‘To sleep – perchance to dream.‘ Shakespeare, 1602.

I’ve been reading up on the importance of sleep in Matthew Walker’s ‘Why We Sleep‘ this week. In reading, I was first struck by the parallels to technology and how we generate data insights. That led me to think about how important it is to have a variety of interests and keep learning if we want to innovate. I’ll summarise my thoughts here, but I really recommend the book if you want to hear more on the value of sleep (it has much broader physical and mental health benefits than mentioned here).

What Walker describes early in the book is the role of sleep in consolidating memory and forging connections. We’re probably all broadly familiar with the knowledge that you cycle through stages of sleep, between deeper sleep and lighter REM sleep. Walker asserts that in the deeper sleep stages, the brain performs a data cleansing exercise in determining what to move from short to long-term memory. In REM sleep, you then forge connections within this updated bank of memory. This is why we sometimes wake up with a solution to a problem that had been eluding us the previous day. Crucially, Walker explains that this is an iterative process that you need a full night of sleep to accomplish. As we sleep, we move through ninety minute cycles that initially weight more time in deep sleep before having extended periods of REM sleep later. That means you need a full night of sleep – around 7 hours – to reach sufficient REM stages to fully access that creative, solution phase.

The process reminded me of data analytics and how we generate data insights. We need to sort through and clean data when it’s first provided for analysis. You want to get rid of unneeded fields or duplicate data right away. Then you need to clean what you keep – eliminating trailing spaces, converting entries that mean the same thing but contain typos or abbreviations, etc. It’s a process that takes longer the first time you get a data set, because the specific scripts to clean that data will need to be generated in a way that’s tailored to the data. Once you’ve completed your first data cleanse, you can start the initial analysis. You start to derive insights based on trends etc, but you’ll also notice where you might benefit from including an additional field, connecting another data set, or adding another period’s data. You find yourself going back to the data preparation part to add in the extra data. It’s quicker to re-run the sorting process as the first set of rules are already there, but there’s still a few new ones to add. Overall, you can spend more time on the analysis. By the time you come to add more data again, it’s quicker still and you can quickly see new insights, re-balancing time from cleansing to analysing. Like with sleep, the full benefits come at the end of that iterative process. Without the right cleansing, the data won’t produce reliable results and without the right analysis, it’s data and not insight.

Now I don’t know if it’s fair to assume that each new day’s experiences are like a new load of data in exactly this way (not least because REM sleep does more than just solutions/connections). Walker’s only real mention of technology in the book includes warning against assuming the brain operates like a computer. But even taking account of Walker’s warning, I think it’s valuable to recognise parallels. The need to keep learning in addition to getting sufficient sleep stands out to me. Just as insights from data analytics improve with sufficient granular data, every individual will be more capable of innovation, problem solving and creativity if they have a commitment to a wide range of learning – a ‘growth mindset’. This is how innovation happens – taking ideas from disparate fields and forging connections. Whether it’s technology, diverse cultures, languages, biology, history, politics or something completely different, it’s worth taking time to learn. My interpretation is that it’s like building up your own mental library into which your subconscious can reach to propose solutions.

Before this ends up a bit Sherlock’s ‘Mind Palace’ – I’m not suggesting one person can have all the answers like some biological equivalent to an AI supercomputer. The recipe for success is still to have a diverse team who are coming at a challenge from completely different perspectives. But if each of those is a person committed to learning and getting a good night of sleep, then I imagine it becomes an organisational superpower in the same way that the correct use of data insights can be. It’ll certainly help equip the humans grappling with the challenges the data reveals.

As always, thanks for reading – and I hope you get a good night of sleep!