There have been many trends in the data and task management world, from day planners, to GTD, to bullet journals, to the PARA method – and many more. This article takes a look at PARA in particular, but also pulls back a bit as to why these various systems keep popping up, what values they offer, and why we will keep seeing new such systems for a long time to come.
The starting point is the same for everyone: We have a sea of knowledge that we want to – or have to – work with, that quickly exceeds the capacities of our attention and memory. This becomes a problem when urgent matters fail to receive prompt attention, or important details cannot be recalled. How, then, do we organize this information so that we can focus on what deserves focus, and recall what needs to be recalled?
Note that this does not mean that all knowledge fits into these two categories. There may be knowledge we possess that never warrants focus, and is never needed. This doesn’t mean that we don’t want the knowledge, however. A picture I took of a meal I ate ten years ago might be a delightful memory to encounter while randomly sampling the entries in my photo catalog. And so some knowledge is needful, some is desired, and some simply is and shouldn’t be entirely lost.
If knowledge is the content of our information universe, structure is the key to making it relevant and useful: like a river’s bed channeling many small streams into a mighty torrent. The imposition of structure, whether hierarchical or multi-classification (e.g., tags, labels, keywords, categories), amplifies the effectiveness of knowledge by fitting various subsets of it within the range of our mental capacity, and giving us the means to switch between these subsets as circumstances require.
Each popular system that has come up in recent decades is an attempt at a new classification: one that promises to work more effectively for more people. I would argue, however, that it is never the system itself that has merit; rather, its value lies in prompting us to conscientiously examine the classifications we’ve been using and how they might be improved. This time spent in, sometimes philosophical, reflection is the true benefit of these novelties. I would even say it’s healthy to try on each new system as it arises simply to undergo this exercise at regular intervals, for no one system is the right answer. We are evolving beings within an ever-changing environment. Part of the unique value we contribute to knowledge itself is our ability to constantly adapt it to fit new circumstances.
Take the PARA method, for example, which applies the hierarchical distinctions of Projects, Areas, Resources and Archives to one’s data collection. These groups can be further clarified by looking at the ways they separate that data:
Projects and Areas represent actionable data, while resources and archives contain reference data. Projects are distinguished from Areas by having a clear definition of completion along with a time horizon, and Resources are distinguished from Archives by asking whether the information currently being referenced.
This implies two axes: for tasks, bounded vs. unbounded; for information, active vs. inactive.
However, are these distinctions meaningful? Are they meaningful enough to deserve “first class” distinction in the form of a directory hierarchy?
For example, a project is no different from a task, it just has additional metadata:
- It contains sub-tasks
- It has a definition of done
- It has a deadline, roadmap or time horizon
This means that, from a sea of tasks, I can infer the list of active projects simply by querying for all tasks with these properties. In this sense, collecting them in a directory called “projects” is just a manual implementation of exactly this query.
It would be meaningful then to ask: What distinguishes a task from a non-task? A project from a non-project? An active piece of information from an inactive one? Are there really meaningful distinctions between these, or are they merely operational?
As an example of the difference between meaningful and operational, consider a tag like “@home”. This is something I use to distinguish tasks that can only make progress while I am at home. This allows me to query for such tasks when I’m at home, so that I see the list of things I can only do at that time. It doesn’t mean that such tasks are more or less urgent, or more or less important, than any other task; they are simply more achievable when I am at home. Thus, how I operate on or accomplish those tasks relates to me being at home, which makes this an operational query.
Another operational distinction lies in archiving information. The computer system I use to manipulate task data has size limitations. Giving it hundreds of thousands of files to work on slows it down, which reduces the appeal of using the system altogether. Thus, separating out the data which is no longer needed has the operational benefit of making the needful parts of the system runs much, much faster. That makes the distinction between active and inactive information a useful one, even if it is still not a meaningful one: it’s still all just data I might or might not want to access at some point in the future.
What makes a distinction meaningful, then? Typically there are several reasons for us to draw distinctions between data:
- If it materially affects our future, or the future of others
- If we need to narrow our focus to what is most needful or relevant
- If we want to improve our ability to locate data in the future, whether by searching, browsing, or filtering
- If we are trying to recognize a larger pattern among a collection of data