Healthy Data Podcast #4
Joe Lichtenberg and Larry Tabb discuss how banks can leverage technology and healthy data to succeed amid volatile markets.
Volatility has become the standard in stock markets across the world over the past several months. Ever since COVID-19 fired off an economic recession, capital markets firms have fought to stabilize their operations. But one key to success amid explosive markets lies within each organization: data.
This week on Healthy Data, we host a discussion on the need for speed and scale in the financial services sector, with its ever-rising number of transactions. Our guests are Joe Lichtenberg, director of product and industry marketing for InterSystems, and Larry Tabb, founder and research chairman of the research and consulting firm the Tabb Group. Together, they examine how capital markets—and banks’ customers—can benefit from healthy data, now and long into the future.
This conversation occurred prior to the COVID-19 pandemic, but its insights remain critical. Tabb and Lichtenberg, for instance, discuss trends among the world’s top banks, who are racing to implement high-performance data technologies—without the drawbacks of in-memory databases. Multi-level architectures that require banks to stitch together a database and a data grid or data fabric simply won’t cut it. Integration and consistency are key. Losing data is, of course, detrimental to the success of any bank. Lichtenberg and Tabb go on to analyze the trend toward built-in persistence and enterprise-wide data fabrics, with guaranteed consistency.
When banks pin down their strategy to achieve and leverage healthy data, anything is possible. They can implement real-time advanced analytics, unlock efficiencies in the front and middle offices, and execute a successful algorithmic trading program. The benefits of a data-driven future for capital markets are limitless.
But the first step is, as always, data that flows seamlessly and is ready for action.
So, tune in and let us know your thoughts. How is your bank using data to stay ahead of the curve? And where can you improve?
Listen to our podcast on: