1. The Generic Statistical Business Process Model Steven Vale, UNECE
METIS Workshop, Lisbon, March 2009

2. Aim of Session 1 To finalize the model Contents
Presentation of the model
Wider context
National implementations
Detailed discussions
Summary and conclusions

3. Background
Defining and mapping business processes in statistical organisations started at least 10 years ago
“Statistical value chain”
“Survey life-cycle”
“Statistical process cycle”
“Business process model”

4. Generic Statistical Business Process Model
Background
Defining and mapping business processes in statistical organisations started at least 10 years ago
“Statistical value chain” X
“Survey life-cycle” X
“Statistical process cycle” X
“Business process model” X
Generic Statistical Business Process Model

5. Why do we need a model?
To define, describe and map statistical processes in a coherent way
To standardize process terminology
To compare / benchmark processes within and between organizations
To identify synergies between processes
To inform decisions on systems architectures and organization of resources 5

6. History of the Current Model
Based on the business process model developed by Statistics New Zealand
Added phases for:
Archive (inspired by Statistics Canada)
Evaluate (Australia and others)
Three rounds of comments
Terminology and descriptions made more generic
Wider applicability?

7. Applicability (1)
All activities undertaken by producers of official statistics which result in data outputs
National and international statistical organizations
Independent of data source, can be used for:
Surveys / censuses
Administrative sources / register-based statistics
Mixed sources

8. Applicability (2)
Producing statistics from raw data (micro or macro-data)
Revision of existing data / re-calculation of time-series
Development and maintenance of statistical registers

9. Structure of the Model (1)
Process
Phases
Sub-processes
(Descriptions)

10. Structure of the Model (2)
National implementations may need additional levels
Over-arching processes
Quality management
Metadata management
Statistical framework management
Statistical programme management
(8 more – see paper)

12. Not a linear model Key features (1)
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

14. Key Features (2) In theory the model is circular:
Evaluation can lead to modified needs and design
In practice it is more like a multiple helix:
There may be several iterations of a process underway at any point in time

15. Mapping to Other Models

16. Remainder of This Session
Wider uses of the model – IT architecture and statistical software sharing
National applications of the model
Norway
New Zealand
Your input – parallel sessions

17. Parallel Sessions
Aim – to review in detail three phases of the model in each parallel session:
Session 1a: Phases 1-3 (Specify needs, Design, Build) - Facilitator: Alice Born
Session 1b: Phases 4-6 (Collect, Process, Analyse) - Facilitator: Jenny Linnerud
Session 1c: Phases 7-9 (Disseminate, Archive, Evaluate) - Facilitator: Jessica Gardner
Start 14.00, End 16.45, short plenary session