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Improved central bank data management in a digital age

The panel

  • Eric Santor, Adviser, Bank of Canada
  • Per Nymand-Andersen, Adviser, European Central Bank
  • Howard Chang, Vice-President of Global Affairs, TCSA
  • Moderated by: Dan Hinge, News editor, Central Banking

Data is crucial for central banks to make effective policy decisions, but many rely on traditional indicators that are no longer fit for purpose. New data infrastructures have the potential to provide central banks with a real-time snapshot of the economy, but the reliance on old legacy data systems and outdated technology means the data collected is fragmented.

Using current infrastructure, data harmonisation is difficult, inaccurate, costly and resource-intensive. While some central banks have adopted new tools such as artificial intelligence and machine learning to help ease some of these challenges, many are not yet making use of new informative data architecture – which have the power to improve policy decision-making

TCSA has developed a new economic model – named the ‘data brain’ or algorithm unit – which is able to transform disparate micro and macro data points into standardised datasets.

Key topics discussed:

  • Where central banks see the biggest opportunity in improving data architecture
  • The challenges central banks face when collecting, aggregating and analysing economic data from new sources
  • The technology/models that could mitigate these problems
  • Issues around data privacy and data governance in a digital environment
  • Whether data-driven policy-making could be the future of central banking.


This feature forms part of the Central Banking focus report, Data-driven policy-making for central banks 2021

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