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Attachment 2: The Generic Statistical Business Process Model (GSBPM) The Generic Statistical Business Process Model provides a consistent and comprehensive framework for describing and analysing statistical processes and identifying where improvements are needed. As such, it will provide the basis for the detailed impleme ntation plan for the framework. At the first and second levels it is made up of nine phases, each of which involves a number of sub-processes. These are as follows. The information is based on the UNECE publication Generic Statistical Business Process Model Version 4.0 published in April 2009 1
. Phase 1: Specify needs
This phase is triggered when a need for new statistics is identified, or feedback about current statistics initiates a review. It determines whether there is unmet demand, externally and / or internally, for the identified statistics and whether the statistical organization can produce them. It involves the following steps: • Determining the need for the statistics, based on the needs of users; • Establishing the high level objectives of the statistical outputs; • Identifying the relevant concepts and variables for which data are required; • Checking if current collections or other processes can meet these needs; • Making the case for the resources that will be needed.
1 See: http://www1.unece.org/stat/platform/display/metis/The+Generic+Statistical+Business+Process+Model
Specify Needs
1.1
Determine
needs for
information
1.2
Consult
and
confirm
needs
1.4
Identify
concepts
1.5
Check data
availability
1.6
Prepare
business
case
1.3
Establish
output
objectives
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Collect 4.1 Select sample
4.2 Set up collection 4.3 Run collection 4.4 Finalize collection Phase 2: Design
This pha se describes the development and design activities, and any associated practical research work needed to define the statistical outputs, concepts, methodologies, collection instruments and operational processes. For statistical outputs produced on a regular basis, this phase usually occurs when the activity is first developed and whenever improvements are identified.
Phase 3: Build
This phase builds and tests the production systems to the point where they are ready for use. For statistical outputs produced on a regular basis, this phase usually occurs for when the activity is first developed and when there is any major change in methodology.
Phase 4: Collection
Build 3.1 Build data collection instrument
3.2
Build or
enhance
process
components
3.3
Configure
workflows
3.4
Test
production
system
3.6
Finalize
production
system
3.5
Test
statistical
business
process
Design
2.1
Design
outputs
2.4
Design frame
and sample
methodology
2.3
Design data
collection
methodology
2.5
Design
statistical
processing
methodology
2.6
Design
production
systems and
workflow
2.2
Design
variable
descriptions
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This phase collects all necessary data, using different collecti on methods (including making use of data derived from administrative and statistical registers and databases), and loads them into the appropriate data processing environment. It does not include any transformations of collected data, as these are included in phase 5 (Process).
Phase 5: Process
This phase describes the cleaning of the raw data and their preparation for analysis. It is made up of sub- processes that check, clean, and transform the data and may well be repeated several times. In practice, Phases 4 and 5 may proceed in parallel and may involve an iterative process.
Phase 6: Analyse
In this phase, statistics are produced, examined in detail and made ready for dissemination. It includes the sub-processes and activities that enable analysts to understand and interpret the statistics produced.
Analyse 6.1 Prepare draft outputs 6.2 Validate outputs 6.3 Scrutinize and explain 6.5 Finalize outputs 6.4 Apply disclosure control Process 5.2 Classify and code
5.5 Derive new variables and statistical units
5.6 Calculate weights
5.1 Integrate data 5.7 Calculate aggregates 5.4 Impute 5.3 Review, validate and edit 5.8 Finalize data files
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Phase 7: Disseminate
This phase manages the release of the statistical products to user and customers.
Phase 8: Archive
This phase manages the archiving and disposal of statistical data and metadata. Given the reduced costs of data storage, it is possible that the archiving strategy adopted by a statistical organization does not include provision for disposal, so the final sub- process may not be relevant for all processes. In other cases, disposal may be limited to intermediate files from previous iterations, rather than disseminated data.
Phase 9: Evaluate
This phase manages the evaluation of specific statistical processes, as opposed to the more general process of statistical quality management. It logically takes place at the end of the Evaluate 9.1 Gather evaluation inputs
9.3 Agree action plan 9.2 Conduct evaluation
Archive 8.1 Define archive rules
8.3 Preserve data and associated metadata 8.4 Dispose of data and associated metadata 8.2 Manage archive repository
Disseminate 7.1 Update output systems
7.2 Produce dissemination products 7.3 Manage release of dissemination products 7.5 Manage user support 7.4 Promote dissemination products 53
process, but relies on inputs gathered throughout the different phases . For well -established and regular statistical processes evaluation may not be carried out every time the statistics are generated, but will need to take place from time to time. For other processes evaluation is very important, providing the information needed for planning next time.
National Bureau of Statistics
VISION “To become a one-stop centre for official statistics in Tanzania”
MISSION “To produce quality official statistics and services that meet needs of national and international stakeholders for evidence-based planning and decision making”