Big Data for Central Banks
In the Bank of International Settlements’ (BIS) latest podcast Bruno Tissot, Head of Statistics and Research Support at BIS, discusses how we can best use big data, the opportunities and challenges it presents, and the policy issues it creates.
Tissot likes to see big data as organic data as opposed to static data: static data is collected for specific statistical purposes, often via a survey, whereas today dealing with big data means drawing statistics and conclusions out of pre-existing organic data that is made up of a multitude of different data sets and types.
Analysing big data offers numerous benefits for the tasks of central banks, allowing them to better monitor financial markets and risks. However, it is not without its risks and challenges.
Tissot highlights how it’s not enough to merely collect the dots – you need to connect the dots. Advanced analytics tools such as Machine Learning present their own risks, including an inherent tendency to be backward looking, as they must be trained on existing data.
With big, unplanned data sets there is a risk that the data is not very representative – and when central banks draw data from sources without quality assurance they add legitimacy to that data, creating reputational risks.
Find the podcast here.