Apr 062017
 

As a unit of measure, a headful is not consistent. Not consistent between individuals, not consistent in any one individual from day to day. I will explain what I mean by the term, how it varies, and how to stretch it.

Firstly the definition. The Collins English Dictionary defines it as ‘the amount a head or brain will hold’. I use it specifically with reference to the understanding of a problem.

So how does it vary? When presented with a new project, I try to understand the big picture, the whole project. There is a level of abstraction at which that is possible. As time goes on, and I become familiar with the problem and the previous headful of information becomes compacted, making space for more detail. This is closely aligned to the process of turning data into knowledge and understanding. The initial headful for any given problem also depends on the other things already in my head; if I can closely align the new data with previously compacted data, then I can take in more at the first go. Each individual has a different capacity for a given problem space.

Note: my mental model of my mental model has diggers and dumper trucks and rollers and rotavators. While digging for the wanted information, there will often be surprising additional facts uncovered leading to interesting juxtapositions. I think a beautifully clean warehouse of a mind, while more efficient at retrieving things, might be rather dull and predictable.

If this is such a loosely defined quantity, then how is it helpful? First there is the appreciation that someone new to a project cannot understand all the detail across all the project areas on day one. We need to abstract the whole to a level that is comprehensible, and add detail once that framework is in place. Second, by understanding that this headful is not fixed through time, we can look at ways to supplement it, aid the compaction, and improve the ability to manipulate the data.

Tools. Pause for a moment and note what comes into your mind when I say tools. Tools come in many shapes and forms. The relevant ones here are:

  • Thinking tools – techniques and shortcuts
  • Data capture tools – databases, data storage
  • Data manipulation tools – modelling tools, more sophisticated search tools on the captured data

Data storage can actually be a bad thing for building mental models. Once something is written down and documented, there is a tendency to forget it. Many people use lists in some form to clear the clutter from their heads and make space for creative thought. Data storage is, however, essential for the sharing of information across teams. We have to ensure we write and remember, not write and forget.

Thinking tools allow us to fit the data to a pattern and gain understanding. This might be by asking certain questions, or by making certain associations. They involve manipulating the data in the headful, shaking it up, and allowing it to settle back into less space.

Data manipulation tools are where the real increase in the unit of the headful can be seen. This allows us to use the thinking tools across a wider data set, to find the next piece of the puzzle that will make sense. These tools also allow us to share and merge our headful of data with the other team members’ headfuls.

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