, the growth of the digital economy, whose output is usually immaterial, limits the role of gdp in measuring economic activity and wellbeing. digitalization has dramatically changed how businesses are conducted and goods and services purchased. this brings us to the productivity paradox : productivity in industrialized countries is growing less than in past decades while economies are experiencing rapid digitalization. jerome powell noted that current statistics may understate productivity growth because they miss the value that we increasingly derive from new technologies, such as internet connections and smartphones. the measurement of the digital economy is now an important challenge for statisticians and economists. we need new methodological insights. to answer some of these questions, in 2016 banca d ’ italia put together a team to assess the potential benefits and hidden risks of big data, artificial intelligence and machine learning. despite controversies about privacy issues, despite password hacking and other worrisome aspects of online life, the world continues to embrace the internet and social media. it is estimated that more than 5. 1 billion people ( two thirds of the world ’ s population ) use a mobile phone and 4. 4 billion people take advantage of internet services. in 2018 the number of internet users recorded 9 per cent growth ( see the we are social blog ). so we have plenty of new data which can be mined to improve our knowledge of economic developments. to understand and exploit these data we need to bring together different skillsets : those of economists, statisticians, engineers and computer scientists. what are the advantages of these data? in his lecture on ‘ big data and measurement : from inflation to discrimination ’, the mit professor roberto rigobon said that the biggest advantages of organic data ( i. e. big data ) as against designed data ( such as administrative data ) are that : ( i ) they reduce the cost of collecting and providing information and ( ii ) they do not introduce distortions arising from the behaviour of respondents. people don ’ t lie to their gps or to google. the banking and financial industries have also been profoundly affected by these developments. central banks must react in a timely manner and develop new tools to carry out their missions : monetary policy, banking supervision and payment system oversight. in these two days you have seen many applications which can improve central bank activities. some papers focused on the extraction of quantitative information from texts and images : this can be useful for forecasting financial market variables, predicting headline inflation, and analysing the real estate market. 1

sentiment and retail depositors ’ trust ’, banca d ’ italia, temi di discussione ( working papers ), 1165, 2018. a. carboni and a. moro, ‘ imputation techniques for the nationality of foreign shareholders in italian firms ’, external sector statistics : current issues and new challenges. irving fisher committee on central bank statistics, 2018. see g. ardizzi, s. emiliozzi, j. marcucci and l. monteforte, ‘ news and consumer card payments ’, banca d ’ italia, temi di discussione ( working papers ), 1233, 2019 ; v. aprigliano, g. ardizzi and l. monteforte, ‘ using the payment system data to forecast the italian gdp ’, banca d ’ italia, temi di discussione ( working papers ), 1098, 2017. increasing the sample does not improve the accuracy of the estimates if the source of the bias is not understood and dealt with. the use of massive amounts of rough data may result in an exceptionally good in - sample fit but may perform poorly out - of - sample ( the curse of overfitting ). moreover, when using big data, special attention should be paid to the robustness and stability of the estimates over time. the second challenge is privacy. protecting the integrity and confidentiality of personal data is of paramount importance to our societies. storing personal data in digital repositories belonging to public and private institutions can be risky. we should avoid any use for illegitimate purposes such as the second - hand trading of personal information. at banca d ’ italia, we are exploring new avenues for utilizing microdata without jeopardizing their confidentiality. in this way data from different institutions can be combined to produce aggregate statistics that are important for understanding economic, financial and welfare issues. we have started experimenting some privacy - preserving algorithms with istat. we are also discussing these issues with eurostat and the national authority for personal data protection. the third challenge concerns the preservation of data over time. while we keep producing a huge amount of data, we have not yet agreed on a shared set of criteria for making these data accessible in the future. it is crucial that technology - neutral standards be established to ensure data availability for future generations. coordination between statistical agencies, governments, academia and private companies is equally crucial. timely and accurate information derived from non - traditional data sources, as well as new analytical techniques