Bank of Italy and Bank for International
Settlements Workshop on
'Computing Platforms for Big Data and
Machine Learning'
Opening Remarks by the Deputy Governor of the Bank of Italy
Luigi Federico Signorini
Rome, 15 January 2019

Ladies and Gentlemen,
It is a pleasure to open this workshop and welcome all the participants.
Everyone is aware that modern societies are generating an unprecedented
amount of digital information. The latest Data never sleeps annual
report, published last June, claims that mankind now generates more than
2.5 exabytes of data every day. The increase is bound to continue: by 2020,
an estimated 1.7 MB of data will be created every second for every person
on earth.
The enormous wealth of information that has thus become available has
great potential value if effectively captured and aggregated. However, it also
poses fundamental challenges that are easily overlooked. Let me open this
workshop by briefly mentioning three that I think deserve more attention
than they usually get.
First, big data are worth little if they are used in a methodologically
unsound way. Such data are typically representative only of selective
strata of the population; increasing the volume of data will not improve
the accuracy of estimates if the estimation procedure does not correct
for this. Social media data for example, like many other internet-based
sources of big data, are usually based on a biased sample of the population;
now-fashionable media-based indicators (of confidence, say, or political
trends) are often employed with scarcely a thought for this fact. Relatedly,
the careless use of massive amounts of rough data will sometimes result
in an exceptionally good in-sample fit, especially with non-parametric
or machine learning approaches, but a poor performance out of sample
(this is sometimes called the 'overfitting plague'). Reference texts should
place much more emphasis on these facts in order to provide a more
rigorous guide to practitioners and, ideally, educate the public at large as
the ultimate consumer of processed information.
Second, while the world produces a relentlessly growing mountain of
data, it has not yet settled on a reliable set of criteria for making (some of)
it effectively accessible in the more or less distant future. Large volumes
of internet data are casually stored, overwritten and discarded without any
consideration for what the next generations will need or want to know about
the world today: and this not just in the general pursuit of knowledge, but
also for more immediately practical purposes, such as clarifying legal rights.
Long-term conservation of data is also hampered by changes in physical
storage devices, IT platforms and software. The theory and practice of
building archives in the big-data era is in its infancy; it needs to grow up fast.
Third, the emergence of big data has raised the importance of data
integrity, confidentiality and privacy to a level never seen before. Protection
of personal data is central to our societies; one could say that it helps to
define them, to the extent that it is inextricably linked to the protection of
individual rights more generally. The sheer amount of personal data now
available, and the growing ease of connecting individual information across
databases, have far-reaching implications for fairness and freedom; they
have thus spurred much digital-privacy regulation. The connections with
the previous point are, I think, rather obvious (data integrity, for instance,
is central to both); so are the implied trade-offs. For instance, the right
to be forgotten, increasingly (and, I think, rightly) granted to individuals,
conflicts with society's desire to keep records. No wonder that rules are
sometimes inconsistent across different jurisdictions, data domains or legal
purposes. A systematic way of weighing conflicting principles against one
another should be found. Ideally, one would like to have global standards;
international cooperation is therefore to be encouraged as far as possible.
However, there are limits to this, at least as long as different societies
protect individual freedom to a different extent. For the time being, we
have to live with this limitation.
The challenges of big data require continuous organisational and
technological innovation in the institutions that want to use them effectively
and fairly. At the Bank of Italy we have taken several steps in the past few
years to enhance our ability to collect, store and exploit huge amounts of data,
using state-of-the-art hardware and software. Other institutions are surely
doing the same. This workshop is a good opportunity to share experiences.
Preserving the confidentiality of individual data is one specific
challenge that many institutions represented here will often face. The issue
is not just that the social value of statistical analysis cannot override the
legal obligation to refrain from infringing on personal privacy, but also
that the quality of the data itself is often premised on confidentiality being
guaranteed: without it, proprietary data would be impossible to collect,
and much sensitive data, such as data on personal wealth, would be next
to useless. We are cooperating with other institutions to find ways to
reap the value of merging data from different sources, and to make them
available - insofar as possible - to independent researchers, while avoiding
confidentiality breaks.
Let me conclude by thanking once again all the speakers and participants
for joining us today. Special thanks go to those who have contributed
to organising the workshop. It brings together highly professional data
scientists, IT architects and business specialists from central banks,
providing views of excellent quality and a broad variety of perspectives.
I am sure that you can count on having an interesting and productive day.
Designed by the Printing and Publishing Division of the Bank of Italy