Cons of Big Data

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It contains the negative points of Big Data.

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1. It incentivizes more collection of data and longer retention of it. If any an
d all data sets might turn out to prove useful for discovering some obscure but
valuable correlation, you might as well collect it and hold on to it. In long ru
n, the more useful big data proves to be, the stronger this incentivizing effect
will bebut in the short run it almost doesnt matter; the current buzz over the id
ea is enough to do the trick.
2. When you combine someones personal information with vast external data sets, y
ou can infer new facts about that person (such as the fact that theyre pregnant,
or are showing early signs of Parkinsons disease, or are unconsciously drawn towa
rd products that are colored red or purple). And when it comes to such facts, a
person a) might not want the data owner to know b) might not want anyone to know
c) might not even know themselves. The fact is, humans like to control what oth
er people do and do not know about themthats the core of what privacy is, and data
mining threatens to violate that principle.
3. Many (perhaps most) people are not aware of how much information is being col
lected (for example, that stores are tracking their purchases over time), let al
one how it is being used (scrutinized for insights into their lives). The fact
that Target goes to considerable trouble to hide its knowledge from its customer
s tells you all you need to know on that front.
4. Big data can further tilt the playing field toward big institutions and away
from individuals. In economic terms, it accentuates the information asymmetries
of big companies over other economic actors and allows for people to be manipula
ted. If a store can gain insight into just how badly I want to buy something, ju
st how much I can afford to pay for it, just how knowledgeable I am about the ma
rketplace, or the best way to scare me into buying it, it can extract the maximu
m profit from me.
5. It holds the potential to accentuate power differentials among individuals in
society by amplifying existing advantages and disadvantages. Those who are savv
y and well educated may get improved treatment from companies and government whi
le those who are poor, underprivileged, and perhaps already have some strikes ag
ainst them in life (such as a criminal record) will be easily identified, and tr
eated worse. In that way data mining may increase social stratification.
6. Data mining can be used for so-called risk analysis in ways that treat people u
nfairly and often capriciouslyfor example, by insurance companies or banks to app
rove or deny applications. Credit card companies sometimes lower a customers cred
it limit based on the repayment history of the other customers of stores where a
person shops. Such behavioral scoring is a form of economic guilt-by-association
based on making statistical inferences about a person that go far beyond anythin
g that person can control or be aware of.
7. Its use by law enforcement raises even sharper issuesand when our national sec
urity agencies start using it to try to spot terrorists, those stakes can get ev
en more serious. We know too little about how our security agencies are using Bi
g Data, but such approaches have been discussed since the days of the Total Info
rmation Awareness program and beforeand there is strong evidence that its being us
ed by the NSA to sift through the vast volumes of communications that agency col
lects. The threat here is that people will be tagged and suffer adverse conseque
nces without due process, the ability to fight back, or even knowledge that they
have been discriminated against. The threat of bad effects is magnified by the
fact that data mining is so ineffective at spotting true terrorists.
8. Over time such consequences will lead to chilling effects, as people become m
ore reluctant to engage in any behaviors that will put them under the macroscope

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