Darkness, A Flashlight, and the Data Scientist

What you don t knowData sciences and data analytics not only use different techniques, that are often highly dependent on the distribution characteristics of the data, but also produce very different categorical types of insights. These insights range from a better understanding things you know you know (data analytics) to discoveries in area where you don’t know what you don’t know (data sciences). However, this knowledge metaphor can be a bit confusing, so I often use the “Darkness, A Flashlight, and the Data Scientist” parable. 

Flash Light

In your mind, picture a darkened room, where you are standing, but do not know where in the room you are. In your hand is large flashlight. You raise it slowly, pointing it in a direction. You turn it on and white light radiates forward.

The light of the flashlight shines brightly on a distant wall, where you see several items. These are the things you know that you know. As you your eyes begin to scan outward, the wall turns to deep dark dark black where the light does not reach. In this darkness, there are things you don’t know you don’t know. You begin to look back into the cent of the light – that grey transitionary boundary between the light of what we know and the darkness of the we don’t know, are all the things we know we don’t know.

Singularity4

Data analytics is lot about understanding those things we know we know, that is quantifying the light. This is the world of descriptive and diagnostic analytics. On the other hand, data sciences help use understand the darkest parts of our world, where we look to predict temporal and spatial relationships  and prescribe means for achieving desired outcomes. Data analytics and sciences are different in their own ways, each very important in their own right. 

However, in the case of the data scientist, the metaphorical role is to pull the flashlight back so that more areas of the wall are illuminated. So, as the flashlight is linearly pulled back, the data scientist enables an exponential increase in our knowledge. In essence, the data scientist works in the dark so that others can benefit from the light. Think about it!



Categories: Data Science, Deep Learning

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1 reply

  1. Reblogged this on What's the Big Data Idea and commented:
    I’ve done deliberate and contingency planning in the military using “things you know, things you don’t know, and things you don’t know you don’t know”. I’ve not seen it to describe data science versus analytics, but this is an excellent parable that explains the dark and the light and the grey in between.

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