IEEE Technology and Society Magazine - September 2019 - 85

debatable (cf., the "Problem of Induction"), such an
approach can empirically backfire in many cases and
result in invalid, misleading, or unhelpful analyses.

Fooled by the "Big Noise"
In [43], the author Nate Silver notes that even though
data is increasing globally by 2.5 quintillion bytes
each day, useful information is of course not increasing commensurately. This means that most of the
data is just noise and that noise is increasing faster
than useful information. A related problem is that of
the curse of dimensionality, where the more dimensions one works with, the less effective standard
computational and statistical techniques become,
which has serious repercussions when we are dealing
with Big Data. As more data is generated and collected, the complexity, deviations, variance (or noise)
and the number of potential false findings grow exponentially compared to the information (or signal).
This spurious rise in data can mislead us to fake statistical relationships.
Another point of concern is that a plethora of hypotheses are tested using a single data in Big Data analytics,
which opens the door for spurious correlations. When
a large number of hypotheses are tested, it becomes
highly likely that some false results will become statistically significant, which can mislead Big Data practitioners if they are not careful. The proper way to formulate
a hypothesis is before the experiment not after it.
It is necessary to understand that the statistical significance under an inaccurate method is totally specious -
i.e., significance tests do not protect against "data
dredging" [45].
Apart from the problem of spurious correlations, Big
Data analysts can also become guilty of cherry picking.
In this phenomenon, scientists tend to focus on finding
support for their hypotheses and beliefs while overlooking other evidence. As a result, they only present positive results of their experiments that endorse their
hypothesis or argument instead of reporting all of the
findings [34]. In this way, Big Data provide a relatively
small advantage to improve public strategies, policies,
and operations with minimum public benefit [8].

Are Big Data Predictions Generalizable?
In Big Data analytics, datasets are mostly gathered
from observational sources instead of a strict statistical experiment. This poses the question of the generalizability of the insights learned from this data,
notwithstanding the large size of the dataset. Even
though we know that statistically speaking the discrepancy in an estimate decreases with an increase
in the sample size as Bernoulli proved, but the messiness of real-world data in terms of incompleteness,

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imbalance, hidden bias, rareness, large variances,
outliers, and non-independent, identically distributed
(IID) nature, means that simply getting more data is
not sufficient. Disregard for the messiness of the realworld data can mislead us into problems such as multiple testing, regression to the mean, selection bias,
and over-interpretation of causal associations [36].
Also we must note that certain things are not precisely predictable (e.g., chaotic processes, complex systems, the so-called black swans) no matter how much
data is available.

Big Data provides opportunities
for unscrupulous people, groups,
government, or organizations to use
users' personal data against them.
In BD4D applications, large administrative databases
may have obscure/indeterminate data quality, limited
information about confounding variables, and suboptimal documentation of the outcome measures; careful
attention should be given to the generalizability of the
learned insights.

Data in the Field of Development
Ethical Use of Data and Privacy Issues
Big Data opens frightening opportunities for unscrupulous people, group, government, or organizations to
use users' personal data against them for evil purposes as various facets of human lives are being digitized
[7]. Privacy depends on the nature and characteristics
of data, the context in which it was created, and the
norms and expectations of people [7]. Therefore, it is
necessary to situate and contextualize data in such a
way that minimizes privacy breaches without affecting
its usefulness.
Big Data can bring more transparency both for individuals and organizations by introducing user anonymization but there are numerous examples when data is
thought to be anonymous but its combination with
other variables resulted in unexpected re-identification
[2], [27], [42]. For example, it has been proved that four
spatiotemporal points are sufficient to uniquely identify
95% of the individuals in a dataset where the location of
an individual is specified hourly with a spatial resolution
given by the carrier's antennas [11]. In another similar
study, Latanya Sweeney [46] argued that 87% of all

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IEEE Technology and Society Magazine - September 2019

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