Developing and applying business process models in practice

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’sBusiness Process Model Specify needs 1 Develop and design 2 Build 3 Collect 4 Process 5 Analyse 6 Disseminate 7

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

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

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

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

13. Result 2 – Metadata checklist

14. Result 3 – Metadata overview

15. Metadata systems & Statistics Norways Statistical Business Process Model

16. 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.