Its not the same thing – the 3 types of collaboration

A year or so ago, i found myself in a (slightly heated) discussion around what the key enabling factors for collaboration were. Somewhere along the way, I discovered (as often happens when one is debating with ones spouse, or at least my spouse) that we were actually not talking about the same thing.

I was talking about helping teams to work together. He was talking about helping people who may not know one another connect as their expertise becomes relevant to one another. Oh. Well those are very different things, and while some enabling factors are similar, these two activities actually have rather different requirements both culturally, organizationally and technologically.

After working this issue for a while, I’ve labels the three major types of collaboration. This categorization seems to work for most people, and I’d love to hear what you think as well. I believe that it is critically important that we have a shared language to discuss and describe these different concepts if we are to make any progress toward enabling them in organizations. We cannot become sophisticated and make progress here if we cannot define the terms. So here is my take on this, and I look forward to your refining input.

Collaboration refers to a cluster of 3 types of activity – they are often interdependent and linked, but they are distinct in what they can achieve, and what is required to enable them.

1 Creative Collaboration.

Creative collaboration is collaboration that’s intended to create something. It is goal-oriented, and has a defined team (though stakeholders may come and go) that is responsible for delivering that product. Examples here are a product team, a legal team, a team responsible for an RFP, or a marketing launch, or developing a product, or a corporate acquisition.

The objective for this type of collaboration is to be able to achieve what an individual can not, either because its too much work for a single individual, or, as is more common, it requires a multitude of skills or perspectives to achieve.

The outcome that we’re concerned with is a factor of the team’s productivity. We want the outcome to happen as quickly, cheaply and with the highest possible quality, and collaboration has been shown to improve each of these dimensions. [citations]

What we need to do to encourage such collaboration is make it easy for teams to form, communicate, get organized, contribute, aggregate and iterate on work. I talk about this in depth in a recent post “Is Collaboration enough for Productivity.” Technology helps enormously here by providing shared workspaces, a variety of communications tools (wikis, document management, discussion forums, instant messages, etc), which, if you’re lucky enough to have well designed software, accelerates the rate at which people can get work done, and removes barriers like geographical and organizational distance.

The key cultural requirements for success for such teams are (and forgive me if you’ve heard this before) 1) a shared sense of mission 2) mutual respect 3) trust 4) a commitment to continual improvement. I’ve discussed these elements elsewhere on this blog, so I’ll spare you the details right now.

2. Connective Collaboration – its not the wisdom of crowds, its the aggregated wisdom of individuals.

This refers to connecting with a broader community – the organization as a whole, or even more broadly than than. You may not know most of the people in this community. The goal of this type of collaboration is to connect dots – find expertise and resources as you need them. Discover unexpected relevance, connections or insights, and maximize the chances that information, resources and expertise find the places that they’re meaningful or critical. (I’ve written about this in several places too – “Intel clear on ROI of Social Media” and “ Is collaboration enough to connect-the-dots?”). While there are examples of this type of requirement everywhere – science, healthcare, art, strategy, problem solving of nearly every kind – the most notable examples these days are from the intelligence community – is it possible that the intel community could have identified and connected the dots to warn them of the 9/11 attacks? The Christmas 09 underwear bomber? The answer is – maybe. There’s a lot involved in that problem, and I won’t get into all of it here, but recognizing patterns of droplets of  information and activity in an ocean of activity is not easy. The goal right now is to maximize the odds.

Connective Collaboration requires a broad, loosely connected community that can maintain awareness of activity, and ideally, technology that helps them find, discover or get pinged about relevant information, resources, insight and expertise –  that they may or may not have been aware of – elsewhere in the system. Status and microblogging have proven surprisingly useful here to build ambient awareness of what is going on  in the organization. It is also vital, however, to have communication and work indexed and searchable to be able to find those nuggets of connection. Semantic analysis, and statistics also have much to offer (and far to go) here.

3. Compounding Collaboration – Standing on the shoulders of giants.

The purpose of compounding collaboration is to ensure that whatever our endeavor, we are leveraging, to the greatest extent possible, the work that has been done already. Even if it is only to show us what to avoid. To the extent that we can do this, we can constantly compound and extend our capabilities, productivity and agility. There is nothing that can compete with this sort of dynamic, and it in competitive situations it trumps nearly any other dynamic (think of compound interest on your money – you cant catch up with an early, strong start).

To achieve this, we need to be able to capture work. Work is not only about documents. Work is what happens when you’re creating those documents (or other products) – what resources were used? What questions were asked? Who answered them? How did you overcome obstacles? What were the false starts or poor assumptions? What processes were followed?

The beauty is, that if you’re using technology to support Creative Collaboration, you should be capturing all this, so that the next people coming through can learn from what you achieved – or failed too. (cultural note – you need a culture where its ok to fail, and it is a respected part of the learning, discovery and continual improvement process).

The field of Knowledge Management was devised to support this type of efficiency and collaboration. But the trouble with KM as it was defined in the 1990s , is that knowledge capture and dissemination was separate from the work itself. It is something that must be undertaken and explicitly referred to. The implications of this are many, but it usually means that only the most formal, documented and recognized knowledge is captured. That a vast majority of insight is lost, and that what is captured is only found if someone explicitly thinks of looking there. In other words, because the prior generation of knowledge management techniques were largely divorced from the act of work itself, they were inefficient at both capture and dissemination of knowledge.

The new age of collaborative technologies should fix this, and make knowledge capture and transfer much, much easier.

There are other issues here as well – onboarding and training of new people.

I was recently part of a discussion where people were talking about the twin issues of senior people leaving and junior people coming up to speed. I’m a firm believer that one of the most tried, true and effective methods of transferring large amounts of knowledge is through apprenticeship – (try learning to make a pie crust from a book, vs doing it  with a friend or relative who already knows how). The transparency and team environments that good collaborative technologies can create enable apprenticeship on a broader scale than ever possible – and the beauty is that it goes both ways. The new kid can teach the old fart some new tricks too – without any loss of face on either side. Transparent environments are tricky and imperfect, and extremely sensitive to culture and organization – but they are also the most effective learning environments there are.

So there you have it. Deb’s basic taxonomy of collaboration. Thanks, Ken, for pointing out that I hadn’t pulled this together in a single post before. And thanks in advance to you, for your feedback so that this can be further refined and increasingly useful.

Is Collaboration Enough for Knowledge Management?

In the last couple of weeks, I’ve written (dashed-off is more accurate) about the relationship between collaboration and team productivity, and collaboration and the ability to connect the dots.

I’m working to ensure that we move the conversation away from collaboration per se, to what we’re actually trying to achieve –

• We want to increase the productivity of knowledge work.
• We want to solve hard problems.
• We want to ensure that we can leverage the collective intelligence of our organizations.
• We want to leverage the work, expertise and assets we have.

That last bullet there is what has traditionally been called Knowledge Management.

Knowledge Management has been in a tough spot for the last couple of decades. Its been identified both as a strategic imperative, and largely a failure.

There are three key reasons for this.

1. First generation knowledge management captured only formal, not tacit, knowledge.

I previously swore off using the term tacit knowledge because people don’t get it and think that its too abstract a concept. For the purposes of this discussion, lets just say that tacit knowledge is the stuff in the organization that people know, but haven’t written down in a formal, organized fashion. It its most basic form, this is the “does anyone know if we’ve ever had a customer who needed x?” or the “does anyone know where to get y” type of information.

It is well known that the vast majority of knowledge in the enterprise is tacit.

2. First generation knowledge is not part of any natural workflow, but an afterthought.

Its an additional chore. It doesn’t help me, so I don’t always practice perfect citizenship and take the extra time to ensure my work is properly catalogued.

3. Usability is poor. I can’t find what is useful for me when I need it.

Obviously defeating the purpose.

So – how does collaboration, or more specifically, social collaboration help solve the knowledge management problem?

1. Social collaboration puts more work and communication in shared, digital form.

By work, I don’t just mean documents. I mean discussion, question and answers, comments – the types of things that often either happen over the phone, or in the black hole, commonly known as email. Because these less formal, more fragmented items are captured, indexed and searchable in conjunction with the more formal knowledge captured in documents.

2. Knowledge capture becomes an organic part of work.

The greatest part of these systems is that I do not have to do anything extra to contribute to the knowledge base. The collaboration platform just absorbs what I do as the course of my work – comment on documents, ask and answer questions, revise, collect feedback, collect links and resources, etc. THIS is the critical point – knowledge capture – the key to knowledge management is organic and automatic.

3. Ease of Use

I’ve called social media in the enterprise a Trojan horse. Its raising the bar on usability for enterprise apps (and how we approach work – but that’s the next post). Social Collaboration tools (good ones!) are well designed so that people actually want to use them. The benefit far outweighs the trouble of using them. So they actually get used. Knowledge is actually captured, and can be meaningfully found.

4. Finding and connecting.

So what about the case when there’s knowledge and resources out there, but you don’t know it? See the last post on Connecting the Dots.

Even better – if your collaboration system is a good one (disclosure, this one is my baby right now), when you search, you’ll not only find the content, but the people who are most actively contributing content in that area.

Now the obvious issue – if I build it will they come? No. They won’t. To be successful in collaboration there must be a happy marriage between understanding your business objectives, the technology, and perhaps most importantly, the culture of your organization. That will be the topic of my next post. If you’re curious about some of my thoughts in this realm prior to my next post,  you can check out a little 10 minute  webinar on the culture of teams that I did (its not my finest – lots of uhms and ahhs, but it makes the point, consider it an early rehearsal) or this slideshare below:

Is collaboration enough to connect-the-dots?

Connecting the dots is what we call the problem of finding various bits of the answer from various people who may not have been aware of the question to begin with. I described this more deeply in a previous post on the intelligence community’s connect the dots problem:

Imagine 10,000 people on 17 teams, working on 100,000 jigsaw puzzles. Now imagine that some of the pieces have been randomly distributed among the other players. Nobody knows how many pieces are in each puzzle. And some pieces may be missing entirely, or fit into multiple puzzles simultaneously. Each person has a limited number of puzzles that they are aware of, and some may be working on the same puzzle without realizing it.

They need a system that will make it possible for people to know what pieces the others have, for the pieces themselves to find the holes they might fit into, and – here’s the odd one – the holes can describe themselves to the pieces. This one needs one with some blue in it, or a fairly oval shaped connector.

Why do we need to connect dots?

Problem solving – I’m working on a problem. Say its how to satisfy a customer’s tricky technology issue. Say its figuring out how to keep explosive underwear off of airplains, or how a particular frog species might predict global warming effects, or how a certain bacteria may solve the worlds energy crisis, or competing against another technology company in a market sector. These can be extremely difficult problems. They all depend on being able to form a complete picture from a diverse set of information, understanding the implications of that picture and being able to act on that insight.

So. Suppose I’m looking for a cure for cancer. Some other person in my company (or not) is studying the effects of pseudonameotryxlate on diabetes. She notes that this drug seems to limit hair growth, but she’s not certain why. I notice that one of the key processes in this cancer progression is the same process that is involved in hair follicle generation. How am I going to know about the study on diabetes?

What if I had a system that would tell me about things that had to do with hair. What if I had a general awareness of surprising outcomes that people chatted about?

What if I were trying to form a strategy to compete against a certain vendor. And one of the sales reps got a comment from a prospect about that vendor that told us something we didn’t know.

What if I’m on a services team, struggling with some exotic server configuration that I’d never seen before – but someone in my org has….

So what are the components that would be of value to us here (so, so, so – why do I write this way?).

First – you need a system which will capture small bits of knowledge, not just formal documents or even wikis. Fortunately, social media tools are (can be) nicely designed to facilitate and capture question and answer, comments, remarks and corrections. In a good system, these will be indexed and retrievable.

Second – you need a system that promotes some level of ambient awareness – twitter/yammer type capability is very helpful here, as you see a (somewhat self filtering) stream of activity go by that you never really have to deal with, but you’ll notice when something critical pops up.

Third, you need a really, really good search tool. This search should not only identify content, in the form of docs, comments, Q&A and wikis, but should also identify people who may have generated that content, and communities, projects or networks where that activity is taking place.

Fourth- you need to let technology work for you in the form of recommendations based on semantic and statistical analysis. Both the – “these concepts are similar and therefore you might want to check this other one out” idea, and the amazon recommends concept “people who read or write about x also find y interesting”.

In this way you are maximizing the flow and availability of information in your organization, and allowing it to be filtered by a) yourself b) your network and c) technology – which is a winning trifeceta. With you, your network and your technology working in complementary fashion to find information most valuable to you, you have maximized your chances of finding what you need to know and which you may not know to ask about.

A social collaboration system should have each of these components, leveraging the productivity focus of collaboration into a refined ability to connect the dots.