nontraditional data & statistical learning with applications to macroeconomics welcome address by daniele franco senior deputy governor of the bank of italy rome, 11 november 2020 ladies and gentlemen, i am glad to open this virtual conference jointly organized by banca d ’ italia and the federal reserve board. i would like to welcome all the participants from more than 60 countries. 1. this conference is about the impact of digital activities on economic analysis. an astonishing amount of digital activity is occurring at any given moment. this explosion stems from the aggregate action of 4. 5 billion internet users, a number that is projected to rise even further in the coming years. since the outbreak of covid - 19, technology has played an even greater role in our daily lives. this development has increased the production of data. in fact, data are constantly being generated by our clicks, reactions on social media, shares, streaming of videos, digital transactions, digital recordings of our personal and work activities, digital circulation of scientific texts, and so on and so forth. these data can give us a better understanding of the state of the economy at both the micro and macro level, provided – obviously – that we have access to them. data ownership is therefore extremely important. as recently highlighted in the economist ( ‘ who owns the web ’ s data? ’, 22 october 2020 ), we are now experiencing a sort of ‘ digital feudalism ’, where a handful of big tech companies have full control over data. tech companies such as facebook, amazon, and google have reached an impressive position, becoming some of the biggest companies in the world. in this process, they have caught up with longer - standing titans like apple and microsoft. the use of data is actually one of the world ’ s biggest businesses. we see this in the market capitalization of some companies, like alphabet ( the owner of google ) and facebook. even once the value of their cash, physical and intangible assets, and accumulated r & d has been stripped out, their capitalization remains huge. these developments are causing increasing concerns among policymakers, from both a political and economic point of view. as to the former, the big techs ’ business model depends on network effects to influence users and sell more advertising, which may result in collective behaviour that can harm public discourse. economic concerns stems from the fact that the size of big tech firms may shrink competition. 2. banca d ’ italia has always based its decisions on the data. micro -

encouraged as far as possible. furthermore, the adoption of machine learning and artificial intelligence requires as input huge amounts of granular and unstructured data, which should be collected through close collaboration between public institutions and private companies. we also need specific platforms or data lakes to store and analyse such data so as to preserve the general data protection regulation ( gdpr ) is the european union law on data protection and privacy ; the california consumer privacy act ( ccpa ) is the statute aimed at enhancing privacy rights and consumer protection for residents of california. privacy and security. this can be done through a collectively trusted cloud where all actors, both public and private, can operate to gain insights for their core business. the availability of privacy - preserving algorithms or the use of federated learning, recently suggested by researchers at google, could provide the enabling technology. federated learning allows machine learning algorithms or artificial intelligence on data to be distributed on different servers, avoiding any data exchange. this will require further investment from both the public and the private sector. it will also require tighter cooperation between the private sector, which typically owns most of the new data, and the public sector, which uses such data for policy reasons and for the common good. 6. the challenges generated by big data and machine learning technologies require continuous organizational and technological innovation in the institutions that want to use them effectively and fairly. banca d ’ italia has taken several steps in recent years to enhance its ability to collect, store and exploit huge amounts of data, using state - of - theart hardware and software. other institutions are doing the same. let me conclude my talk by thanking, once again, all the speakers and participants for joining us today. we hope to welcome you here in person in the future. special thanks go to those who have contributed to organizing the workshop, which brings together leading economists, statisticians, artificial intelligence and machine - learning specialists, data scientists from about 60 central banks, 57 universities, 24 government agencies, 7 multilateral organizations, and 20 private companies. this will ensure a broad variety of perspectives and a lively discussion. i am sure that you can count on having two interesting and productive days. designed by the printing and publishing division of the bank of italy