Big Data: Datafication

John Heo
CISS AL Big Data
Published in
3 min readOct 16, 2020

MATTHEW FONTAINE MAURY was a promising U.S. Navy officer headed to a new assignment on the brig Consort in 1839, when his stagecoach suddenly slid off its path, toppled over, and hurled him into the air. He landed hard, fracturing his thighbone and dislocating his knee. The injuries left Maury, at 33 years old, partially crippled and unfit for the sea. After nearly three years of recuperation, the Navy placed him behind a desk, as the head of the uninspiringly named Depot of Charts and Instruments. turned out to be the perfect place for him. As a young navigator, Maury had been bewildered by the way ships would zigzag across the water rather than take more direct routes. When he quizzed captains about it, they replied it was far better to steer a familiar course than to risk a less known one that might entail hidden dangers. ”

Photo of Matthew Fontaine Maury (https://en.wikipedia.org/wiki/Matthew_Fontaine_Maury#/media/File:Lt._Matthew_Maury.jpg)

As Maury was given time and chance to explore and examine the oceans, he started identifying patterns and also recorded the climates of the seas. Since then, he aggregated the recorded data and divided the Atlantic Ocean into blocks of 5 degrees longitude and latitude. In addition, in each section, he noted the temperature, the speed and the directions of winds and waves, the month (seasonal). Eventually, Maury combined all his logs and with the combined data, the seafarers were able to reveal more efficient routes, saving about 1/3 of the total journey time.

An Image of Prof. Shigeomi Koshimizu’s “Seat profile detectors” (Engineers unleash car-seat identifier that reads … — Phys.orgphys.org › pdf244013392)

In a study by professor Shigeomi Koshimizu at Japan Advanced Institute of Industrial Technology, Tokyo, he was able to invent seat profile detectors through the datafication with sensors on car seats. Koshimizu used sensors to quantify the contours of the body, posture, and distribution of weight of a person on the seat and recorded the data. With this technology, Koshimizu argues that it can be used to deter auto theft and improve road safety.

In the modern society, “The IT revolution is evident all around us, but the emphasis has mostly been on the T, the technology” but the I of information.

The word “data” in “big data” means to record, reanalyze and reorganize, which in another words, it is the information. In IT, even if the technology very advanced, without the “information” the technologies would not be able to connect and work together. So there should be a greater emphasis on the “Information” field of the IT in future.

Digitalization is also an important aspect along with the Datafication, but they have a distinct difference between each other. If datafication is the quantification of phenomenon, digitalization is the transfer of data from analog to digital (from papers to computers). Digitalization has significantly improved the organization and analyzation of massive collected data, advancing the process of data analyzing.

You may not realize but Datafication is interpreted in multiple different ways of our daily life.

If you enjoy watching videos on Youtube, you would notice the recommendation section on you page, showing videos that you may have interest in and enjoy watching. Youtube uses the method of Datafication to create algorithms to analyze your previous data and you’re your interest, in order to suggest a video that may appeal to you.

Another daily example can be the smart watches or bands that many people wears. One of the main purposes of these accessories is to measure personal movements and health information to analyze bio-rythms.

Just like this, datafication has already smear into our daily patterns and habits, improving people’s quality of life.

In conclusion, datafication is a very crucial process in data analytics, since if there were only observations of a phenomenon but no other recording, the phenomenon would not be able to be analyzed nor examined.

This article is based on Chapter 5 of Big Data: The Essential Guide to Work, Life and Learning in the Age of Insight by Viktor Mayer-Schönberger and Kenneth Cukier.

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