Abstract

The aim of this session is to focus specifically on how data science can contribute (and have contributed) to the statistics production process at central banks. Some topics include (but not limited to): (1) the automation and optimisation of statistics production processes (including dissemination and reporting), (2) the use of novel techniques in the areas of cluster analysis, outlier identification and treatment, as well as survey-level imputation, (3) the development of open-source data management platforms and its integration with existing internal systems , (4) internally-developed business intelligence (front-end) solutions for internal and external stakeholders, and (5) the sourcing, processing and dissemination of statistics using non-traditional sources of data. Lastly, central bank representatives will showcase what has been done in their respective areas on the topic. There is in particular interest in the toolchains and workflows used by central bank data scientists to enhance the sourcing, compilation, and dissemination process. The interactions with the information technology department regarding architectural designs (tech stack), the introduction of new technology, and cybersecurity considerations (and the associated challenges/opportunities) would also be analysed.