Elena Sobol

Director, Data Governance and Analytics
Canadian Imperial Bank of Commerce

Elena is a Banking industry professional with over 20 years of progressive international experience including 15 years across Capital Markets: Front Office, Risk Management, Business Management, Technology and Operations. Having solid Risk Management and Capital Markets products experience, Elena led a number of Regulatory Compliance data intensive initiatives. In previous roles Elena managed Counterparty Credit Risk measurement and reporting groups for Canadian and European Banks. Recently led the implementation of Master Data Management and Data Quality Solution. Elena facilitates Capital Markets Data Executive Committee in Canadian Imperial Bank of Commerce.

Elena possesses Master degree in Statistics and Economics.

Check out the incredible speaker line-up to see who will be joining Elena.

Download The Latest Agenda

Operational, Technology and Business Foundations for Success

Thursday, May 12th, 2016

09:30 Panel Discussion | Mastering Business Metadata and Providing Data Lineage

Metadata management is one of the pillars to the house of data management. A recent industry survey identified metadata management as an ongoing pain point for financial institutions. How can you better understand metadata and data lineage from the point of view and standpoint of the business? How do you peel back the onion and see lineage from the point of system of record and help the business to be the drivers of data?
Panelists will discuss:
• How to determine where business metadata needs to be deepest and most accurate
• How to make lineage more readable to auditors and downstream consumers
• How to build a sustainable set of roles, processes and a repository to maintain it and mature the data management organization

Innovation, Data Quality and Compliance

Sunday, June 12th, 2016

11:00 Panel Discussion | Developing and Maturing Data Quality Monitoring

Fixing data quality is the plumbing of a huge skyscraper, it cannot be fixed overnight. After building in metadata, terms and definitions, the next phase is a strong framework around data quality that can be continuously monitored.
Find out how to:
• Develop metrics to measure and prove data quality while reducing redundancy
• Quickly get up the data quality curve if you’re behind
• Balance data quality activities with new opportunities, including advanced analytics