If you want to do meaningful work, forget your expertise.

Expand your breadth of knowledge instead.

Scott Wagers (Train World, Brussels)

Being an expert seems important, but its meaningless.

Its you ability to work with others that makes all the difference. Multi-party collaborations are where the greatest and most meaningful achievements happen.

• The Apollo moonshot involved more than 400,000 skilled workers

• The Human Genome Project was 20 universities and 1,000's of researchers

• The Apple iPhone requires parts and resources from 50 countries

• The Strive nonprofit in the US bringing together 300 community organizations to make substantial gains in public education where others have failed

Small and simple collaborations are just not that powerful.

Multi-party collaborations, while potentially much more powerful, are however constrained by collaboration’s evil step-sister, competition.

Merging ideas and aspirations

It requires skill to merge the ideas and aspirations of multiple people, particularly the high-powered people you need to do truly meaningful work. If you don't merge interests well, your multi-party collaborations will end up mired in a swamp of competitive acrimony.

There is one thing that makes it much easier to get high-powered people to collaborate. Knowing something about their fields of expertise. We tend to like and trust people who have knowledge we can relate to. Plus, effective collaboration only happens when we understand each other.

I have seen many misunderstandings and sometimes outright conflict when high-powered people try to collaborate.

It’s because communication between people with different expertise is like trying to get directions from someone in a foreign country but they don’t even realize that you are speaking a different language. Of course, there is frustration and conflict.

Mixing researchers and bioinformaticians

One of the best examples comes from discussions between medical researchers and bioinformaticians.

A concept that is seemingly simple always leads to a great deal of confusion. That is the concept of a data model. Often researchers don't know what is meant by the term data model. You will get responses like: "You mean the variables?" The variables certainly are a component of a data model, but it is also how the data is structured.

For example, if you have a study with just one point of measurement the model is different than if you have follow up visits through time in which case a particular variable needs to be represented in such a way that it can be analyzed in comparison to other time points. Another feature of a data model is what parameters do you have describing each data point.

The data model matters because when you go to run the code that will analyze the data, the structure of your data model will make certain types of analyses more or less difficult.

It is a classic problem of merging disciplines because the researchers who understand how the data will be used need to help define the data model, but when they don't understand what a data model is you can go around in circles.

Science’s lost in translation problem

This lost in translation problem is particularly true for science. Scientists are well known for their use of obscure words and difficult concepts. Scientists do this because they are trying to not leave out any subtle and not so subtle complexities.

However, complexity and clarity have an antagonistic relationship. A concept cannot be clear to a non-expert and complex at the same time. It is a sort of clarity-complexity uncertainty principle.

Does that mean that to effectively collaborate we need to become experts in the fields of all of our collaborators?

Fortunately, no. Researchers do not need to become bioinformaticians and bioinformaticians do not need to become researchers.

Different types of knowledge

What most people do not realize is that there are different types of knowledge.

For a breadth of knowledge it is more about being literate, but not necessarily an expert. We have to understand the issues and know why they are important.

Guiding the design, development and delivery of big medical research projects across multiple different research areas, I have seen this problem over and over again. In fact, the above data model example comes directly from my experience.

I started out simply deferring to the experts, trusting that they would find a way to understand each other and merge their interests.

But then I noticed after I had learned something about bioinformatics in a project, it was very useful in helping to merge the interests bioinformaticians and researchers in other projects. I was better able to be a bridge between the researchers and the bioinformaticians. I was also better able to understand what was feasible and where connections could be made.

So, now, the first thing I do when I start to support a group in the design of a big project is look to gain boundary-spanning literacy. It does mean having to have a process for learning what is most relevant in an efficient manner. To do this I created an approach to learning, but first I had to understand something about knowledge.

The T model of knowledge

There is a model of knowledge called the T model. You have some people who have deep expert knowledge, which is likely the vertical part of the T. Others have knowledge that spans between experts - the horizontal top of the T. In reality, it is like a bunch of Ts combined together with one long horizontal top of the T stretching between multiple areas of deep expertise.

Scott Wagers

In network theory, this is called boundary spanning. It would be easy if we could just have a few boundary spanners and that would be enough.

But what I have seen over the past 19 years is that the need to have people with diverse expertise talking and working together in very substantive ways has increased markedly. With the rapid pace of new technology becoming available this is likely to just continue to increase. Some would say, particularly with the emergence of AI that can provide the in-depth expertise that now the role of humans is to be connectors. The rapid pace of knowledge expansion means that to stay relevant and achieve something meaningful we must have both depth and breadth to our knowledge.

You now understand that by developing more breadth in your knowledge you will be more equipped to effectively bring together people with different expertise. Next, I will show you how to build up the breadth of knowledge you need 10 x faster than you might otherwise do it.

This will make the power of multi-party collaborations available to you. Those types of collaborations are how you will be able to achieve something meaningful.

How does one build up the breadth of one’s knowledge, expanding your boundary-spanning literacy?

It is simple - learn.

But, of course, we all know how long it has taken us to become experts in what we know. However, this type of learning does not need to take that long or require that much effort as long as we take the right approach.

Here is a easy to implement 7-step process for learning a topic efficiently based upon a few principles of knowledge and learning that anyone can apply.