1. United Nations Economic Commission for Europe Statistical Division Towards a Generic Statistical Business Process Model Steven Vale, UNECE

2. Steven Vale - UNECE Statistical Division Slide 2 Contents  Background  Modelling statistical business processes  Applicability  Structure and key features  Relevance to SDMX  Next steps

3. Steven Vale - UNECE Statistical Division Slide 3 Background  Defining and mapping business processes in statistical organisations started at least 10 years ago “Statistical value chain” “Survey life-cycle” “Statistical process cycle” “Business process model”

4. Steven Vale - UNECE Statistical Division Slide 4 Background  Defining and mapping business processes in statistical organisations started at least 10 years ago “Statistical value chain”X “Survey life-cycle”X “Statistical process cycle”X “Business process model”X Generic Statistical Business Process Model

5. Steven Vale - UNECE Statistical Division Slide 5 Modelling Statistical Business Processes  Reached a stage of maturity where a generic international standard is the logical next step  Many drivers for a generic model: “End-to-end” metadata systems development Harmonization of terminology Software sharing Process-based organization structures Process quality management requirements...

6. Steven Vale - UNECE Statistical Division Slide 6 Why do we need a model?  To define, describe and map statistical processes in a coherent way  To standardize process terminology  To compare / benchmark processes within and between organisations  To identify synergies between processes  To inform decisions on systems architectures and organisation of resources

7. Steven Vale - UNECE Statistical Division Slide 7 History of the Current Model  Based on the business process model developed by Statistics New Zealand  Added phases for: Archive (inspired by Statistics Canada) Evaluate (Australia and others)  Three rounds of comments  Terminology and descriptions made more generic  Wider applicability?

8. Steven Vale - UNECE Statistical Division Slide 8 Applicability (1)  All activities undertaken by producers of official statistics which result in data outputs  National and international statistical organisations  Independent of data source, can be used for: Surveys / censuses Administrative sources / register-based statistics Mixed sources

9. Steven Vale - UNECE Statistical Division Slide 9 Applicability (2)  Producing statistics from raw data (micro or macro-data)  Revision of existing data / re-calculation of time-series  Development and maintenance of statistical registers

10. Steven Vale - UNECE Statistical Division Slide 10 Structure of the Model (1) Process Phases Sub- processes (Descriptions)

11. Steven Vale - UNECE Statistical Division Slide 11 Structure of the Model (2)  National implementations may need additional levels  Over-arching processes Quality management Metadata management Statistical framework management Statistical programme management........ (8 more – see paper)

12. Steven Vale - UNECE Statistical Division Slide 12 Key features (1)  Not a linear model  Sub-processes do not have to be followed in a strict order  It is a matrix, through which there are many possible paths, including iterative loops within and between phases  Some iterations of a regular process may skip certain sub-processes

14. Steven Vale - UNECE Statistical Division Slide 14 Key Features (2)  In theory the model is circular: Evaluation can lead to modified needs and design  In practice it is more like a multiple helix: There may be several iterations of a process underway at any point in time

15. Steven Vale - UNECE Statistical Division Slide 15 Mapping to Other Models

16. Steven Vale - UNECE Statistical Division Slide 16 Relevance to SDMX  Process modelling already mentioned in: SDMX User Guide V2 Technical Standards Euro SDMX Metadata Structure  Common terminology  If inputs and outputs use SDMX formats, why not the intermediate processes?

17. Steven Vale - UNECE Statistical Division Slide 17 Standardized process descriptions Harmonised processes Rationalization of software Use of open source and shared components SDMX between components Convergence of business architectures

18. Steven Vale - UNECE Statistical Division Slide 18 Next steps  Several organisations are implementing this model or similar ones  Gather implementation experiences and other comments as input for Part C of the “Common Metadata Framework”  Present to the Bureau of the Conference of European Statisticians  Role in SDMX?

19. Steven Vale - UNECE Statistical Division Slide 19 Questions and Comments? steven.vale@unece.org For more information see the METIS wiki: www1.unece.org/stat/platform/display/metis