Jay Krish

The future calls for flexibility in design and architecture of our data systems. As organizations continue to grow the ways they can leverage their data, developing an architecture that is scalable, agile, and easily maintained will be key to ensuring business value can be easily provided by data teams. This fireside chat will look at the concept of data modulization and will highlight: 

• The benefits of data modulization in financial data management.  
• Hurdles and opportunities that those interested in data modulization should be aware of. 
• Examples and use cases of data modulization across various industries.  

The rapid advancement of AI technologies is transforming the landscape of model and data governance in financial institutions. As AI systems become more sophisticated and integral to operations, data managers face new challenges in ensuring compliance, transparency, and ethical use of these technologies. The evolving nature of AI necessitates a proactive approach to governance frameworks that can adapt to emerging risks and regulatory requirements. Join us as we explore the key considerations for evolving model and data governance in the age of AI for financial institutions and learn how:

• Implement AI-specific governance policies that address model explainability, bias detection, and algorithmic accountability.
• Establish cross-functional teams to oversee AI development, deployment, and ongoing monitoring throughout the model lifecycle.
• Develop robust data quality and lineage processes to ensure the integrity and traceability of AI training and operational data.
• Foster a culture of responsible AI by engaging stakeholders, providing comprehensive training, and recognizing champions who drive governance initiatives