1. Developing and applying business process models in practice Statistics Norway Jenny Linnerud and Anne Gro Hustoft

2. Business Process Model (BPM) for Statistics Norway Project within our programme on improvement and standardisation of statistical production (FOSS) Progress BMP project started in March 2008 and ended mid-August 2008 Resources 520 man-hours were used

3. BPM project group The project group consisted of 9 members of the FOSS coordination group, who represent different professional areas within the process: management support, data processing, IT industry, labour market statistics, registers IT development, metadata, sample surveys, population statistics and statistical methods.

4. Statistics Norway’s Business Process Model Develop and design 2 Build 3 Collect 4 Process 5 Analyse 6 Disseminate 7 Specify needs 1

5. Business Process Model Specify needs 1 Develop and design 2 Build 3 Collect 4 Process 5 Analyse 6 Disseminate 7 Consult and confirm need 1.2 Check data availability 1.4 Establish output objectives 1.3 Prepare business case 1.5 Prepare data for dissemination database 7.1 Produce product 7.2 Release and promote product 7.3 Classify and code 5.1 Micro-edit 5.2 Macro-control 5.3 Impute for partial non-response 5.4 Interpret and explain statistics 6.4 Establish frame and registers, select sample 4.1 Set up collection 4.2 Run collection 4.3 Finalise collection 4.4 Outputs 2.1 Data collection methodology 2.3 Process and analysis methodology 2.4 Production system 2.5 Integrate production system with other systems 3.2 Test production system 3.3 Finalise production system 3.4 Acquire domain intelligence 6.1 Produce statistics 6.2 Prepare statistics for dissemination 6.5 Finalise content 6.6 Frame, register and sample methodology 2.2 Determine need for information 1.1 Manage user queries 7.4 Calculate weights and derive variables 5.5 Quality assure statistics 6.3 Build and enhance process components 3.1

6. Phase 5. Process Classify and code 5.1 Micro- edit 5.2 Macro- control 5.3 Imputation for partial non-response 5.4 Calculate weights and derive variables 5.5 Code and store micro-data 5.1.3 Prepare derived variables 5.5.4 Link data sources and establish statistical registers 5.1.1 Evaluate imputations 5.4.2 Perform manual editing 5.2.2 Identify and investigate outliers and critical values 5.3.1 Perform controls at macro-level 5.3.2 Calculate weights 5.5.2 Run automated control and correction routines 5.2.1 Identify and establish statistical units 5.1.2 Run imputation routines for partial non-response 5.4.1 Supplement statistical registers 5.5.3 Impute for unit non-response 5.5.1 Store micro-data 5.5.5 Data ready for processing Data ready for analysis

7. Comparison with Generic Statistical Business Process model Specify needs 1 Develop and design 2 Build 3 Collect 4 Process 5 Analyse 6 Disseminate 7 Consult and confirm need 1.2 Check data availability 1.4 Establish output objectives 1.3 Prepare business case 1.5 Prepare data for dissemination database Update output systems 7.1 Produce products 7.2 Release, 7.3 market and promote product 7.4 7.3 Classify and code 5.3 Micro-edit 5.2 Macro-control 5.3 Impute for partial non-response 5.4 Interpret and explain statistics 6.4 Establish frame and registers, select sample 4.1 Set up collection 4.2 Run collection 4.3 Finalise collection Load data into processing environment 4.4 Outputs 2.1 Data collection methodology 2.3 Process and analysis methodology 2.4 Production system Processing systems and workflow 2.5 Integrate production system with other systems Configure workflows 3.3 Test production system 3.4 Finalise production systems 3.5 Acquire domain intelligence 6.1 Produce statistics Prepare draft outputs 6.2 Prepare statistics for dissemination Disclosure control 6.5 Finalise content outputs for dissemination 6.6 Frame, register and sample methodology 2.2 Determine need for information 1.1 Manage user customer queries 7.5 Calculate weights 5.6 and derive new variables 5.5 Quality assure statistics Verify outputs 6.3 Build and enhance process components 3.2 Data collection instyument 3.1 Standardise and anonymise 5.1 Integrate data 5,.2 Calculate aggregates 5.7 Edit and impute 5.4

8. This process is associated with, among other things: - Quality control in every processes - Identify and propose process-related improvements - Collection, follow-up and analysis of process data - Identify and propose product-related improvements - Collection, follow-up and analysis of user and customer feedback - Quality indicators

9. Examples of resources under this: Legal acts Control documents e.g. IT-strategy Systems and associated documentation Templates, guidelines and handbooks Committees, fora, expert groups Support processes, e.g. ITIL (IT Infrastucture Library) Data storage and administration Population administration Cross cutting: Security International activities Financial matters Competence and development Last but not least: Business Process Model

10. Recommendations from the BPM development project The business process model will need to be reviewed and updated to ensure that it reflects the real state of affairs at any time. The model originally in Norwegian was translated into English for international use. A process guide for the model should be made available on Statistics Norway’s intranet.

11. Case study - Description of the production process for Price index for legal services with emphasis on the use of metadata throughout the process. –Description of the process for a new statistic and for future publishing of the same statistic. –Creation of a metadata checklist that can be used whenever this type of statistics is produced. - 7 participants: statistics, IT, metadata - 435 man-hours used.

12. Result 1 – New statistic ProcessActivitiesActors 1 Specify needs Statistics division, Eurostat, National accounts, Branch organisation, businesses, Justice department 1.1 Consult and confirm need Discuss need for price index with national accounts & branch organisation

13. Result 2 – Metadata checklist ProcessMetadata checklist 1Specify needs 1.1Consult and confirm need Update product register, make resource estimates and project description. 1.2Establish output objectives Check for existing variables and classifications and update if necessary.

14. ProcessCreateUseUpdate 6.5 Prepare statistics for dissemination New classifications for new statistics, if necessary Existing classifications Classifications for established statistics New variables for new statistics, if necessary Existing variables Variables for established statistics Result 3 – Metadata overview

15. Specify needs Develop & design BuildCollectProcessAnalyseDisseminate Variables XXX Classifications XXXX File descriptions XX Questionnaires XX Rules XXX About the statistics XX About the data collection X Metadata portal X Metadata systems & Statistics Norways Statistical Business Process Model

16. Specify needs Develop & design BuildCollectProcessAnalyseDisseminate Eurostat XX Branch organisations XXX Businesses XXXX Justice department X Director general XX Head of department XX Head of division XXXXX Resp. statistics XXXXXXX Different actors & Statistics Norways Statistical Business Process Model

17. Conclusions - case study -Process improvements were suggested and made - Include metadata documentation and linking of metadata in formal approval procedure - Suggestions for improved functionality in systems were identified and improvements made.

18. Conclusions – BP model - The method of documenting a statistic based on the Statistical Business Process Model, can be used for other statistics. - Documentation of new and established statistics is useful for training new employees and for rotation of current employees

19. Conclusions – BP model – cont. -The business process model is an important tool in planning, standardising and improving work processes in statistical production, and for training purposes. -The business process model is also a communication tool for standardisation and cooperation between statistical agencies and government departments.