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

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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