Opening & Welcome

Practitioner Innovation Keynote: CDO TBC

CDO Panel: How to build a data strategy for AI, to mitigate risk and gain business value

• How is the role of the CDO and the CDO office evolving in the AI and GenAI world?
• How should CDOs align data strategy and AI with the broader business objectives to ensure it contributes to the firm’s value?
• How should CDOs evaluate and select the right AI technologies to support AI data strategy? What are the most important considerations in these selections?
• More specifically, how can CDOs manage the risks of poor quality data in GenAI models?
• How should CDOs create a robust framework to ensure responsible use of GenAI and a positive approach to data ethics?
• How do you measure the return on investment (ROI) of your AI initiatives? In what ways have they created value for the business?

Keynote: Arcesium

Keynote: 

Panel: Designing for the future – How to simplify and modernize data architecture to unleash data value and innovation 

• Where are financial institutions on the journey to data architecture modernization and what stage of this transformation journey are most firms currently at?
• What are the most significant challenges financial institutions face in simplifying and modernizing their data architecture, and what strategies can be employed to overcome these obstacles?
• In what ways can cloud technologies be used to modernize data architecture and enhance the value of data in financial institutions?
• Beyond cloud computing, what emerging technologies hold the most promise for revolutionizing data architecture in financial services?
• What are the best practices for utilizing data marketplaces and sharing capabilities to enable innovation and reduce complexity accessing essential data?
• Where has optimizing and modernizing data architecture significantly impacted business value, including the investment required and ROI metrics?

Panel: How to set up a data products strategy to deliver business value

• What are the drivers behind the creation of data products by the business?
• How should financial institutions define and identify data products, and what are their common use cases?
• How can a CDO office create the right environment, guidance and culture to enable business development of data products?
• When considering data products, how can firms manage data ownership and ensure data quality?
• What are the data architecture challenges required to successfully deliver a data products ecosystem?
• What are the challenges in operationalizing data products?
• What are the benefits of focusing on data products as an approach to accelerating data value creation?

Keynote: 

Best practice approaches to self service analytics to empower users and unlock data democratization

• What are the essential elements of a self service analytics program?
• What are the challenges from the business in delivering successful self service analytics?
• How should a CDO office set up and create the right environment and culture to enable self service analytics?
• How can firms balance innovation in analytics with appropriate data controls and entitlements?
• How can data quality management and metrics be implemented to ensure self service analytics are underpinned by trusted data?
• How can AI be applied along the data value chain to fix issues and help firms scale self service analytics? What new data governance challenges does the use of AI bring?

Best practice approaches to data management for regulatory reporting

• What approaches are firms taking to meeting increased demands from regulators for higher quality and more granular data for reporting?
• What are the data management challenges for global firms that have to meet global and local requirements and work across multiple regulatory regimes?
• How is data standardization and harmonization progressing and what more needs to be done?
• What is the role of AI and GenAI in data management for regulatory reporting?
• How are market participants leveraging reporting data to make better decisions for the business?

Towards data intelligence – how to organize, integrate and structure data for successful AI

• Where are firms on their journey of tackling data silos and creating a complete picture of the data landscape?
• What is the role of metadata in making relevant and intelligent data searchable and accessible for the business?
• How are new tools and techniques being used to unify disparate sources of structured and unstructured data?
• When considering scalability and optimizing performance, what are the considerations for database selection?
• As firms re-architect data stacks, should they be centralizing data governance and controls (data privacy, security) in order to mobilize data across the organization efficiently?
• Why is data literacy important and what skills and types of teams can support the journey towards data intelligence?

Networking drinks and Data Management Insight Awards Ceremony