1. The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management Meeting the Future Demands of a Statistical Organization Laurent Meister Senior Information Management Officer Statistical Information Management, STA Meeting on the Management of Statistical Information Systems Paris, France 23 - 25 April 2013

2. 2 Financial Crisis – G20 Data Gaps Initiative  Data demands  Four-fold increase in data demands in 5 years  Increasing trend towards bilateral data  Staff resources  Remain constant

3. 3 Objectives and Goals  Meet the rapidly increasing demands for more data and metadata products  Develop a model that is scalable  Increase the timeliness of data and metadata delivery  Increase efficiency of data and metadata collection, processing and content delivery  Reduce the incidence of data and metadata errors  Increase the quality and volume of data and metadata validation performed

4. 4 Scalable Operations  Meet the rapidly increasing demands for more data and metadata products  Standards  A Generic Production Process Model is possible  With supporting Technology, Metadata and Work Practice Standards  Specialization  Organizational specialization  Collection, Production, Content Delivery teams  “Standards, Process and Technology” team  Operational independence  Use of generic interfaces between operational teams

5. 5 Organizational specialization and Operational Independence CollectionProduction Content Delivery Standards, Processes and Technology Interface

6. 6 Efficient Operations  Increase the timeliness of data and metadata delivery  Workflow Automation  Automated Tasks  Reduce manual tasks to a minimum  Data exchanges  Data and Metadata Transformations  Quantitative validations  Report/Email Generation  Automated Decisions  Perform automated tests on data to route work (if needed)  Users should only be given tasks when their input is needed

7. 7 Generic Process Model

8. 8 Effective Operations  Reduce the incidence of data and metadata errors  Capable and Efficient validation technology  Business user-driven  Responsiveness to evolving business needs  Large portfolio of possible validation tests  Observation, Series, Cross-Series, Cross-Database, Metadata, Data-Metadata validation, Ad-hoc  Metadata integration  Contextual, Operational  Large volumes of diagnostics and diagnostic aggregates  Volume of diagnostics > 10x volume of data  Diagnostic aggregates useful for top-down and managerial perspectives

9. 9 Validation Lifecycle  Identify  Perform large variety of automated tests  Bring users to the issues  Diagnostic aggregates, Navigation through results, Visual media  Investigate and Decide  Have all the information related to issues on hand  Easy access to related data and metadata (possibly from multiple sources)  Act  Ad-hoc or procedure based content corrections  Comments related to contents or issues for future use

10. 10 Work in Production Validation Charts Detailed Diagnostics Cross-Database Comparisons Diagnostic Summary OLAP Analytics Metadata Integration

11. 11 Work under way Prototype – End-To-End Process

12. 12 Work under way Workflow – End-User Interface