You’ve been specializing in financial information for some time. What has changed the most over the years?
I took my first steps in the financial market data business as a data analyst at Refinitiv, collecting data on the Swiss, Austrian, and German bond markets. A job in data was not really understood back then, let alone seen as an attractive career path. I never thought that this experience would be the starting point of an international career. Today, skills in data are highly sought after across all sectors as all companies strive to use their data assets to improve their performance.
Data is particularly important for the financial markets. It’s the fuel that drives them. Without data there’s no trading, no investment, no risk management, and no compliance. Where data and technology meet is where the magic happens. Data science, discovery and exploitation of new data sets, digitization, artificial intelligence, and machine learning will transform our industry.
What role does SIX play in this ecosystem?
It makes no sense for each and every financial institution to source the same data multiple times over. While SIX takes care of providing well-connected data and valuable insights, our clients can concentrate on their core business. As one of the world’s leading providers of financial data, we’re an essential link in the global financial value chain.
Our clients are under cost pressure, but need to innovate, digitize their businesses, and meet new customer demands. It’s our role to provide frictionless data services that enable them to do that, and we’re in an ideal position to do so. SIX helps financial institutions to better serve their customers, assess risk, and enhance returns.
How important is alternative data in this?
With increasing investor demand for ethical investing and with the progression of data science and innovation, there is no dispute that alternative data, especially ESG data, will continue to gain importance. However, it is important that this data is connected to core reference data. Otherwise, it’s meaningless and leads to data scientists who are busy cleaning data rather than analyzing it.