5 rules for an optimal information processing model

We live in interesting times. No need to tell you this, while I am writing this article the whole world is single focusing on solving a global pandemic crisis. In these times of uncertainty, if there is something I can tell for sure is that the world after this crisis won’t be the same as we know it today.

Paradoxically, a changing world is no news for us, humans of the 21st century. The continuous flow and fluctuation of information and the speed our societies are changing year over year make us more prepared than ever to adapt to the new world that will emerge after this crisis. It happened after the Black Monday of 1987, 9/11 and the financial crisis of 2008 and it is happening again.

However, one of the main challenges of living in this fast-paced changing world is how to optimize our mental processing model to ensure we organize, classify and process the information we receive, avoid data saturation and are prepared to make the right decisions in work as in life.

The following five rules I am presenting in this article respond to a processing model that I have applied successfully in multiple scenarios where data is unclear, abundant or incomplete. Either if you are a Manager, a Product Designer, an Architect, an Engineer or just a person trying to make sense of the world, these rules could help you process new information and make better decisions.

1. From General to Specific

This is a common rule when drawing, painting or writing, and there is a good reason for this. It is easier to work from the broader and general to the specific. Making general marks helps to make more specific ones. By creating the largest shapes first and organizing the space in a composition, we define the frames where to place and define the details.

I find this technique very useful when processing multiple information with no apparent connection at once. We must take a step back to see the general picture, map the whole content and start creating general marks where we can group similar or related data.