Big Data: New Opportunities in Economic and Statistical Analysis - main page

Big Data - main page

Big Data: New Opportunities in Economic and Statistical Analysis

March 8 - 11

Chair: Per Nymand-Andersen, Advisor, European Central Bank

Encompassing the dramatic rise of computational technology, big data empowers researchers to explore and understand complex statistical as well as economic challenges.  In 2020 covid-19 created a global crisis and now more than ever, it is time to make the most of the opportunities Big Data derives. Big data is already transforming the financial industry: market players actively use data patterns from micro datasets to produce new and timely indicators. But, has Covid-19 created new opportunities and risks that central banks need to consider?

For central banks and regulators, big data opens up unprecedented possibilities. These include: enhancing financial stability assessments, applying new approaches to economic forecasting, as well as obtaining rapid feedback on their policies. Yet, in order to make the most of these new opportunities, the central banking and supervisory community still needs to overcome a number of methodological, technical and institutional challenges. 

This course, “Big Data: New Opportunities in Economic and Statistical Analysis”, will look at key topics from best processes for data management and analytics, the opportunities the cloud brings for data management to what good governance looks like when managing big data.

Each day will feature three hours of expert-led live content to maximize the opportunity to share and learn. The Course chair will ensure participants have opportunities to network throughout the programme.


Global timezone: 11am-2pm (GMT) | 6am-9am (ET) | 7pm-10pm (SGT) 

Big Data: New Opportunities in Economic and Statistical Analysis agenda

Foundations and building blocks of Big Data

11:0011:15

Big Data 2021 - Course introduction and participant welcome

11:00 - 11:15

  • Introductions and welcome from the chair
  • Overview of the training course and key themes
  • Discussion of delegate expectations and particular areas of interest
     
Per Nymand-Andersen

Adviser

European Central Bank

Per Nymand-Andersen is an adviser to Senior Management in Directorate General Statistics at the European Central Bank (ECB). He has developed his expertise in financial markets, securities settlement systems, statistics, big data and communication. Mr Nymand-Andersen is the key editor of the ECB’s Statistics Working Paper Series and a lecturer at the Goethe University in Frankfurt am Main in Central Banking Policy and Transparency. He has an MBA in Economics and Management Science from Copenhagen Business School, Denmark. Prior to joining the ECB, Mr Nymand-Andersen provided market research consultancy services for Eurostat, Luxembourg. He is author of several articles and publications regarding financial markets, statistics and communication.

11:1511:45

Machine learning and AI: utilising Big Data - Big Data 2021

11:15 - 12:00

  • Overview of methods and practices for combining available technologies and digital platforms
  • Management of key operational and ethical risks and challenges
  • Implications for institutional organisation and resources
  • Combining Big Data and machine learning for systemic risk identification and containment
     
David Bolder

Head of Capital and Credit Portfolio Risk

Nordic Investment Bank

David Jamieson Bolder is currently head of the World Bank Group’s model-risk function and will be undertaking a new position with the Nordic Investment Bank this summer. Prior to his present appointment, he provided analytic support to the Bank for International Settlements’ (BIS) treasury and asset-management functions and worked in quantitative roles at the Bank of Canada, the World Bank Treasury, and the European Bank for Reconstruction and Development. He has authored numerous papers, articles, and chapters in books on financial modelling, stochastic simulation, and optimization. He has also published two comprehensive sole-authored books on fixed-income portfolio analytics and credit-risk modelling, respectively. His principal focus during his, almost 25, years as a quantitative-finance practitioner is the application of mathematical techniques towards improving our practical knowledge and informing decision-making in the areas of sovereign-debt, pension-fund, portfolio-risk, asset-liability, and foreign-exchange-reserve management. 

12:0012:45

The building blocks of Big Data

12:00 - 12:45

  • New applications of open source technologies in data collection, management and analysis
  • Overview of key models and analytics frameworks
  • Tips for management of sensitive issues in the areas of security standards and confidentiality
  • Effectively managing large amounts of data
Adrian Waddy

Designer, Technology Delivery

Bank of England

13:0013:45

AI and ML implications for data management and analytics

13:00 - 13:45

  • Current capabilities of AI and machine learning
  • Data management, processing and analysis 
  • Examples of machine learning based software solutions for the regulators and the regulated
  • Discussion: what are the best opportunities in AML and CFT

New challenges for Big Data and data management

11:0011:45

What is new in Big Data and how can central banks make sense of it? - Big Data 2021

11:00 - 11:45

  • Key forces and dynamics shaping the work of economists, statisticians and data analysts in central banks
  • Evolution of data needs for monetary and macro-prudential policy making
  • Opportunities and challenges related to new international standards and initiatives
  • Discussion: how do central banks need to change to make the most of innovation?

12:0012:45

Designing a supervision and surveillance analytics framework - Big Data 2021

12:00 - 12:45

  • Key features of effective risk-based supervision in 2021: where does big data plug in?
  • Technological foundations of surveillance analytics
  • Applications in risk assessment and stress-testing programmes
  • Tips for overcoming data collection and processing issues

13:0013:45

Cloud and data management: innovation and opportunities for central banks - Big Data 2021

13:00 - 13:45

A (very) brief history of cloud computing in central banks  

  • Standout examples of cloud applications in today’s central banking environment 
  • Understanding cyber resilience when using cloud   
  • Discussion: what does it take to effectively manage limitations and potential legal and security risks?
     

Effective use of Big Data

11:0011:45

Good governance: how should central banks manage Big Data - Big Data

11:00 - 11:45

  • Key approaches and challenges to successful governance of Big Data 
  • Framework for effective management of Big Data 
  • Strategies to balance oversight and promote innovation 
  • Discussion: How effective is existing data governance in coping with Big Data?

12:0012:45

Using Big Data for now casting macro-economic indicators - Big Data 2021

12:00 - 12:45

  • Overview of emerging trends in regulatory Big Data
  • Opportunities and challenges of Big Data applications for financial stability and systemic risk analysis
  • Examples of good practice in combining Big Data from the internet, administrative and commercial sources
  • Case study: European Central Bank’s use of Google search data in autoregressive now-casting models

13:0013:45

Visualisation: new tools and techniques for visualising new data sets - Big Data 2021

13:00 - 13:45

  • Data visualization: state of the art
  • Overview of machine-learning based tools and techniques
  • Applications in financial data analytics
  • Discussion: where are the opportunities for central bank communication?
Lyndsey Pereira-Brereton

Data Visualisation Editor

Bank of England

Advanced uses of Big Data

11:0011:45

Effectively leveraging open data - Big Data 2021

11:00 - 11:45

Overview of the benefits and risks of open data 
How to link data with third parties 
Examples of making data more disseminated and consumable 
Workshop: visualising how data is being used

12:0012:45

Text mining: applications in economic analysis (and text analysis) - Big Data 2021

12:00 - 12:45

Taxonomy of data derived from textual databases
Overview of tools and methods for their systematic analysis
Examples of applications in predictive models
Case Study: textual analysis for monitoring macroeconomic developments

13:0013:30

Closing remarks and delegate action plans - Big data 2021

13:00 - 13:45

Summary of the course
Discussion of the observed trends and case studies 
Application of learning points in the delegates’ home organisations
Preparation of action points

Per Nymand-Andersen

Adviser

European Central Bank

Per Nymand-Andersen is an adviser to Senior Management in Directorate General Statistics at the European Central Bank (ECB). He has developed his expertise in financial markets, securities settlement systems, statistics, big data and communication. Mr Nymand-Andersen is the key editor of the ECB’s Statistics Working Paper Series and a lecturer at the Goethe University in Frankfurt am Main in Central Banking Policy and Transparency. He has an MBA in Economics and Management Science from Copenhagen Business School, Denmark. Prior to joining the ECB, Mr Nymand-Andersen provided market research consultancy services for Eurostat, Luxembourg. He is author of several articles and publications regarding financial markets, statistics and communication.

Central Banking’s Post Training is a chair-led forum where participants can bring further questions or discussion points to group, after the live content and self-paced learning elements. What has worked, and what has not? Post Training focuses on questions that may have arisen since returning, the challenges of implementation and the setting of medium-term goals. Participants are encouraged to bring a short presentation, or questions on a particular topic, to gain the most from the discussions with peers.

11:0012:00

Post course catch up
Zoom link to be sent to you after the conclusion of the live content

11:00 - 12:00

Benefits of attending the post course catch up:

  • Developments in the area since the live content sessions, including new resource material
  • Questions arising since returning to the central bank
  • Challenges of implementation: where are the roadblocks?
  • Medium-term goals: what is realistic?
  • Establishment of group network to keep in touch peers and share best practices

Learning outcomes

By the end of the training course, participants will be able to:

  • Effectively manage large amounts of data
  • Understand the opportunities that cloud and data management have and the relationship between cyber resilience and cloud
  • Design a supervision and surveillance analytics framework
  • Use new tools and techniques for visualising new data sets
  • Understand the importance of good governance of big data and create an effect governance framework for balancing oversight and encouraging innovation

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Per Nymand-Andersen

Adviser

European Central Bank