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

2. 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. 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. 4 4 Quality

5. 5 Model for Total Quality and Code of Practice

6. 6 6 Cornerstones of standardisation

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

8. 8 Enterprise Architecture Coherence and interoperability Generic Statistical Business Process Model ICT- Architecture (Principles) Generic Statistical Information Model Best Practice Statistical Methods

9. 9 9 Business processes

10. 10 GSBPM – leaving stove pipes

11. 11 Data and semantics

12. 12 Specify needs DesignBuildEvaluate Quality Management/Metadata Management Process stages and data archiving Data archiving spans the 4 main business processes, and comprises 4 steady states of the data life cycle

13. 13 Classifi- cations Interface Master Metadata Statistics production systems Data Doc Variables Statistical Products Input data definition Users Interface About the Statistics

14. 14 Specify needs DesignBuildEvaluate Quality Management/Metadata Management Adopting standards DDI SDMX ?

15. 15 IT

16. 16 10 IT architecture principles IT-solutions must be built upon standard methods, a standard infrastructure 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

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

18. 18 Governance

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

20. 20 Statistics Norway Organisation and Management SLA Common Services Appendix SLA Departments Appendix Service Level Agreements Systems Maintenance 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

21. 21 Some international trends

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

23. 23 Maturity growth in e-Government Organisational Interoperability Semantical Interoperability Source: www.semicolon.no Analytical Framework for e-Government Interoperabilitywww.semicolon.no Sharing Knowledge Aligning Work Processes Joining Value Creation Aligning Strategies Bilateral data exchange, semi automated, Technical specifications and standards Share best practises, metadata specifications, Set up standards for technical systems and data exchange Common information models, process models and service catalogues, shared development costs Legislation, Whatever

24. 24 Common Generic Industrial Statistics GSBPMGSIM MethodsTechnology Statistical ConceptsInformation Concepts Statistical HowTo Production HowTo conceptual practical Industrializing Statistics De-coupling content and technical standardisation

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