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:
“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.
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.”
Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group
