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Laurie Reedman and Claude Julien

Current and Future Applications of the Generic Statistical Business Process Model at Statistics Canada. Laurie Reedman and Claude Julien. May 5, 2010. Overview. The Generic Statistical Business Process Model (GSBPM) Quality Assurance Reviews Quality in Publications

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Laurie Reedman and Claude Julien

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  1. Current and Future Applications of the Generic Statistical Business Process Model at Statistics Canada Laurie Reedman and Claude Julien May 5, 2010

  2. Overview • The Generic Statistical Business Process Model (GSBPM) • Quality Assurance Reviews • Quality in Publications • Quality Guidelines 5th Edition • Corporate Business Architecture • Integrated Business Statistics Program

  3. 7 Disseminate 1 Specify Needs 2 Design 3 Build 4 Collect 5 Process 6 Analyse 8 Archive 9 Evaluate 1.1 Determine needs for information 2.1 Design outputs 3.1 Build data collection instrument 4.1 Select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Update output systems 8.1 Define archive rules 9.1 Gather evaluation inputs 1.2 Consult and confirm needs 2.2 Design variable descriptions 3.2 Build or enhance process components 4.2 Set up collection 5.2 Classify and code 6.2 Validate outputs 7.2 Produce dissemination products 8.2 Manage archive repository 9.2 Conduct evaluation 1.3 Establish output objectives 2.3 Design data collection methodology 3.3 Configure workflows 4.3 Run collection 5.3 Review, validate and edit 6.3 Scrutinize and explain 7.3 Manage release of dissemination products 8.3 Preserve data and associate metadata 9.3 Agree action plan 1.4 Identify concepts 2.4 Design frame and sample methodology 3.4 Test production system 4.4 Finalize collection 5.4 Impute 6.4 Apply disclosure control 7.4 Promote dissemination products 8.4 Dispose of data and associated metadata 1.5 Check data availability 2.5 Design statistical processing methodology 3.5 Test statistical business process 5.5 Derive new variables and statistical units 6.5 Finalize outputs 7.5 Manage user support 1.6 Prepare business case 2.6 Design production systems and workflow 3.6 Finalize production system 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data files Levels 1 and 2 Generic Statistical Business Process Model, version 4.0 (Joint UNECE/Eurostat/OECD Work Session, April 2009)

  4. Quality Assurance Reviews • Independent review of the execution (not design) of statistical program • Focus is on quality assurance practices • Objective is to identify “best practices” as well as areas for improvement • Several programs reviewed each year • Summary presented to upper management

  5. Quality Assurance Reviews • Reviewer is a mid level manager with no experience in the program being reviewed • Tools to perform the review: • Program documentation • Meetings with program area managers and staff • Templates for the written report and presentation • GSBPM

  6. Factors to • look for • in particular: • Staffing • Renewal • Training • Workload • Project Management • Schedule • Checklists • Documentation • Sign-off • Risk planning • Change • Systems • Specs • Maintenance • Renewal • Validation • Resources • Tools • Engagement 7 Disseminate 1 Specify Needs 2 Design 3 Build 4 Collect 5 Process 6 Analyse 8 Archive 9 Evaluate 1.1 Determine needs for information 2.1 Design outputs 3.1 Build data collection instrument 4.1 Select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Update output systems 8.1 Define archive rules 9.1 Gather evaluation inputs 1.2 Consult and confirm needs 2.2 Design variable descriptions 3.2 Build or enhance process components 4.2 Set up collection 5.2 Classify and code 6.2 Validate outputs 7.2 Produce dissemination products 8.2 Manage archive repository 9.2 Conduct evaluation 1.3 Establish output objectives 2.3 Design data collection methodology 3.3 Configure workflows 4.3 Run collection 5.3 Review, validate and edit 6.3 Scrutinize and explain 7.3 Manage release of dissemination products 8.3 Preserve data and associate metadata 9.3 Agree action plan 1.4 Identify concepts 2.4 Design frame and sample methodology 3.4 Test production system 4.4 Finalize collection 5.4 Impute 6.4 Apply disclosure control 7.4 Promote dissemination products 8.4 Dispose of data and associated metadata 1.5 Check data availability 2.5 Design statistical processing methodology 3.5 Test statistical business process 5.5 Derive new variables and statistical units 6.5 Finalize outputs 7.5 Manage user support 1.6 Prepare business case 2.6 Design production systems and workflow 3.6 Finalize production system 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data files

  7. Quality Assurance Reviews • Benefits of using the GSBPM: • Common language for describing process steps • Assurance that no steps would be overlooked • Locate where in the process greater risks lie • Compare risks in one process to another • Identify global issues

  8. Quality in Publications • Over 400 statistical programs • Numerous tables, time series, publications and papers • The Daily - First line of communication • Over 1,250 texts published every year

  9. Quality in Publications • Some corrections after release • Corrections are recorded, analyzed, summarized and reported • Corrections on accuracy are further investigated to determine where, how and why error occurred • First level of GSPBM is used to summarize and report

  10. Quality in Publications

  11. Quality Guidelines 5th Edition • Describe a set of best practices for all steps of a statistical program • Target audience is those developing and implementing the statistical program • Guiding principles: • Quality must be built in at each phase of the process • Quality is multidimensional • Guidelines for many boxes in the GSBPM

  12. 7 Disseminate 1 Specify Needs 2 Design 3 Build 4 Collect 5 Process 6 Analyse 8 Archive 9 Evaluate 1.1 Determine needs for information 2.1 Design outputs 3.1 Build data collection instrument 4.1 Select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Update output systems 8.1 Define archive rules 9.1 Gather evaluation inputs 1.2 Consult and confirm needs 2.2 Design variable descriptions 3.2 Build or enhance process components 4.2 Set up collection 5.2 Classify and code 6.2 Validate outputs 7.2 Produce dissemination products 8.2 Manage archive repository 9.2 Conduct evaluation 1.3 Establish output objectives 2.3 Design data collection methodology 3.3 Configure workflows 4.3 Run collection 5.3 Review, validate and edit 6.3 Scrutinize and explain 7.3 Manage release of dissemination products 8.3 Preserve data and associate metadata 9.3 Agree action plan 1.4 Identify concepts 2.4 Design frame and sample methodology 3.4 Test production system 4.4 Finalize collection 5.4 Impute 6.4 Apply disclosure control 7.4 Promote dissemination products 8.4 Dispose of data and associated metadata 1.5 Check data availability 2.5 Design statistical processing methodology 3.5 Test statistical business process 5.5 Derive new variables and statistical units 6.5 Finalize outputs 7.5 Manage user support 1.6 Prepare business case 2.6 Design production systems and workflow 3.6 Finalize production system 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data files Levels 1 and 2 Generic Statistical Business Process Model, version 4.0 (Joint UNECE/Eurostat/OECD Work Session, April 2009)

  13. Quality Guidelines 5th Edition • Future plans to make greater use of the GSBPM: • Provide guidelines for more (all?) Level 2 steps • Base the structure of the Quality Guidelines document on the model itself • Locate specific guidelines by navigating through the model

  14. Corporate Business Architecture • Challenge: maintain quality of products, use fewer resources • Corporate Business Architecture (CBA) is an initiative to address this challenge • CBA task force used the GSBPM to structure its analysis and organize its report • Embedded the GSBPM in their own Core Business Process

  15. Core Business Process

  16. Integrated Business Statistics Program • Redesign of the business statistics program • Align with Corporate Business Architecture • Task force recommendations • Align services with GSBPM • Develop and maintain a business process model • Use corporate services, statistical processing standards, industry best practices and the Corporate Business Architecture principles wherever possible

  17. Conclusions • Pre-occupation with quality assurance • GSBPM is relatively new to us • GSBPM is a good fit for us • GSBPM provides a common framework and tool for communication • Snowball effect – we are finding more and more ways to use it

  18. For more information, please contact: Pour plus d’information, veuillez contacter : Contact information Laurie.Reedman@statcan.gc.ca

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