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The Law of Productivity I found in Cal Newport’s “Deep Work” is simple.

‘High-Quality Work’ = ‘Time-spent’ x ‘Ability-to-focus’

Thus, if what you need to do takes some time, the amount of focus or concentration allows you to perform other tasks. But in most cases, Time is short. Therefore, you need to be highly focused to get high quality in your work.

law of productivity

This is easier said than done. You cannot stop people from knocking on your door. Cannot avoid people calling your office. For some, if you don’t find time to answer or organize emails, your inbox becomes demotivating.

Also, contrary to the Law of Productivity, some research tasks demand a lot of your concentration and take an enormous amount of time. This happened to me while processing some data. I couldn’t just automate the process because I needed to pay attention when faulty data affected the results and needed to be discarded. In this example, the time planned for the task was undervalued and other tasks planned for the day postponed.

On top of this, if you need a lot of time and be highly focused, does this mean you get a super-high-quality-work? Well, I was just processing data. The analysis performed on the data acquired usually defines the quality of your work, not the data process phase, even if analyzing depends on processing.

I’m puzzled about being productive in the processing situation. I’d like to share what I’ve learned and hope it resonates.


Plan ahead

… establish deadlines and define what to do. Don’t deviate. It demands sacrifice but it pays off. Keep your eyes on the goal.


Chunking tasks

… small tasks in the intervals required to pause and recover strengths. In this way, you decrease the number of small tasks that otherwise remain undone.


Be patient as you are resilient

A lot of time-demanding-boring tasks will test your resilience. See it for learning mind point of view to find sense and meaning in those tasks. But also see a way to learn patience. It is one of the highest qualities a researcher should develop because good science always takes time.