Big Data comes in many forms and is applied in various fields. Over the last years, there has been a big jump in acceptance and use of this data — even in important institutions such as Central Banks. Read on to learn how central banks define and use big data — and how they tap into Social Media.
The history of banks and banking starts in the 16th century in Florence, Italy. It takes a while to establish the system of minting, lending and credits to spread over the old world and then into the new territories and colonies all over the globe. Eventually, national banks establish themselves — under the rule of crown and state, and they hold hoards of gold to guarantee price stability.
>> Fast forward and we have a European currency area and have the base camp of the European Central Bank (ECB) in Frankfurt since 1998. Since then, the hordes of gold have been replaced by control of the base rate and preventing crises like “GFC”* 2007/9. Here is where big data comes on stage, as it can be used to forecast, or even nowcast certain developments and analyses sentiments and much more.
Big Data — definition of an anthill 🐜
But what really is big data apart from a buzzword, which has been around for at least a decade now? One classical definition is, that “Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety.”[Source] On top of this, for central banks, big data is a new, alternative source for information that supplements classical statistical methods. There are three sources central banks consider relevant, where big data is produced, more or less accidentally [see Tissot, Bruno: „Big Data for Central Banks” 2018], as a by-product of certain transactions.
- Source 1: Traditional business systems like credit cards and e-commerce.
- Source 2: another buzzword — the IoT — “internet of things” which refers to all kinds of computer-run machines that may or may not have (e)SIM-cards and log their activity like mobile phones or traffic sensors.
- Source 3: “social networks” consists of human-sourced information such as blogs, videos, searches but also the well know social networks like Twitter, Facebook or the like.
The troubles with Big Data
The trick is then how to turn these huge chunks of data into valuable information. There are many obstacles that range from a lack of transparency in the methodologies, inconsistent observations, unstructured data to a lack of skilled data scientists to solve the trouble. There has been progress in the last years; a recent study shows that 80 percent of central banks now use big data compared to only 30 percent in 2015 [see BIS Working Papers No 930: “Big data and machine learning in central banking“, p.3.].
👍 Thumbs up! Followerpower!
But it´s not all about crises and base rates — central banks also care about the public opinion. And where would one find this better — maybe in an amplified way — than on Twitter and other popular networks? Especially the non-expert audience is audible here and delivers input for sentiment analysis, which parses what is said and the way of active participation in the dialogue, thus delivering facts are key to understanding the public opinion. But also, early on warnings to crisis might be achieved through data gathered from social media.
More at safeFBDC Live ✨
To learn more on what your “Thumbs up” does to the inflation and what other kinds of data are used, to guarantee the financial stability of the EU and other things, join us for safeFBDC Live on 30th September — live from TechQuartier with these distinguished experts:
Sascha Steffen | Vice President Research and professor of finance at the Frankfurt School of Finance & Management, and the director of Financial Intermediaries and the Real Economy (FIRE) — a research-led think tank dedicated to understanding the important challenges facing financial markets and the economy. He is also principal investigator of the project “AI & Monetary Policy,” which is driven by questions such as the role of monetary policy and banks in climate change.
Michael Ehrmann | Head of the Monetary Policy Research Division in the ECB’s Directorate General Research. Previously, he worked as Director in the International Department and as Head of Research at the Bank of Canada, and held various positions at the ECB, including Head of the Financial Research Division. His research covers central bank communication, monetary policy transmission, international finance and household finance. He holds a PhD in Economics from the European University Institute in Florence, Italy.
Paula Cocoma | Assistant Professor of Finance at the Frankfurt School of Finance & Management. Her research is centered around theoretical asset pricing, where she focuses on the decision to learn and process information and the consequences of such decisions for financial markets.
We are looking forward to the discussion, so get your tickets here and subscribe to the safeFBDC newsletter to stay in the know.
* GFC stands for Great Financial Crisis in academic papers. For real.