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Developing a framework for standardisation

Developing a framework for standardisation. High-Level Seminar on Streamlining Statistical production Zlatibor, Serbia 6-7 July 2011 Rune Gløersen IT Director Statistics Norway. Contents. Preconditions for improved standardisation The characteristics of processes and data at NSIs

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Developing a framework for standardisation

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  1. Developing a framework for standardisation High-Level Seminar on Streamlining Statistical production Zlatibor, Serbia 6-7 July 2011 Rune GløersenIT DirectorStatistics Norway

  2. Contents • Preconditions for improved standardisation • The characteristics of processes and data at NSIs • Applicable standards for various business processes • Governance • Some international trends

  3. Reasons for standardising statistical production • Leaving stovepipes • Shift of focus from surveys and products to processes • Introduction of quality frameworks • Coherence and comparability, quality assessments • Quality assurance and audits, risk reduction • Improving efficiency • Internal interoperability, streamlining work processes, cost effectiveness • Globalisation • International interoperability, comparability, benchmarking • Content standardisation • Data and metadata standards • Best practise methods • Technological standardisation • High-level architecture, standardise and reuse tools

  4. Quality

  5. Model for Total Quality and Code of Practice

  6. Cornerstones of standardisation

  7. Cornerstones of standardisation and improved interoperability Organisational interoperability Semantical interoperability Technological interoperability

  8. Enterprise ArchitectureCoherence and interoperability Generic StatisticalBusiness Process Model Best PracticeStatistical Methods ICT- Architecture (Principles) Generic Statistical Information Model

  9. Business processes

  10. GSBPM – leaving stove pipes

  11. Data and semantics

  12. Process stages and data archiving Quality Management/Metadata Management Specifyneeds Design Build Evaluate Data archiving spans the 4 main business processes,and comprises 4 steady states of the data life cycle

  13. Interface Users Master Metadata Interface Statistics production systems Classifi-cations StatisticalProducts Input datadefinition About theStatistics Variables DataDoc

  14. Adopting standards Quality Management/Metadata Management Specifyneeds Design Build Evaluate DDI SDMX ?

  15. IT

  16. IT-solutions must be built upon standardmethods, a standardinfrastructure and be in accordance with Statistics Norway’s business architecture IT-business alignment Open standards Our IT-solutions must be platform independent and component based, shared components must be used wherever possible It must be possible to create new IT-solutions by integrating existing and new functionality Our services must have clearly defined, technology-independent interfaces Distinguish between user interface, business logic and data management (layered approach) End-user systems must have uniform user interfaces Store once, reuse may times(avoid double storage) Data and metadata must be uniquely identifiable across systems 10 IT architecture principles

  17. A layered and modularised model for a coherent, streamlined production system Users Monitor and Manage Tools and services Workflow Data and metadata

  18. Governance

  19. Project Portfolio ManagementPrioritized and followed up by the Top Management Project- Directive Project- plan Project- Proposal Planning Project Execution Decision Decision Assessment Reports Assessment Priority parameters Score Comments Approved project plans Detailed requirements Available resources

  20. Service Level AgreementsSystems Maintenance Statistics NorwayOrganisation and Management • One SLA for each of the subject matter departments • Approx. 400 IT systems are maintained for the 4 Statistics Production Departments. In addition approx.70 systems for data collection, administration and dissemination • An SLA covering Common Services is under development SLA Common Services SLA Departments Appendix Appendix

  21. Some international trends

  22. The diversity of users, needs and data flows Common high level models, vocabulary etc Questionnaires Public(re)use Data transfers Registers Domain specificanalysis Research andData Integration

  23. Maturity growth in e-Government OrganisationalInteroperability Legislation,Whatever AligningStrategies Joining ValueCreation Common information models, process models and service catalogues, shared development costs SharingKnowledge Share best practises, metadata specifications,Set up standards for technical systems and dataexchange Aligning WorkProcesses Bilateral data exchange, semi automated,Technical specifications and standards SemanticalInteroperability Source: www.semicolon.noAnalytical Framework for e-Government Interoperability

  24. Industrializing Statistics Statistical Concepts Information Concepts conceptual GSBPM GSIM Common Generic Industrial Statistics Methods Technology practical Statistical HowTo Production HowTo De-coupling content and technical standardisation

  25. Thank you for your attention! Questions or comments…

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