We Don’t Know What Productivity Really Is
Most of us want to be productive but do not completely understand what we do, why we do it, and who judges it as productive or non-productive.
Productivity can be defined as the effectiveness of an effort, measured in terms of input and output. This definition is limited, as modern knowledge work does not fall in the category of industries (like a sugar factory) or agriculture, where productivity can be measured tangibly, and the output is already defined in a linear way.
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Knowledge work in the modern age involves thinking, analysis, theories, trial and error, problem solving, brainstorming and other stuff which is done in a variety of counter-intuitive ways. It is defined by a diverse range of iterative exploration of yet-to-discover possibilities.
Comparing the earlier (linear) definition of productivity to modern knowledge work is like comparing a bicycle to a computer.
Focus is a state of attention which puts us in an intentional flow mode, making us live the knowledge we have inside, and create connections using deep thinking in a space and time devoid of distractions.
When one does not burn ‘cognitive calories’ and waste precious attention on tasks that have little or no value, and is not subscribing to an obsolete productivity model, then one can be ‘productive’ in the modern sense.
Fixing employee productivity in the industrial age, when most workers were handling machinery and it’s parts, was a tedious but doable process. The managers had to fix the people who were making mistakes or were inefficient through systematic management.
Today, in the age of software and intellectual property, when half of the workforce is made up of knowledge workers, the old practices are of no use.
Productivity metrics are a means of tracking and measuring how quickly and efficiently employees are completing assignments.
These metrics are also helpful insights to track, manage, and improve employee performance.
As you would expect, there are various types of employee productivity metrics, including quantitative and qualitative measurements.
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