Recent developments in the Generic Statistical Business Process

Recent developments in the Generic Statistical Business Process Model: Revisions and Quality Indicators Alice Born, Statistics Canada, alice. born@canada. ca Nilgün Dorsan, Turk. Stat, nilgun. dorsan@tuik. gov. tr Therese Lalor, UNECE, Therese. Lalor@unece. org Deniz Özkan, Turk. Stat, deniz. ozkan@tuik. gov. tr Marina Signore, Istat, signore@istat. it June 27, 2018 Session 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.

Why Revise? Users needs CES Other Models GAMSO • 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. • This model was endorsed by the CES in 2017 on the understanding that it should be updated every 5 years. • GSBPM v. 1 -2008, v. 4 -2009 and v. 5 -2013 https: //statswiki. unece. org/display/GSBPM+v 5. 0 • Revisions only if really needed • The recent developments of GAMSO requires some updating to GSBPM in order to ensure alignment between the two reference models.

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).

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

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)

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).

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 https: //statswiki. unece. org/display/GSBPM/Quality+Indicators+Home

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

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 Admin. Data) • 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.

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 ü Avoid duplication of work in different sectors within the organisation ü Map/fill in gaps with QIs in use in an NSO • To define a mid-term quality policy ü Can be tailored by NSIs according to their needs ü Set quality targets to be achieved in a 3 -5 year period ü Inspiration from indicators defined for the overarching processes

Next steps • Communicating and promoting the revised version of GSBPM. • Providing further clarity on how the modernisation models and frameworks fit together. ü Developing a mapping of inputs and outputs of each GSBPM subprocess (using GSIM objects). ü 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.

Recent developments in the Generic Statistical Business Process Model: Revisions and Quality Indicators Thank you for your attention! Nilgün Dorsan, Turk. Stat, nilgun. dorsan@tuik. gov. tr Marina Signore, Istat, signore@istat. it