), Broadcaster (Telling) and Observer (Watching) and we felt that they could be mapped to the following behavioural archetypes, that we might see on the football field:

Behavioural Persona Classifier Football Player Characteristic
Engager Roughly equal number of passes received as completed passes made Someone who is a central connector linking plays
Catalyst Receives more passes than completed passes made Someone who wants the ball and pushes the team forward
Responder Completes more passes than they receive (assumes they make more intercepts) A good support player; cleans up the plays
Broadcaster Completes more passes than they receive (assumes they take free kicks and corners) Takes the big kicks but does not back up or intercept that much
Observer They have a low level of participation Usually a bench player, but perhaps on the field, does not get involved that much.

Our SWOOP Personas are classified according to the posting patterns of the ESN participants. The order that they are shown in the table above is also what we believe is the order of most positive impact on collaboration performance. For example, an engager is able to balance the number of posts, replies and likes that they make with those that they receive. We see the Engager as the strongest persona for collaboration. A Catalyst might be the target for many passes. They may take more risks in pushing the ball forward and therefore more passes might go astray, leaving them with an excess of passes received over successful passes completed. A responder will make more passes than they receive, perhaps because in their ‘cleaning up’ work; they may intercept more passes from the opposition, leaving them with an excess of passes made over passes received from a teammate.  A Broadcaster also has an excess of passes made over passes received, but perhaps their passes come more from fixed ball situations like free kicks or corner kicks, rather than intercepts. Finally, the observer characterises someone who really isn’t in the game that much.

With these characterisations in mind, we took the passing distribution data from the Germany Portugal match into our SWOOP SNA analysis:

Passing data

The passing distribution shows the number of times a pass has gone from one player to another. The network is therefore directional as shown in the above matrices. The number of passes between two players can indicate strength of the connection between those players. We can represent these passing patterns in a social network diagram (sociogram):

Germany Portugal

The thicker lines relate to number of passes. The layout algorithm clusters more frequent connectors closer together physically. Qualitatively, the sociogram does appear to show Germany as a tighter outfit, in terms of their passing patterns, than Portugal. However, we need to look at the quantitative data to be sure of any marked differences:

Germany     Portugal    
Player Minutes Persona Player Minutes Persona
NEUER 94 Responder PATRICIO 94 Responder
HOEWEDES 94 Responder ALVES 94 Responder
HUMMELS 74 Catalyst PEPE 36 Responder
KHEDIRA 94 Engager VELOSO 47 Catalyst
OEZIL 64 Engager COENTRAO 66 Responder
MUELLER 83 Catalyst RONALDO 94 Catalyst
LAHM 94 Engager MOUTINHO 94 Catalyst
MERTESACKER 94 Engager ALMEIDA 27 Catalyst
KROOS 94 Catalyst MEIRELES 94 Responder
GOETZE 94 Engager NANI 94 Broadcaster
BOATENG 94 Broadcaster PEREIRA 94 Catalyst
SCHUERRLE 29 Catalyst EDER 66 Catalyst
PODOLSKI 10 Engager COSTA 47 Catalyst
MUSTAFI 19 Responder ALMEIDA 27 Engager

We can see that the tighter passing patterns of the German team is confirmed by the higher number of Engager personas (6 vs 1) and even then the Portuguese Engager was a substitute playing the least minutes. The Catalyst persona is the next most valued in our view and on this dimension Portugal has 7 vs Germany’s 4; suggesting that Portugal played a more expansive, yet more risky, pattern of play. The actual result was a 4-nil win to Germany.

We also wanted to do a similar analysis for the world cup final game between Germany and Argentina:

Germany     Argentina    
Player Minutes Persona Player Minutes Persona
NEUER 129 Responder ROMERO 129.00 Broadcaster
HOEWEDES 129 Broadcaster GARAY 129.00 Responder
HUMMELS 129 Broadcaster ZABALETA 129.00 Broadcaster
SCHWEINSTEIGER 129 Broadcaster BIGLIA 129.00 Engager
OEZIL 124 Engager PEREZ 87.00 Catalyst
KLOSE 89 Catalyst HIGUAIN 79.00 Engager
MUELLER 129 Engager MESSI 129.00 Broadcaster
LAHM 129 Engager MASCHERANO 129.00 Broadcaster
KROOS 129 Engager DEMICHELIS 129.00 Catalyst
BOATENG 129 Engager ROJO 129.00 Broadcaster
KRAMER 30 Responder LAVEZZI 47.00 Engager
SCHUERRLE 98 Broadcaster GAGO 41.00 Catalyst
MERTESACKER 4 Observer PALACIO 49.00 Engager
GOETZE 39 Catalyst AGUERO 82.00 Catalyst

In contrast to the Germany-Portugal game, the ‘Engager’ score was much closer (5-4), though two of Argentina’s Engagers were substitutes playing less minutes. The score was a very narrow 1-nil win to Germany in overtime. Compared to the previous game, there were also more Broadcasters on both sides. We surmised that broadcasters may start play from fixed ball positions i.e. they make more passes than they receive. Perhaps this reflects the stop-start nature of the final. Overall though, there is some evidence that team success might be predictable using relationship derived personas.

While we find the results interesting and intriguing, for us this analysis is a fun diversion; and therefore we are careful not to claim too much in terms of groundbreaking research. That said, we are looking to have our on-line personas identified with contexts beyond the online social networking field, so we think this analysis qualifies.

We close this article with some food for thought:

  • How much are sports teams really like work teams? There are defined roles and expectations in both. Sports teams however have clearer success criteria.
  • How much is the persona related to the role in the team versus the individual playing style?
  • How much might the personas change based on the context of the game and game specific tactics i.e. both in sport and work teams, how adaptable can the members be from their ‘preferred’ behaviour persona?
  • And the big question. Can relationship analytics predict the x-factor in team success, independent of player specific profile information?

Of course much more research work would need to be done. But we are happy to have been able to provide another example of how collaborative behaviours can span many contexts and not just be online specific.

Learn more about SWOOP:

Swoop Persona: Are you an Online ‘Engager’?

Are you an Engager image

This post is the first in a longer series of posts devoted to a deeper dive into the specific measures included in the SWOOP Collaboration Framework #swoopframework. We are starting with the ‘Engager’ Behavioural Persona; in our view the most desirable collaboration persona.

How Measured?

The ‘Engager’ persona is currently calculated as a balance of all contributions made against responses received. Contributions include posts, replies and likes, while responses are replies and likes. On the Yammer platforms these are the most common contribution and response types.


According to the research on the positive collaborative behaviors that contribute to superior performance, the balance of give-receive is a key indicator. In his book “Social Physics” , MIT’s Sandy Pentland identifies team members with a balance of give and receive are associated with high performing teams. Pentland goes further by using wearable social tags, he was able to identify the nature of the interactions also being short, sharp and frequent. For high performing teams, relationships have matured to the point where tacit knowledge exchanges do not require extensive explanations and justifications. Average message length could capture this dynamic, though we are yet to implement this.

We see ‘Engagers’ as the glue that keeps teams and communities engaged. They are the brokers and connectors. Without ‘Engagers’, a community or team risk disconnected conversations and therefore unproductive interactions. Is there a problem having a team full of engagers? In most instances we would say no, as this would indicate the team or community is buzzing along in a highly productive way. The exception we would make is when an injection of new thoughts or innovations are required. For that we see the need for some ‘Catalysts’ to be added to the mix.

What should this mean to you?

Based on the benchmarking we have done to date, it is not easy to become and sustain the ‘Engager’ persona. We believe that everyone should aspire to develop skills to enable them to become and sustain engager status. In this way you will have the skills to be a productive member of any team or community. It will require you to be mindful of your contribution patterns and the sorts of posts and replies that you make, that may or may not attract reactions from your colleagues.

There will however, be times where it may be appropriate for you to adopt a different behavioural persona. For example, if the team or community is looking for fresh ideas and positive change, the ‘Catalyst’ persona may be more appropriate for you, if you want to lead that change. In other contexts you may be looking to sustain interactions in a community or team, where the ‘Responder’ persona may be a positive one for you.

In summary, the ‘Engager’ persona is, we believe, the most positive persona to exhibit online and off. Those that have developed the skills to switch into and sustain this mode of interactions, will always be a sought after team or community member.



What Makes a Great Team On-line and Off?

We are witnessing a significant shrinkage in the digital divide between on-line and off-line work, as rapid digitization takes hold. The days when online work was led by the digital savvy are rapidly disappearing as even the baby boomer generation embrace social media. Our research has even identified that co-located teams use electronic means to record and share their work artifacts. So how can the movement to digital be used to assess team performance?

The management literature is profuse with models and methods for assessing teams and team performance. Traditionally team assessments have focused on the behavioural preferences of team members and the needs to accommodate diversity, or sometimes a lack of diversity, to ensure maximum team performance. The ubiquitous Myers-Briggs personality profiling emerged in the 1960’s with profiles compiled from introvert/extrovert; sensing/thinking; thinking/feeling and judging/perceiving poles. One of my favorites though is the Team Management Systems.  I have been ‘TMSed’ several times in my career. Comparing our online SWOOP Personas with the TMS profiles we can see there is a somewhat loose mapping:

Personas teams

Briefly, Engagers are able to balance contributions made and contributions received; Catalysts receive more than they give; Responders contribute more than they receive; Broadcasters are like Responders, but post more than they reply and finally Observers have too lower levels of participation to be classified otherwise. While we would happily assign ‘Linking’ to the ‘Engager’ persona, Margerison-McCann, the inventors of the TMS, suggest that linking can be a learned trait for all profiles, rather than an inherent preference. If so, we could infer that no matter what persona you display, in the longer term we are all capable of learning how to be a Linker/Engager. Margerison-McCann place a high emphasis on the connection between linking and team success. We can see the Catalyst Persona mapping to Innovating; Broadcaster to Promoting, Engager to Developing/Organising/Producing and Responder to Maintaining/Advising. The one potential mismatch at this time is that our ‘Observer’ persona is currently a catch all for those not well engaged with the platform. We might hope though that some of these observers at least might adopt an ‘inspecting’ role, if they choose not to be more centrally active. Overall, the mapping to TMS profiles gives us the confidence that over time, real-time team assessments are within reach.

Perhaps my most memorable TMS learning was when the TMS profiles were conducted in conjunction with one of those executive outdoor adventure-learning programs. We were Team blogall duly provided coloured caps that represented our preferred TMS style. We quickly learnt how dysfunctional a raft building team full of ‘Organisers’ and ‘Producers’ could be!  In the on-line world one could also envisage a team overloaded with one particular persona, might have dysfunctional effects e.g. teams full of catalysts might have trouble concluding tasks; or a team of responders and broadcasters may have trouble moving forward with team tasks. The exception may be a team full of engagers, which on balance, may arguably be the secret for team success, based on some of the more recent research described later in this post.

Instruments like Myers-Briggs and TMS and the like can only be point in time checks. They are expensive and can therefore only be done periodically, and often only with selected teams. Unlike my outdoor adventure learning experience, we don’t wear our preference profiles on our sleeves or on our head. Teams are also changing far more dynamically now, making the team bonding task even more challenging. So what can the movement to digital do to help us maximise team performance? Can we do team profiling now in real-time?

More recent research on teams is surfacing the social inclusion aspects of high performing teams.  Google’s search for what constitutes the ‘perfect team’ largely dismissed the importance of what we might call the ‘team of stars’ in favour of interaction attributes like social sensitivity and psychological safety. In common with one of our favourite studies on what makes great teams, by MIT’s Sandy Pentland, is the recognition that great teams have members who all talk and listen in roughly equal proportions. Pentland’s invention of the social tag provides a hint that we are close to assessing team performances in real time.

Recently we wrote about the SWOOP Online Personas that we had designed and tested on live Enterprise Social Networking sites (Yammer). We also noted that ESN use was yet to move down to the team level; something that we strongly recommended should happen if true collaboration performance changes were to be achieved.

To move the agenda forward with on-line team profiling, we developed a collaboration measurement framework that acknowledges team assessment methods now need to move from the traditional off-line survey based assessments to on-line and real time. It also recognizes that team assessments need to move from a focus on the individual profiles to personas that capture interaction behaviours. While we will save a review of this framework for a later post, we can say that the framework has been mostly used at the enterprise level, though where it has been applied to smaller  ‘team sized” organisations or even for individual teams inside a larger organisation, we can clearly see the positive impact of moving collaborative platforms and their associated analytics down to the team level.

We think that the movement to on-line team profiling, leading to higher team performance is imminent. Team profiling assessments will not have to wait until outcomes are achieved and measured, but will monitor team behaviour patterns in real-time, to allow adjustments to be made in time to ensure good outcomes are achieved in all circumstances.

So back to our question “What makes a great team online and off?”  I hope we have been able to convince you that it’s the same things, no matter whether you are operating largely on-line or off.