While walking around the office I happened upon a relatively new employee dragging emails from his inbox into folders. I asked why and was told, “I’m just answering emails and getting stuff off my desk.” An empty inbox may be emotionally satisfying to look at, but in practice, you should never do it. Here’s why.
I recently wrote a piece arguing that from a mathematical perspective, Messy Desks Are Perfectly Optimized. While it validated the genius of my friends with messy desks, it also generated a barrage of good-natured ribbing from my super-neat friends. Emotions aside, the math is the math! By putting the last paper you looked at on top of the pile, you are organizing your desk using an algorithm called LRU (Least Recently Used). It is based on the idea that the papers you most recently used are the ones you are most likely to use again. Conversely, the papers you have not used in a long time will probably remain unused. It is the closest you can come to predicting what data you are most likely to need next. But what about the papers on the bottom of the pile? When and where should they be filed?
Caching and Storage
Even if you don’t mind piles of paper everywhere, at some point, you are going to have more piles than will actually fit on your desk. It is this natural evolution from messy desk to messy office that gives us some insights into possible algorithms for caching and storage. Again, LRU comes to our aid.
Imagine you are doing research for an article. You keep the books that have the information you are using most open on your desk. Books that are important but not currently in use can be piled closed on the corners of your desk. Books you may need sometime in the near future can be piled on the floor around your desk or on a nearby chair. Books you don’t need too often can be on a bookshelf across the room. Books you don’t really need can be in your home library down the hall. Books you will rarely need can be stored on bookshelves in your basement. Each level of storage comes with increased costs of placement and retrieval. It will take much longer to go downstairs to find a book in the basement than it will to find a book you placed on the chair next to your desk. LRU helps determine what data to store and where to store it, and provides insights required for the related cost-benefit analysis.
Work vs. Meta-Work
In the physical world, you don’t need to sort any of the books alphabetically (although many people do it anyway); you just need to be able to scan the bookshelves with your eyes. Alphabetical sorting requires hours (possibly days) of meta-work. The result is an aesthetically pleasing, emotionally satisfying library shelf. But it’s a huge waste of time. You just need to know the general location of the book you want, and you can quickly scan the shelf to find what you need. What if you had a million books? Again, the math is the math. You’d need to look at all million books at least once to sort them. Until you reach a certain level of organization (which requires the application of other algorithms), searching beats sorting.
This is especially true with regard to your email inbox. You should never even consider manually moving emails into folders. It’s a huge waste of time. Practically speaking, even auto-filtering emails into folders is borderline useless. It’s fine to tag an email to help classify it for a search. In fact, tagging is a best practices method of enhancing search, but dragging an email into a folder is just meta-work. It will take you about the same time to manually place an email into a folder that it takes to file a piece of physical paper into a file folder. The key difference is that you can search your inbox in milliseconds. Why take hours to meticulously file when it only takes fractions of a second to search?
So the next time you are overwhelmed by the number of emails in your inbox, rejoice. They are all in one place and are automatically sorted by temporal relevance. Be comforted by the fact that no email in your inbox is more than a keyphrase and a click away. And revel in the glory that LRU and a well-indexed search algorithm are tuned to optimize your productivity.
Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it.