Introduction: What Is Data Science? - Doing Data Science - page 5

collected might be enough to be considered “big” (more on this in the
next chapter); in other cases, it's not.
But it's not only the massiveness that makes all this new data interesting
(or poses challenges). It's that the data itself, often in real time, becomes
the building blocks of data products . On the Internet, this means
Amazon recommendation systems, friend recommendations on Face‐
book, film and music recommendations, and so on. In finance, this
means credit ratings, trading algorithms, and models. In education,
this is starting to mean dynamic personalized learning and assess‐
ments coming out of places like Knewton and Khan Academy. In gov‐
ernment, this means policies based on data.
We're witnessing the beginning of a massive, culturally saturated feed‐
back loop where our behavior changes the product and the product
changes our behavior. Technology makes this possible: infrastructure
for large-scale data processing, increased memory, and bandwidth, as
well as a cultural acceptance of technology in the fabric of our lives.
This wasn't true a decade ago.
Considering the impact of this feedback loop, we should start thinking
seriously about how it's being conducted, along with the ethical and
technical responsibilities for the people responsible for the process.
One goal of this topic is a first stab at that conversation.
Datafication
In the May/June 2013 issue of Foreign Affairs , Kenneth Neil Cukier
and Viktor Mayer-Schoenberger wrote an article called “The Rise of
Big Data” . In it they discuss the concept of datafication, and their ex‐
ample is how we quantify friendships with “likes”: it's the way
everything we do, online or otherwise, ends up recorded for later ex‐
amination in someone's data storage units. Or maybe multiple storage
units, and maybe also for sale.
They define datafication as a process of “taking all aspects of life and
turning them into data.” As examples, they mention that “Google's
augmented-reality glasses datafy the gaze. Twitter datafies stray
thoughts. LinkedIn datafies professional networks.”
Datafication is an interesting concept and led us to consider its im‐
portance with respect to people's intentions about sharing their own
data. We are being datafied, or rather our actions are, and when we
“like” someone or something online, we are intending to be datafied,