international collaboration on industrialization of editing: business case (part 1, wp38)

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1 1 International Collaboration International Collaboration on Industrialization of on Industrialization of Editing: Editing: Business Case (Part 1, Business Case (Part 1, WP38) WP38) Li-Chun Zhang Statistics Norway

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International Collaboration on Industrialization of Editing: Business Case (Part 1, WP38). Li-Chun Zhang Statistics Norway. Industrialization of Editing: Some issues to be dealt with. Overall objective, principles and guidelines (e.g. the “new” paradigm of editing) - PowerPoint PPT Presentation

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International Collaboration on International Collaboration on Industrialization of Editing:Industrialization of Editing:Business Case (Part 1, WP38) Business Case (Part 1, WP38)

Li-Chun Zhang

Statistics Norway

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Industrialization of Editing: Some issues to be dealt with

• Overall objective, principles and guidelines (e.g. the “new” paradigm of editing)

• Conceptual reference framework with regard to GSBPM

• Conceptual reference framework with regard to GSIM to-be

• Design of generic functionality

• Minimum set of standard methods

• IT tools and platforms

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Objectives & principles

• Example: Objectives (the “new” paradigm)– Error-source identification and error prevention– Collect information about quality– Identification and adjustment of critical errors in data

• Example: Objectives (SNZ proposal)– Efficiency as quality against cost– Continuous quality improvement– Provide quality information

• Example: Principles– Original data as much as possible (“old” Felligi-Holt paradigm)– Maximum automated processing– Analysis of (editing) process efficiency– Training, documentation– …

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Generic Statistical Data Editing Process (GSDEP)

• GSBPM ≠ Flow Chart

• An example from EDIMBUS

• Mapping GSDEP with GSBPM– Micro vs. macro editing– “Editing & Imputation” (E&I) vs.

“Editing & Estimation” (E&E)

• Connections to GSIM to-be

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Common Statistical Data Reference (CSDR):Interface btw. SDE and GSIM to-be

• Statistical production as transformations of data

=> steady / major states of data

• Common Micro Data Format for database management

• Common Functional Data Format for method library

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Design of generic functionality• Databases

– Micro database of CMDF data files (M-Base)– Functional database of functional data files and alignment tables (F-Base)– Function library (F-Lib) contains all available standardized generic (program) tools.

• Builders– Functional data builder (D-Build) transforms relevant CMDF data files into the required functional data files, and

updates the relevant alignment tables.– Function builder (F-Build) takes functional data files as the input data and tools from the F-Lib, and configures the

necessary parameters according to a given specification for machine-based or automated data processing.– Screen builder (S-Build) takes fnctional and/or CMDF data files as the input data, and configures an environment

for manual inspection/editing of individual records/questionnaires according to a given specification.

• Runners: – Batch processor is the environment for executing automated/machined-based SDE processes, chiefly relying on

functions that are configured in the F-Build.– Manual processor is the environment for manually executing SDE processes, chiefly relying on the interface

provided through the S-Build.– Selection and Drilling are the dedicated environments for carrying out selective editing and drilling up-and-down

among hierarchically structured aggregations.– Data processor supports the necessary administration of data and metadata.

• Managers: – ANOPE is the environment for quality assessment of the editing processes.– Response manager provides the interface for re-contact with the data providers, and other generally related

production processes (such as Process 4 Collect).

Claude PoirierStatistics Canada

Next steps

• Objectives, guidelines and principles– Finalize user requirements– Identify existing methods– React to functional gaps– Set up the framework– Develop the toolset– Deliver training

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Finalizing user requirements

• Prioritizing edit and imputation requirements

– Micro-editing methodsAutomated E&I on numerical and categorical data

– Macro-editing methodsSelective editing; Macro editing; Editing of macro data

– On-line editingCollection edits and self-administered edits

– Data confrontation and certificationMethods using multiple data sources

– Standardized platformCommon architecture

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Existing tools and Platforms

• Identifying and analysing existing products

– SigEE (Australia)

– BANFF, CANCEIS (Canada)

– BEST, POSS (New Zealand)

– ISEE, DYNAREV (Norway)

– TRITON, SELEKT (Sweden)

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Reacting to functional gaps

• Not all requirements will be satisfied

• Brainstorming sessions are being organised

• Development priorities will be discussed

Developing the tool set

• Consolidate preferred tools

– Adapt existing tools to the environment

– Develop pre/post processors to fit the environment

• Develop missing functions10

Delivering training material

• User guide

• Methodology documentation

• System documentation

Comments / Questions

• It’s your turn

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Frequently asked questions (FAQ)

Q1: What governance model drives the project?

Q2: When do we expect the suite of editing functions to be delivered?

Q3: As a member of the collaboration network, will my agency have to pay any fees for accessing and using released functions?

Q4: My statistical agency is not part of the network. Are there any fees that are planned to let me use the products?

Q5: My agency would like join the network. Is this possible? How?

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Frequently asked questions (FAQ)

Q6: I understand from your presentation that a common environment is being planned? Would I be able to use the functions in another environment?

Q7: My agency is willing to share a system but its foundation software is not compliant with the proposed environment. What will happen?

Q8: My agency is willing to offer a system or a module for the network. Who will own the module?

Q9: Will the resulting products become open-source?

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