Data science in economy and finance: A central bank perspective

Data science is fundamentally different from traditional data analysis, as it typically applies to large, complex and/or unstructured information sets. The role of data scientists, hence, lies at the intersection of three areas. The first is IT: central banks are increasingly aware that a modern technological architecture is crucial to reliably and securely deal with data. The second relates to the use of mathematical and statistical techniques to deal with the raw data at hand. This paper analyses how many different approaches can help support central banks’ operations, especially in the monetary policy and micro- and macroprudential areas, as well tasks related to the functioning of the payment system, financial inclusion, consumer protection and anti-money laundering. It presents how, in addition, the use of the new techniques could transform the whole production chain of official statistics, making it potentially more efficient, resilient and user-friendly. The third key area is to ensure close cooperation between data specialists and subject-matter experts involved in data science projects. Analysing statistics calls for an awareness of the way they have been compiled as well as of the complex factors that drive them — which are essential elements for transforming data into knowledge and taking informed policy decisions.