Transcript Quality Framework - IZA - Institute for the Study of Labor

United Nations Economic Commission for Europe
Statistical Division
Exploring the relationship between
DDI, SDMX and the Generic
Statistical Business Process Model
Steven Vale, UNECE
Contents
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Introducing the GSBPM
Links to DDI and SDMX
Combining Standards to Industrialize
Official Statistics
Conclusions
Steven Vale - UNECE Statistical Division
Slide 2
GSBPM – The Background
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Statistical production has traditionally been
organised by topic, e.g. transport, trade, …
Financial pressures are encouraging new
ways of thinking
Some statistical organisations are moving
towards a process-based approach
Others are considering a matrix approach
Steven Vale - UNECE Statistical Division
Slide 3
Topics
P
r
o
c
e
s
s
e
s
Steven Vale - UNECE Statistical Division
Slide 4
Which processes?

Defining and modelling processes in
statistical organisations started at least
10 years ago
“Statistical value chain”
• “Survey life-cycle”
• “Statistical process cycle”
• “Business process model”
•
Steven Vale - UNECE Statistical Division
Slide 5
Which processes?

Defining and modelling processes in
statistical organisations started at least
10 years ago
“Statistical value chain”
• “Survey life-cycle”
• “Statistical process cycle”
• “Business process model”
•
X
X
X
X
Generic Statistical Business
Process Model
Steven Vale - UNECE Statistical Division
Slide 6
Why do we need a model?
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To define and describe statistical processes
in a coherent way
To standardize process terminology
To compare / benchmark processes within
and between organisations
To identify synergies between processes
To inform decisions on systems
architectures and organisation of resources
Steven Vale - UNECE Statistical Division
Slide 7
Applicability
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All activities undertaken by producers of
official statistics which result in data outputs
National and international statistical
organisations
Independent of data source, can be used for:
•
Surveys / censuses
• Administrative sources / register-based statistics
• Mixed sources
Steven Vale - UNECE Statistical Division
Slide 8
Structure of the GSBPM
Process
Phases
Subprocesses
(Descriptions)
Steven Vale - UNECE Statistical Division
Slide 9
Key features
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Not a linear model
Sub-processes do not have to be followed
in a strict order
It is a matrix, through which there are many
possible paths, including iterative loops
within and between phases
Some iterations of a regular process may
skip certain sub-processes
Steven Vale - UNECE Statistical Division
Slide 10
Links to DDI and SDMX
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SDMX standards refer to business
processes, but do not have a model
DDI has the Combined Life Cycle Model
Steven Vale - UNECE Statistical Division
Slide 12
Generic Statistical Business
Process Model
DDI 3.0 Combined Life Cycle Model
1 Specify Needs
Study Concept
Repurposing (part)
2 Design
3 Build
4 Collect
Data Collection
5 Process
Data Processing (mostly)
Repurposing (part)
6 Analyse
Data Discovery
Data Analysis
Data Processing (part)
7 Disseminate
Data Distribution
8 Archive
Data Archiving
9 Evaluate
Quality Management
Metadata Management
Steven Vale - UNECE Statistical Division
Slide 13
Combining standards
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“Industrialization” of statistical production
GSBPM provides the framework, but
more is needed:
•
Information model
• Data / metadata formats
• Modular architecture
• Shared development of tools and methods
Steven Vale - UNECE Statistical Division
Slide 14
Data / metadata formats
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SDMX?
•
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DDI?
•
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Suitable for micro-data??
Suitable for exchange of statistical
aggregates?
Combined approach?
•
Trial by Australian Bureau of Statistics
Steven Vale - UNECE Statistical Division
Slide 15
Combining standards?
Steven Vale - UNECE Statistical Division
Slide 16
Conclusions

Too early to say for definite, but:
•
No single standard meets all requirements
• Combine standards?

GSBPM
 DDI
 SDMX
 Others?

More work needed!
Steven Vale - UNECE Statistical Division
Slide 17
Questions and Comments?
[email protected]
METIS Workshop on GSBPM and related
standards, 5-7 October 2011, Geneva
DDI community welcome!
Steven Vale - UNECE Statistical Division
Slide 18