When I was an editor on Global Strategic Trends, we produced a network diagram of future trends that was so wide ranging and colourful we called it 'the sneeze'. It was a complex mixture of nodes and connections that was frankly, daunting even to trained analysts let alone the poor drafting team who had to, somehow craft a narrative from something that looked like this?
Life is not linear
So, how do you communicate data from a complex mixture of concepts and connections? To answer this, it's worth thinking about how we currently communicate narratives.
People like stories and (in Western culture especially), they like their stories to be linear. Stories tend to follow a very basic structure of a beginning, middle and end. The perils and pitfalls a protagonist experiences on their 'journey' is all part of a linear progression to an end point that illustrates the premise of the story.
Similarly, most communications in real life follow well-established linear structures. For example, academic papers tend to consist of an introduction, a method, results and then conclusions. Again, they work linearly to a point as do most policy papers and shareholder reports.
But, when you're looking at network diagram, understanding how to resolve the data into information and craft a central narrative is tricky. The best thing you can do is be aware of what it is you actually looking at.
Communicating networked information
The challenge with a network map is that you have to use it at the right point in your drafting. You need it before you try to write a structured report. Think about it - when you're studying, at what point do you make a mind-map? You do it at the beginning of your project.
The reason for this is actually quite simple. The point of the network map is to show you as full a picture as possible of a problem or idea. By mapping concepts in a non-linear, network map you start to get an awareness of the full range of issues.
Additionally, if your map is weighted, you can also get an understanding of most frequent topics and interconnections. For example, you might be able to understand the most frequently occurring, or most popular idea. Additionally, you might also be able to see which particular nodes are the most connected? What does this tell you? Does it show that a particular individual is the most important expert in a particular field?
The real power of using a map to start your drafting is that you can then base any linear communication you want to produce on the structure you've seen in the data. For example, using a network map as your start point you could produce a linear output with the following structure.
1. Introduction - overview of the network map and how it was produced.
2. Most significant nodes - what are the largest points on the map and why?
3. Most significant connections - what do the connections between the nodes reveal about the problem?
4. Outliers - what strange, low frequency, unconnected issues are there around the map?
If you approach your map in this fashion you'll probably be able to then focus down on the main issues that fall out of it and these can be presented as the big, strategic deductions for you assessment.
Working with the data you have collected to generate your structure can be a little strange at first, but it can reward you by being a real reflection of the research you have collected. It also, lets you make a bit more sense of something that looks somewhat bewilderingly organic and rather alarmingly like a sneeze!