Can you remember what you did last Tuesday between the hours of 14:00 and 15:00? Neither can we. But this exact kind of memory slip is costing the US economy $7.4 billion a day in lost productivity. They’re not alone, of course; the majority of the business world continues to bill work using manual, memory-based time tracking, with all the error and guesstimation that comes with.
But instead of hammering employees to get better at tracking their time or looking for nicer-looking timer tools, we need to admit the fundamental flaw in our methods: our time tracking software doesn’t reflect how the human brain actually works.
Time tracking is draining. It’s fiddly, intrusive and often strikingly ugly, full of irritating timers and lacklustre UI. As software designer Thomas Fuchs notes: “Everything is grey and beige, you need to fill out confusing forms and you have to wait forever for anything.” Few of us enjoy doing it, and few of us actually do it well.
But forgetting to start or stop timers, putting off logging your hours, and missing out details of our work are all due to limitations in our biology – not our willpower. Time tracking simply isn’t something we can actively change or get better at – as humans, we’re fundamentally bad at doing it. Here’s why our brains aren’t designed to track time:
Unlike computers, human cognitive processing requires regular downtime, using both activity and relaxation to process information.
The brain is made to go into a less active state, which we might think is wasteful; but probably memory consolidation, and transferring information into memory takes place in this state.
– Erik Fransén, Professor of Computer Science at KTH
But taking cognitive breathers is obviously at odds with manual time tracking, which requires constant attention in order to be accurate. Unsurprisingly, research shows that the more frequently you log your hours manually, the more accurate your time records. But to do that, you need to be always on – consciously aware of everything you’re doing, and meticulously managing timers to capture the time you spend on them.
Given the difficulty and irritation involved in managing manual timers, many people end up logging their daily hours from memory – especially when their work involves frequently moving between tasks, clients and projects. But doing so places huge faith in their short-term memory.
Psychologist George Miller first suggested that people are capable of storing between five and nine items in their working memory, but more recent research has shortened this to about four. Exceeding this limit incurs a biological cost, making it harder to recollect things with such quality.
Given such limitations, even daily memory-based time logging can leave time records open to serious errors and omissions.
Of course, you can’t actually consume an unlimited amount of data each day. Pushing beyond our limited capacity can quickly overwhelm us. Many of us unwittingly do this every day – consuming on average about 34 GB of information and generating 50,000 thoughts. It can lead to decision fatigue – where we make bad, impulsive decisions – and poorer memory encoding.
This means our brains simply can’t create detailed, mathematically precise logs of our entire work day. Instead, they excel at processing and being creative with the data they find.
“ A computer is of course a great tool for accurate storage and reliable retrieval… We need to free up any space that is used for pointless memorization so that the brain can do what it does best — process information.”
– Leon Ho, Lifehack founder
To be worth anything, your time tracking data needs to be accurate and objective – a faithful account of exactly how you spend your time each day. But when you consider that the brain doesn’t process event duration uniformly, manual time tracking again appears to be a doomed exercise.
While we’ve invented clocks to break time into neat, countable units, our lived experience of it is completely subjective. Time can elastically speed up and slow down depending on our emotional state, and the amount of new or important information we encounter warps our perception of how much time we spend on a task.
So while time spent on an interesting, new or complex task will expand retrospectively in our memory, time on a repetitive or familiar one will contract. Just think about how hard it is to gauge the time you spend driving on a motorway – when everything looks the same and there’s nothing new to respond to, it all becomes a shapeless blur of time.
🎧 Find this topic fascinating? Check out our podcast on the psychology of time!
Manual time tracking requires you to stay conscious about the time you spend on tasks – recognizing when you’re about to switch tasks and continually inputting your time throughout the day. But this effectively means you have constantly divide your attention between the clock and your work.
Maintaining this continual partial attention takes its toll on the quality of our work, making it impossible to give a task our full cognitive effort. It effectively introduces a form of multi-tasking into our day, that severely limits our performance – with small task shifting costing us as much as 40% of our productive time.
So while splitting your focus may improve the accuracy of your manual time tracking, it restricts the progress and quality of your work.
So poor time tracking isn’t our fault – it’s a design failure in our methods. The way we’ve approached it since the 1800s simply doesn’t reflect the way our human brains work. To produce truly accurate, objective time logs, we need to outsource the task to our computers.
Many professionals have already grasped this and have stared automating their time tracking. Instead of using timers or spreadsheets, they use secure software to meticulously record the time they spend on documents, tasks, apps and websites for them in the background. Some even leverage AI to make their time sheets write themselves.
Instead of constantly interrupting your day to fiddle with timers, or trying to construct a work log from memory, you can just focus fully on your work knowing every detail is being captured. When it comes to mass data processing, this kind of computer automation is a no-brainer – in every sense of the word.