Agenda

Agenda

Agenda

08:3009:00

Registration and refreshments

08:30 - 09:00

09:0010:30

Implementing a data governance framework

09:00 - 10:30

  • What is data management and data governance?
  • How does data management add value to the business?
  • The pillars for succeeding in data governance
  • Policy, roles and responsibilities, processes
  • How to identify critical data governance elements
  • Creating and using data lineage effectively
  • Assessing the current maturity of your data governance program using data management maturity model and other capabilities

 

Dennis Slattery

CEO

EDMWorks

10:3010:45

Morning break

10:30 - 10:45

10:4512:00

Data quality and data culture

10:45 - 12:00

  • Case study example: Lack of high quality ESG data
  • Poor data quality and core infrastructure and its challenges 
  • Creating sustainable data quality
  • Culture of hiding mistakes
  • Fixing anomalies

12:0013:00

Lunch

12:00 - 13:00

13:0014:30

Data quality metrics

13:00 - 14:30

  • Establishing data quality metrics and thresholds
  • Data quality management center 
  • Measurement and DQ accountability
Alexander Denev

Head of AI - financial services, risk advisory

Deloitte LLP

Alexander Denev has more than 15 years of experience in finance, financial modelling and machine learning and he is the former lead of the Advanced Analytics & Quantitative Research at IHS Markit. He has written several papers and two books on topics ranging from stress testing and scenario analysis to asset allocation. He is currently writing his third book on Alternative Data in Trading&Investing. Alexander Denev attained his Master of Science degree in Physics with a focus on Artificial Intelligence from the University of Rome, and he holds a degree in Mathematical Finance from the University of Oxford, where he continues as a visiting lecturer.

14:3015:00

Afternoon Break

14:30 - 15:00

15:0016:30

Data privacy

15:00 - 16:30

  • Accountability and best practices
  • Inclusiveness and equality
  • GDPR: legal and ethical concerns with persisting data, ownership and consumer rights
  • Data issues across different jurisdictions globally 
  • Need for algorithmic transparency and accountability
Drago Indjic

Data Science Practitioner

Oxquant

Drago’s hedge funds career continued in fintech as a data science practitioner at Oxquant (Oxford; FCA #232160), co-founder of Soft-Finance (Geneva), Technological Partnership (Belgrade) and Richfox Capital (Amsterdam). ETFmatic.com “robo” app runs his implementation of a fully digital investment process, following quant/PM roles in various hedge funds, a sovereign wealth fund and a multi-family office, Drago has co-edited the Legaltech book for Wiley (2020) and lectures part-time at Queen Mary and Regents. Member of the IEEE and IET, PhD (Imperial College), Dipl Ing (Belgrade), fintech and AI funding reviewer for the EU and @dindjic on Twitter.

16:3016:35

End of day one

16:30 - 16:35

08:3009:00

Refreshments

08:30 - 09:00

09:0010:30

Data security

09:00 - 10:30

  • Creating a single security master
  • Cyber incident response

10:3010:45

Morning break

10:30 - 10:45

10:4512:00

Bringing your data in to cloud environments

10:45 - 12:00

  • Benefits of the cloud
    • Agile innovation – reallocating resources to deliver products and services faster to the market
    • Improving security, reducing financial crime and cost savings
  • Machine learning and deep learning
  • Multiple data centres, data residency, security and control 
  • Vendor lock-in
  • Shared liability

12:0013:00

Lunch

12:00 - 13:00

13:0014:30

Alternative data for investors

13:00 - 14:30

  • Natural language processing and structure text based data
  • Python tools
  • Investor use cases for alternative data for a number of different types of datasets
  • Leveraging the right FX flow data to measure supply and demand of a particular currency

14:3015:00

Afternoon Break

14:30 - 15:00

15:0016:30

Regulatory compliance and data-driven decisions

15:00 - 16:30

  • The hype of “big data” in the investment management industry.
  • Data usage by financial advisors and inferences about the profitability of investment strategies.
  • Outlier-dependence and regulatory responsibility.
  • Machine-learning (ML) methods and generation of regulatory-compliant “synthetic” financial data.

16:3016:35

End of course

16:30 - 16:35