meeting the future demands of a statistical organization
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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. - PowerPoint PPT PresentationTRANSCRIPT
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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 MeisterSenior Information Management OfficerStatistical Information Management, STA
Meeting on the Management of Statistical Information Systems Paris, France 23 - 25 April 2013
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Financial Crisis – G20 Data Gaps Initiative
Data demandsFour-fold increase in data demands in 5 yearsIncreasing trend towards bilateral data
Staff resourcesRemain constant
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Objectives and Goals
Meet the rapidly increasing demands for more data and metadata productsDevelop 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
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Scalable Operations
Meet the rapidly increasing demands for more data and metadata productsStandards
A Generic Production Process Model is possibleWith supporting Technology, Metadata and Work
Practice StandardsSpecialization
Organizational specializationCollection, Production, Content Delivery teams“Standards, Process and Technology” team
Operational independenceUse of generic interfaces between operational teams
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Organizational specialization and Operational Independence
Collection Production Content Delivery
Standards, Processes and Technology
Inte
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Efficient Operations
Increase the timeliness of data and metadata deliveryWorkflow Automation
Automated TasksReduce manual tasks to a minimum
Data exchangesData and Metadata TransformationsQuantitative validationsReport/Email Generation
Automated DecisionsPerform automated tests on data to route work (if needed)Users should only be given tasks when their input is
needed
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Generic Process Model
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Effective Operations
Reduce the incidence of data and metadata errorsCapable and Efficient validation technology
Business user-drivenResponsiveness to evolving business needs
Large portfolio of possible validation testsObservation, Series, Cross-Series, Cross-Database,
Metadata, Data-Metadata validation, Ad-hocMetadata integration
Contextual, OperationalLarge volumes of diagnostics and diagnostic
aggregatesVolume of diagnostics > 10x volume of dataDiagnostic aggregates useful for top-down and
managerial perspectives
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Validation Lifecycle
IdentifyPerform large variety of automated testsBring users to the issues
Diagnostic aggregates, Navigation through results, Visual media
Investigate and DecideHave all the information related to issues on hand
Easy access to related data and metadata (possibly from multiple sources)
ActAd-hoc or procedure based content correctionsComments related to contents or issues for future use
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Work in ProductionValidation
Charts
Detailed Diagnostics
Cross-Database Comparisons
Diagnostic Summary
OLAP Analytics
Metadata Integration
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Work under wayPrototype – End-To-End Process
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Work under wayWorkflow – End-User Interface