Harnessing Big Data & Machine Learning
Technologies for Central Banks
Closing Remarks by the Deputy Governor of the Bank of Italy
Luigi Federico Signorini
Ladies and gentlemen,
This two-day workshop on 'Harnessing Big Data & Machine Learning
Technologies for Central Banks' is now drawing to a close. Let me begin by thanking all
the speakers, discussants and participants who have contributed to the success of this
initiative. I have the pleasure of sharing with you some thoughts on the main issues.
The papers presented during this two-day workshop have left no doubt as to the
actual and potential value of big data and their importance for economic analysis. The
present wave of data warehousing and business analytics confirms this, with big data
poised to deliver topline research and statistical applications in a cost-effective way.
Huge amounts of data can provide substantial insights for the private and public
sectors, enabling them to transform these data into new products and services for
customers and citizens. Big data are already changing some aspects of business in the
financial industry with regard to banks, hedge funds, broker-dealers, and other firms.
Central banks and supervisors are also interested.
The widespread adoption of digital technology has increased the number and depth
of information sources of interest to economic analysis and financial stability, two of the
core concerns of financial authorities. We are not only voracious data users, we are also
big data producers. We collect terabytes of granular data in the fields of banking
supervision, oversight of financial markets and payment systems.
Central banks can and should harvest the benefits of new technologies. We should
select the best technology and exploit its power in turning ideas into actual applications and
improved statistical and computing efficiency.
The potential is huge, but there are, as usual, pros and cons. There are important
trade-offs that must be kept in mind. I see two main issues, or rather groups of issues.
One is technical, and some papers in this workshop have pointed to it. We need to
keep analysing and experimenting in order to be able to assess the real information value of
big data bases.
'N = All', as in the popular definition by Mayer-Schönberger and Cukier1, may
sometimes be illusory. Sometimes big data turn out not to be representative of the whole
population. For example, Groves (2016)2 argues that big data often contain few variables
and extracting their value requires linkages to other data; furthermore, they lack
representativeness. Social media or any other internet-based sources of big data also usually
lack full coverage of the population of interest.
The availability of massive amounts of data increases the chances of finding an
exceptionally good fit in sample when one estimates any model, but a very poor
performance out of sample. Big data are harvested from different technical sources and
change with consumers' preferences. Therefore, the parameters estimated using these data
can be subject to different forms of instability. This, however, is not unique to big data.
Large volumes, variety, and the short-lived nature of data available on web pages
make it difficult to accumulate and preserve historical big data; their preservation is
hampered by changes in physical storage devices, IT platforms and software. This point
deserves attention. Poor preservation or the loss of data could jeopardise the accountability
of decisions and research reproducibility.
The other key issue with big data is the protection of integrity, confidentiality and
privacy of data. Exploitation of data must be respectful of this principle, in legal and
ethical terms.
The effective application of the relevant laws and international coordination are
essential in this field.
Let me now conclude by touching on some principles that we follow in the Bank of
Italy as large producers, consumers and providers of data.
We adopt an integrated approach to collecting and processing banking and financial
data. As I have said on other occasions, we have always gathered information from
reporting agents following a single protocol and have then used this information for
multiple purposes.3 This ensures consistency across datasets and intermediaries and reduces
the need for burdensome ex-post data reconciliation. We have followed this approach since
the 1980s.
The Bank of Italy has developed a shared corporate statistical data dictionary and a
corporate statistical data warehouse to ensure the uniqueness of reporting models. Both
were planned with a view to managing the different areas of statistical and supervisory
information as parts of a single system.
Although the supervision and central banking functions require their own analytical
approaches, their decision-making processes draw on the aforementioned data dictionary
and warehouse. The same holds for other uses of the data, such as for research and official
statistics.
Some of the data we manage are confidential and/or sensitive. The protection of the
integrity and confidentiality of data is therefore a key concern. Access to data is suitably
restricted within the Bank, and data management follows clear rules on procedures and
responsibility.
Central banks must also be ready to cope with the increasing demand by the public
for access to granular data. As a data producer, the Bank of Italy has always strived to make
its statistics available to the widest possible audience. We already share some data with
researchers and other institutions, with appropriate and adequate protection of
confidentiality, of course. Within the Eurosystem's Household Finance and Consumption
Survey (HFCS), Italian data are available on request alongside those of other euro-area
countries. For firms' data, which are much more difficult to anonymize, the Bank has
developed a remote access system called BIRD (Bank of Italy Remote Access to Data),
which enables users to perform their analyses online, thereby preserving data
confidentiality. Particularly confidential data might require on-site access. The Bank of Italy
has started to design a Research Data Centre which will have a suitable taxonomy of
confidentiality areas.
Other European countries have already started to work on this (the Centre d'accès
sécurisé aux données in France, the Administrative Data Research Network in the UK and
the Deutsche Bundesbank Research Data and Service Centre). In addition, we need to find
solutions to allow us to link microdata belonging to different institutions without
compromising individual confidentiality.
The explosion of granular data provides many new research opportunities. For all the
issues I have highlighted, big data have big potential. In the past two days we have just
scratched the surface. More research is surely needed.
I wish to thank you all very much for your participation. Special thanks to all the
speakers, discussants, chairs and participants for making the sessions lively and thought-
provoking. Finally, I would also like to take this opportunity to thank all those involved in
preparing this event.
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