1 1 Improving interoperability in Statistics Some considerations on the impact of SDMX MSIS 2011 Luxembourg 23 – 25 May 2011 Rune Gløersen IT Director Statistics Norway

2 Contents The characteristics of processes and data at NSIs Applicable standards for various business processes The preconditions for increased interoperability A top-down approach to further standardisation SDMX as part of industrialisation of statistics

3 3 GSBPM – leaving stove pipes

4 4 Data archiving

5 Specify needs DesignBuildCollectDisseminateAnalyseProcessEvaluate Archive Quality Management/Metadata Management Process stages and data archiving Data archiving spans the 4 main business processes

6 Specify needs DesignBuildEvaluate Quality Management/Metadata Management Process stages and data archiving And comprises 4 steady states of the data life cycle

7 Dissemination of aggregated statistics using SDMX SDMX Conv SDMX Common Architecture Can (somewhat) easily be streamlined

8 Dissemination of any statistical data using SDMX SDMX Conv SDMX Common Architecture Requires a paramount strategy

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

10 The diversity of users, needs and data flows Public Domain specific Research Questionnaires Data transfers Registers Common high level models, vocabulary etc

11 Challenges The high-level decision to use SDMX for the exchange of statistical data; how should this be envisaged? –The role of the standardisation experts, the IT experts, the subject domain experts and the top management SDMX implementation is strategic, but is regarded as technical –The importance and impact of the Information Model and the Common Metadata Vocabulary Choosing standards; DDI, SDMX, DSPL etc. –No standard is likely to fit all purposes. –Will a common high-level information model contribute to easier implementation of standards? –Can a high-level information model bridge different standards? Provide well defined interfaces, or develop software to hide the challenges? –Common requirements for the quality of software

12 Improved interoperability is crucial Some trends Organisational interoperability Technological interoperability Semantic interoperability

13 Maturity growth in e-Government Organisational Interoperability Semantical Interoperability Source: 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

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

15 Common Generic Industrial Statistics GSBPMGSIM MethodsTechnology Statistical ConceptsInformation Concepts Statistical HowToProduction HowTo conceptual practical Industrializing Statistics

16 Conclusions Standardisation is not a goal in itself; any standardisation effort must be based on well defined business cases The adoption of SDMX and related standards must be aligned with the on going process oriented developments among NSI’s. Success requires a top-down, management driven approach based on agreed high-level models The statistical community should act as an industry, not only as individuals Standardisation at NSI level must be driven by NSIs Agreeing on common high-level models, creates an opportunity for flexible, targeted and effective solutions on the detailed level, still harmonised within a standardised framework The overall objective should be to provide for increased commercial attention to the industry of statistics