The Adoption of METIS GSBPM in Statistics Denmark

1. The Adoption of METIS GSBPMin Statistics Denmark

2. Agenda • Background and context • Working with business processes • An example of documentation • Results of process analysis • Metadata coverage • Lessons learned

3. Agenda • Background and context • Working with business processes • An example of documentation • Results of process analysis • Metadata coverage • Lessons learned

4. Working group on standardisation • Multi-annual corporate strategy as basis (”Strategy 2015”) • Working group, that refers to Board of Directors • METIS GSBPM adopted as common frame • Dual focus • Process analysis and documentation • Coverage of metadata systems

5. Quality management / Metadata Management 1 Specify Needs 2 Design 3 Build 4 Collect 5 Process 6 Analyse 7 Disseminate 8 Archive 9 Evaluate 8.1 Define archive rules 6.1 Prepare draft outputs 7.1 Update output systems 3.1 Build data collection instrument 9.1 Gather evaluation inputs 5.1 Integrate data 1.1 Determine need for information 2.1 Design outputs 4.1 Select sample 6.2 Validate outputs 7.2 Produce dissemination products 3.2 Build or enhance process comp. 8.2 Manage archive repository 2.2 Design variable descriptions 5.2 Classify & code 9.2 Conduct evaluation 1.2 Consult & confirm need 4.2 Set up collection 2.3 Design data collection methodology 5.3 Validate & edit 8.3 Preserve data & associated metadata 4.3 Run collection 7.3 Manage release of dissem. prod. 9.3 Agree action plan 1.3 Establish output objectives 3.3 Configure workflows 6.3 Scrutinize & explain 5.4 Impute 7.4 Promote dissemination products 6.4 Apply disclosure control 2.4 Design Frame & sample methodology 1.4 Identify concepts & variables 3.4 Test production systems 8.4 Dispose of data & assoc. metadata 4.4 Finalize collection 7.5 Manage user support 5.5 Derive new variables & stat. units 2.5 Design stat. processing methodology 3.5 Test statistical business process 1.5 Check data availability 6.5 Finalize outputs 2.6 Design prod. systems / workflows 1.6 Prepare business case 3.6 Finalize production system 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data files

6. Reference document – ”SD’s METIS” • METIS: confirmed standard for official statistical production • Adopted by some of our peers • Translation of document • Approach for SD version • Testing the extent to which the model apply to SD • An ”SD METIS” would be a milestone for business process- and architectural maturity • Necessary to move ahead according to our corporate objective of increasing standardisation • Initial focus on phases 4-7

7. Agenda • Background and context • Working with business processes • An example of documentation • Results of process analysis • Metadata coverage • Lessons learned

8. Model/template for statistical business processes • METIS level (“which phases do we open”?) • Control-flow level (phases, input, output, time) • Functional level (”who does what, and in what order?”) • ”AS-IS” and/or ”TO-BE” • BPMN: Standardized notation • Collect ideas and convert them into action (standardisation, efficiency and quality) • Form • Workshop • Facilitated by working group • Ownership of results to the statistical team • Needs a mandate!

9. Selection of pilot cases • Social Statistics: • Population register • Student register (register updates) • Business Statistics • General account statistics (SBS) • Employment in construction industries • Retail Trade Index • Industrial commodity statistic • Farm Structure Survey • Car register and associated statistics • Use of ICT in enterprises • Economic Statistics • Consumer price index • Foreign trade in services • Sales and Marketing • Interview task: Yearly survey on safety • Key figures in housing (standardized product from SDs Customer Services Centre) • User Services • Data collection-processes/-systems (XIS, CEMOS)

10. Selection of cases in Business Statistics

11. Agenda • Background and context • Working with business processes • An example of documentation • Results of process analysis • Metadata coverage • Lessons learned

12. Example: METIS level

13. Example: Control flow level • Trigger • Phases • Input • Regulations • Data • etc. • Output • Intermediate • Final • Time

14. Example: Functional level • Who does what • Start condition • End condition • Note that…

15. Agenda • Background and context • Working with business processes • An example of documentation • Results of process analysis • Metadata coverage • Lessons learned

16. Results of process analysis (an overview) • Focus on processes is useful and has immediate effect in some cases • Improvements for statistical teams • Quality (documentation, new quality measures, etc.) • Standardisation (Use of standardised systems) • Efficiency (Eliminate manual processes) • Improvements in communication • Many project managers regarding digitalisation • Coordinator function • Improvements in efficiency for data collection • Focus on areas of responsibility • Huge difference in degree of standardisation • Dissemination • Data collection • Data processing

17. Agenda • Background and context • Working with business processes • An example of documentation • Results of process analysis • Metadata coverage • Lessons learned

18. Metadata coverage

19. Metadata coverage • Dissemination phase is very well covered • Although dissemination phase is covered by four different applications the overlap is very limited • The vision for the future is to create a single metadata system • The data model should be based on three data stages (raw data, micro data, macro data)

20. Metadata coverage

21. Agenda • Background and context • Working with business processes • An example of documentation • Results of process analysis • Metadata coverage • Lessons learned

22. Lessons learned • Planning a strategy for further development is better using GSBPM • Identify areas of interest for improvement initiatives. • Major challenges regarding steps where data is processed • Further standardization of methods is necessary • A clearer view of the different need for metadata and documentation • A better overview of the strong and the weak areas of our metadata applications