After taking the course of Analytics and Visual
of Big Data, we are all aware of the great ability and advanced functions of
date mining technology. From the blogs, we’ve seen a great deal of applications
of various fields, such as in Sports, weather, business and et cetera. However,
today I’d like to write something about
the shortness and drawbacks of the data. Absolutely there must be something
that big data can not do or only can do poorly.
Thing first come to my mind is that data
struggles with social. Compared to human being, computer-driven data analysis
are pretty good at measuring the quantity of social interactions rather than
the quality. Unlike people, data analysis rarely can do few things in mirroring
each other’s emotional states, detecting uncooperative behavior and assigning
value to things through emotion. Thus one would draw a wrong conclusion about
how close social relationships between people rarely based on the number of days
they meet each other or calls they make.
Plus, data mining somehow might “favor
memes over masterpieces”. Due to employing dimensionality reduction approach
like singular-value decomposition (SVD), data mining software have to filter and
ignore relatively large data, in which useful information might exist. For instance,
data analysis are most likely to detect when great numbers of people take an
instant liking to some cultural product. However many important products of great
value are hated at the first beginning only as result as the factor that they
are unfamiliar.
Data also obscures values. There is saying
that, in a sense, the data is never raw, which means that it’s always
structured in order to achieved people’s predispositions and values. In fact,
from construction to interpretation, there are always value choices exist.
Well, this is not to say that data mining
isn’t a great technique. But like every coin has two sides, it has strong
merits in some aspects, in the meantime, it is supposed to have a little shortness
as well.
No comments:
Post a Comment