Central banks have traditionally relied on established economic indicators, financial reporting, and statistical models to understand market conditions and guide monetary policy. However, the rapid growth of digital information is creating new opportunities to analyze economic activity through data sources that were previously unavailable or too expensive to process.
From internet searches and mobile phone activity to financial transactions and market data, big data offers central banks additional ways to monitor economic developments. Yet the volume and complexity of this information present significant challenges. As central banks explore advanced analytics, machine learning, and cloud computing, they must determine which data sources can provide reliable insights and how those insights can support economic research and policy decisions.
How Central Banks Are Approaching Big Data
This article in Central Banking is based on a forum that included David Bholat, Senior Manager, Advanced Analytics, Bank of England and Jyry Hokkanen, Head of Statistics, Sveriges Riksbank of Sweden. The discussion makes most Big Data problems pale in comparison, so read the whole article if you want your Big Data problems to appear smaller:
The Growing Supply of Data and Analytics Tools
“David Bholat: Yes, unequivocally among both central banks and the broader financial sector. The reason is that you have both supply and demand factors at work. On the supply side there is an accumulation of data – from everyday uses and appliances such as mobile phones or Google searches – which is constantly being created, and therefore the opportunity exists to mine it in some way. Plus, you have the development of very cheap tools to store and analyse that data, and the development of cloud computing, which means organisations can have a lot more data storage capacity. On the other end of the data analytical spectrum are open-source tools like Python and R that come with ready-made machine-learning packages. Again, they’re free, so there’s a huge value proposition there.
On the demand side, both central banks and financial firms see the need to drive operational efficiency, particularly in the private sector among financial firms – to the extent that we are now in a low-interest environment and you can’t drive top-line growth. Margin can therefore only be maintained by cutting operational expenses.
Turning Unstructured Data into Economic Insights
Jyry Hokkanen: I completely agree – there’s so much data on the internet from economic agent activities that can be collected and stored cheaply. The question is: how is this interesting for central banks? It takes a lot of effort to analyse this data because it is so unstructured. We see a lot of interesting research on the unstructured side of big data, but it’s going to be difficult for central banks to use it in a meaningful way. Structured big data is more for central banks to analyse financial institutions and markets, and hopefully monetary policy as well to aid macroeconomic analysis.”
The discussion highlights how the growth of big data is reshaping analytical opportunities for central banks and financial institutions. Increasing volumes of digital information, combined with lower storage costs and accessible analytical tools, have made it possible to examine economic activity in ways that would have been difficult or prohibitively expensive in the past.
However, access to more information does not automatically produce better economic insights. Central banks must distinguish between data that offers meaningful signals about financial markets and economic conditions and information that introduces unnecessary complexity. Unstructured data, in particular, presents challenges because it often requires extensive processing, interpretation, and validation before it can support reliable analysis.
The distinction between structured and unstructured information is especially important for central banks. Structured financial and market data may be more readily incorporated into existing analytical frameworks, while alternative sources such as internet activity could require new methodologies. Both approaches offer potential value, but their usefulness depends on the quality of the information and the ability to connect findings to economic questions.
Ultimately, the value of big data in central banking will depend on how effectively institutions transform large datasets into actionable intelligence. Investments in analytical expertise, machine learning capabilities, and data infrastructure could improve economic monitoring and operational efficiency. As these technologies mature, central banks will need to balance experimentation with the rigorous standards required for economic analysis and monetary policy decisions.
Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group
