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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
2 Design
3 Build
4 Collect
5 Process
6 Analyse
7 Disseminate
1 Specify
Needs
3.5 Test statistical
business process
3.4Test
productionsystem
3.3
Configureworkflows
3.2 Build or enhance
processcomponents
3.1Build datacollection instrument
1.6
Preparebusiness case
1.5
Check dataavailability
1.3Establish
outputobjectives
1.2
Consult andconfirm needs
1.1Determineneeds for
information
2.6Design production
systems andworkflow
2.5Design statistical
processing methodology
2.4Design frame and sample
methodology
2.3Design datacollection
methodology
2.2Design variable
descriptions
2.1
Designoutputs
4.4
Finalize collection
4.3
Run collection
4.2
Set up collection
4.1
Select sample
5.1
Integrate data
5.2
Classifyand code
5.3 Review, validate and edit
5.4
Impute
5.5 Derive new
variables andstatistical units
5.6
Calculateweights
5.7
Calculate aggregates
6.1
Prepare draft outputs
6.2
Validate outputs
6.3
Scrutinizeand explain
6.4Apply
disclosure control
6.5
Finalize outputs
7.5Manage
usersupport
7.4Promote
disseminationproducts
7.3 Manage release of dissemination
products
7.2Produce
disseminationproducts
7.1
Update output systems
8 Archive
9 Evaluate
8.2Manage archive
repository
8.1
Define archive rules
8.3Preserve dataand associate
metadata
8.4 Dispose of dataand associated
metadata
9.1Gather
evaluationinputs
9.2
Conduct evaluation
9.3
Agree action plan
Levels 1 and 2Generic Statistical Business Process Model, version 4.0(Joint UNECE/Eurostat/OECD Work Session, April 2009)
1.4
Identify concepts
3.6 Finalize
productionsystem
5.8
Finalizedata files
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
2 Design
3 Build
4 Collect
5 Process
6 Analyse
7 Disseminate
1 Specify
Needs
3.5 Test statistical
business process
3.4Test
productionsystem
3.3
Configureworkflows
3.2 Build or enhance
processcomponents
3.1Build datacollection instrument
1.6
Preparebusiness case
1.5
Check dataavailability
1.3Establish
outputobjectives
1.2
Consult andconfirm needs
1.1Determineneeds for
information
2.6Design production
systems andworkflow
2.5Design statistical
processing methodology
2.4Design frame and sample
methodology
2.3Design datacollection
methodology
2.2Design variable
descriptions
2.1
Designoutputs
4.4
Finalize collection
4.3
Run collection
4.2
Set up collection
4.1
Select sample
5.1
Integrate data
5.2
Classifyand code
5.3 Review, validate and edit
5.4
Impute
5.5 Derive new
variables andstatistical units
5.6
Calculateweights
5.7
Calculate aggregates
6.1
Prepare draft outputs
6.2
Validate outputs
6.3
Scrutinizeand explain
6.4Apply
disclosure control
6.5
Finalize outputs
7.5Manage
usersupport
7.4Promote
disseminationproducts
7.3 Manage release of dissemination
products
7.2Produce
disseminationproducts
7.1
Update output systems
8 Archive
9 Evaluate
8.2Manage archive
repository
8.1
Define archive rules
8.3Preserve dataand associate
metadata
8.4 Dispose of dataand associated
metadata
9.1Gather
evaluationinputs
9.2
Conduct evaluation
9.3
Agree action plan
1.4
Identify concepts
3.6 Finalize
productionsystem
5.8
Finalizedata files
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
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
Year Texts Correction
Rate
Location of error and relative magnitude of correction
Design Build Collect Process Disseminate
Higher Lower Higher Lower Higher Lower Higher Lower Higher Lower
2006 623 2.1% 1 1 4 1 3
2007 1234 3.1% 3 6 14 2 10
2008 1260 2.2% 1 1 2 2 10 1 11
2009 1171 0.8% 2 1 2 1
2010 315 0.3% 1
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
2 Design
3 Build
4 Collect
5 Process
6 Analyse
7 Disseminate
1 Specify
Needs
3.5 Test statistical
business process
3.4Test
productionsystem
3.3
Configureworkflows
3.2 Build or enhance
processcomponents
3.1Build datacollection instrument
1.6
Preparebusiness case
1.5
Check dataavailability
1.3Establish
outputobjectives
1.2
Consult andconfirm needs
1.1Determineneeds for
information
2.6Design production
systems andworkflow
2.5Design statistical
processing methodology
2.4Design frame and sample
methodology
2.3Design datacollection
methodology
2.2Design variable
descriptions
2.1
Designoutputs
4.4
Finalize collection
4.3
Run collection
4.2
Set up collection
4.1
Select sample
5.1
Integrate data
5.2
Classifyand code
5.3 Review, validate and edit
5.4
Impute
5.5 Derive new
variables andstatistical units
5.6
Calculateweights
5.7
Calculate aggregates
6.1
Prepare draft outputs
6.2
Validate outputs
6.3
Scrutinizeand explain
6.4Apply
disclosure control
6.5
Finalize outputs
7.5Manage
usersupport
7.4Promote
disseminationproducts
7.3 Manage release of dissemination
products
7.2Produce
disseminationproducts
7.1
Update output systems
8 Archive
9 Evaluate
8.2Manage archive
repository
8.1
Define archive rules
8.3Preserve dataand associate
metadata
8.4 Dispose of dataand associated
metadata
9.1Gather
evaluationinputs
9.2
Conduct evaluation
9.3
Agree action plan
Levels 1 and 2Generic Statistical Business Process Model, version 4.0(Joint UNECE/Eurostat/OECD Work Session, April 2009)
1.4
Identify concepts
3.6 Finalize
productionsystem
5.8
Finalizedata files
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
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
Contact information
For more information, please contact:
Pour plus d’information, veuillez contacter :