Instrumentation & Measurement Magazine 23-3 - 32

Big Data, Dataism and
Measurement
Dario Petri

urrently, size of data is celebrated while only their
usefulness in decision-making is actually important. Principles, methodologies and techniques of
metrology are crucial to ensure proper and effective collection
and use of data. Unfortunately, the fundamentals of metrology
are seldom offered in educational degree programs, including
those in scientific and technological subjects. Consequently,
we should not be surprised if dataism (i.e., unconditioned belief in data) is spreading and a tsunami of improperly used
data is submerging us. In the age in which measurements permeate all areas and every level of society with the expectation
of promoting inconceivable socio-economic progress, underestimating the relevance of metrology is likely to lead to many
serious potentially negative consequences.

Measurement and Big Data
Measurement has fostered the evolution of human society, civilization and quality of life. Since the beginning of human history
measurements have been an essential tool for trading, building,
and artifact production. In the last centuries, measurement-based
knowledge has been crucial for geographical discoveries and for
the development of modern science, industrialization, and medicine. More generally, it has been a fundamental background for
knowledge advancement and socio-economic progress [1].
In the current information society, measurements permeate
all areas and every level of society. The growing opportunities to collect data at lower and lower costs offered by modern
technologies encourage amassing big amounts of data under the undiscussed belief that data will ensure inconceivable
progress and a deep transformation of our ways of working,
living and thinking [2], [3]. The ambition is to replace judgment based on personal experience and talent with processing
of information provided by ubiquitous sensors or collected
through the internet. As a result, astonishing quickness and efficiency improvements are expected. Moreover, the achieved
conclusions are perceived as fair and reliable because they are
obtained using formal procedures and objective data instead
of being the outcome of subjective judgments possibly biased
by personal opinions or self-interest.
32

Regrettably, while according to many society observers the
classic ideologies that provide foundation to modern societies
(like liberalism and socialism) are weakening, a new ideology
is quickly spreading, without most people being aware: dataism [4]. In its extreme form, the dataist:
◗◗ perceives the entire world as a flow of data;
◗◗ believes that data provide a fair and exhaustive representation of reality;
◗◗ has unconditioned confidence in data and bases his/her
everyday judgments only on data;
◗◗ believes that artificial intelligence will overcome human
intellect; and
◗◗ advocates the concept of cosmic data processing and sees
living organisms as biochemical information processing
systems.
Dataism is quickly spreading throughout the scientific community, too. Indeed, academic disciplines tend to
be walled off from one another. Also, there is often a significant gap between academic research and real-world needs.
Since data science seems to have the potential to break down
the barriers between the different disciplines and to contribute to fill the gap between theory and practice, it is expected to
become a single overarching and unifying language able to investigate and to explain the empirical world, thus promoting
inter-domain extension of insights and results. According to
this ideology, "Beethoven's Fifth Symphony, a stock-exchange
bubble and the flu virus are just three patterns of dataflow that
can be analyzed using the same basic concepts and tools" [5].
This perspective is extremely exciting: data science appears as
the scientific Holy Grail that has eluded scientists for centuries.
However, confidence of data driven decision-making does
not depend on the amount of data, but on their significance
and usefulness for the problem at hand. Thus, we do not need
"big data" but smarter exploitation of useful data.

Dark Side of Data
Unfortunately, while considering the amount of data is quite
easy, recognizing data usefulness cannot be so simple and
immediate. In fact, both a proper cultural background and a

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May 2020

Instrumentation & Measurement Magazine 23-3

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