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Meeting the Future Demands of a Statistical Organization

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. Financial Crisis – G20 Data Gaps Initiative.

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Meeting the Future Demands of a Statistical Organization

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  1. 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. 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. 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. 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. Organizational specialization and Operational Independence Interface Interface Content Delivery Collection • Production Standards, Processes and Technology

  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. Generic Process Model

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

  11. Work under wayPrototype – End-To-End Process

  12. Work under wayWorkflow – End-User Interface

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