The explosion in data sources, quantity and type (mobile, social, IoT and more combined with technological advancements in data science) has created both challenges and opportunities for financial services companies. The challenge lies in managing the vast amounts of data, and the opportunity—through advanced technology—is to gain deep customer insights, at scale. After the financial crisis of 2008, regulation opened the way for new entrants, who built their value proposition on a sophisticated use of data. In contrast, traditional banks have historically organised data in silos according to the markets in which they operate (retail banking, wealth management, etc.). A change in Data Strategy is a common statement shared by Dreamquark, Microsoft and NetApp.

For Rob Benoit, infrastructures modernization is the reality for many financial institutions who want to get real time insights from their data and make it available and closer to their business. Those institutions need to transform themselves into data-driven companies able to aggregate data into single sources available across a centralized set of applications. Such strategy will require somehow to rationalize the volume of data to make it available at the right place and the right time into hybrid environments. The same statement for a structured approach for data estate was echo by Patrice Amann “Data is the new currency. There is no great AI with no great data”. Financial institutions need a modernized Data model, more accurate and industry specific. With a comprehensive classification of data and the right governance in place, Financial institutions can start ingesting data, store data, apply process to extract the knowledge, visualize in dashboards, and then project the extracted knowledge into business applications. Data is the new currency and Financial institutions have indeed a strong appetite to use data but there are still barriers for Financial sector to move AI projects from pilot to deployment at scale as explained by Nicolas Meric. It is a combination of different elements such as fragmented data, operating the proper tools for the business, delivering ethical AI and explainability. The ability to extract the right data from the core banking toward AI solutions operated in hybrid-cloud environment is a necessary evolution.

The real transformation of banks will undoubtedly come through the exploitation of data, and the ability to rely on AI. The modernisation of infrastructures and better data management should enable the financial institution to create their Data-Fabric and an AI-Fabric to optimise their business results!

If you want to learn more from Dreamquark, Microsoft and NetApp on the evolution of the regulatory framework, data management best practices and key factor of success, we invite you to watch the replay of the discussion below.