1. Recent developments in the Generic Statistical Business Process Model: Revisions and Quality Indicators
Alice Born, Statistics Canada,
Nilgün Dorsan, TurkStat,
Therese Lalor, UNECE,
Deniz Özkan, TurkStat,
Marina Signore, Istat,
June 27, 2018
Session 2

2. Generic Statistical Business Process Model
The model describes and defines the set of business processes needed to produce official statistics.
Widely used by the statistical community for a range of different purposes from process documentation and monitoring to training staff.
Currently maintained and updated by the UNECE Supporting Standards Modernisation Group.

3. Why Revise?
Users needs
Recent experiences from users in implementing GSBPM in their organisations and a number of developments in business landscape where statistical organisations operate are requiring changes in GSBPM.
CES
This model was endorsed by the CES in 2017 on the understanding that it should be updated every 5 years.
GSBPM v , v and v
Revisions only if really needed
Other Models - GAMSO
The recent developments of GAMSO requires some updating to GSBPM in order to ensure alignment between the two reference models.

4. GSBPM Revision Task Team
Purpose
The purpose of this project is to revise GSBPM to ensure the model remains relevant and continue serving as the reference framework for statistical organisations.
GSBPM Revision Task Team
The GSBPM revision working group consists of 18 team members, who are from different national and international statistical organisations (such as Eurostat, ILO and UNECE).

5. Revision Process The revision project focussed on three major tasks;
Compilation of user feedback (July October 2017)
Review of feedback and revision process (October June 2018)
Public consultation and revision (July November 2018)
The GSBPM revision working group meets every three weeks through webex.

6. Basic Principles of Revision Process
To the largest extent possible, keep the existing structure unchanged
Revision needs to encompass other exchange channels and data sources, such as administrative data, big data and geospatial data
Ensuring consistency with other standards (GSIM, GAMSO, CSPA)

7. Issues and Challenges
Achieving clarity about the model without reducing the simplicity of the model
Determining the boundaries of the subprocesses more precisely
Allowing for the business processes for all data sources to be described without losing generality
The need of reviewing and expanding the definition of the overarching processes for "Quality and Metadata Management”
Difficulty to reflect some users’ specific needs in the generic model
Make sure to keep in GSBPM what has been a success (i.e., a general framework of the data lifecycle for official statistics).

8. Quality Indicators for the GSBPM
The development of QIs for the GSBPM was one of the priorities of the UNECE Supporting Standards Modernisation Group
Version 1.0 was released in 2016 for Survey-derived statistics
A task team composed by Statistics Canada, Italy, New Zealand Turkey and Statistics Finland updated the document
Version 2.0, released October 2017, mapped quality indicators for Statistics derived from Surveys and Administrative Data Sources to the GSBPM phases and sub-processes

9. Quality Indicators for the GSBPM
Quality and Metadata management overarching processes
Quality indicators were developed for each phase (1 to 8) and sub-processes

10. Types and Levels of the Indicators
Generic indicators were proposed in order to reflect the nature of the GSBMP itself.
Consistency with existing frameworks was ensured (e.g.UN, ESS Q&P indicators, ESSnet AdminData)
No formulas were indicated but explanations and reference to the related quality dimension were provided.
Quantitative indicators were used whenever possible.
Qualitative indicators were expressed in the form of yes/no or high/medium/low degree indicators.

11. Uses of the Quality Indicators
QIs for the GSBPM provide a standard framework and a common terminology and support a process-oriented approach to Quality Management
To rationalise quality work within an organisation
Avoid duplication of work in different sectors within the organisation
Map/fill in gaps with QIs in use in an NSO
To define a mid-term quality policy
Can be tailored by NSIs according to their needs
Set quality targets to be achieved in a 3-5 year period
Inspiration from indicators defined for the overarching processes

12. Next steps Communicating and promoting the revised version of GSBPM.
Providing further clarity on how the modernisation models and frameworks fit together.
Developing a mapping of inputs and outputs of each GSBPM sub- process (using GSIM objects).
Providing more detailed explanations on the overarching processes in GSBPM and in GAMSO
Continuing to monitor developments in official statistics and discuss how these should be reflected in the modernisation models to keep them relevant for the statistical community.

13. Thank you for your attention!
Recent developments in the Generic Statistical Business Process Model: Revisions and Quality Indicators
Thank you for your attention!
Nilgün Dorsan, TurkStat,
Marina Signore, Istat,