To promote international collaboration, ModernStats, an initiative coordinated by the UNECE, offers the following
article, which is part of a series on joint efforts already underway.
GSPBM: a better way than stovepipes
Not so long ago, it was difficult for statistical organisations to share information on outputs. Data
production was often based on a ‘stovepipe’ model.
Staff who worked on a particular survey became experts in that survey and its production tools and
processes. While this allowed for the development of highly specialized expertise, this expertise could
not be shared readily – either among different statistical organizations or even among staff in the same
organization.
In a sense, the stovepipes became Towers of Babel. No one spoke the same language.
The Generic Statistical Business Process Model started to change all that. The GSBPM, one of an interrelated family of statistical models, made it easier to share expertise.
‘One of the biggest strengths of GSBPM is that it provides a standard terminology to describe the
different steps involved in statistical production,’ says Stephen Vale, Head of the UNECE’s Statistical
Management and Modernisation Unit.
‘Having a standard terminology makes it much easier to communicate between and among statistical
organisations,’ says Mr. Vale.
GSBPM is designed to show the different steps organisations go through to produce official statistics.
‘If we have the same description or name for part of a statistical production process in economic and
social statistics, we start to see similarities in processes, and where there may perhaps be scope for
rationalising the number of tools or methods.’
GSBPM started about 15 years ago. Originally created by a group on statistical metadata, the name of
project changed several times along the way.
In the early years, the main output was a common metadata framework describing statistical production
processes.
Early work on different aspects of the project was inspired by best practices of statistical organizations
in a number of countries, including New Zealand, Slovenia, the United Kingdom, Canada and Australia.
The first model of what is now known as GSBPM was launched in 2009, and refreshed in 2013 following
feedback from many statistical organizations. Enhancements included improved documentation and a
less survey-focused approach.
GSBPM has improved both communication and decision-making, Mr. Vale says. ‘We can now define
and describe statistical processes in more coherent way, among and within statistical organizations. We
can make much better decisions on how we develop production systems and organise resources.’
GSBPM is part of a wider trend, a move to a process-oriented approach rather than one focused on a
particular subject-matter topic, such as transport or trade.
Mr. Vale says financial pressures are encouraging new ways of thinking. Statistical organisations are
moving away from the stovepipe model with specialised tools for statistical production. Instead, they
are adopting a process-oriented approach favouring standardisation in all dissemination activities.
Think horizontal sharing instead of vertical specialization.
GSBPM is applicable to all activities undertaken by statistical organisations which lead to a statistical
output. It accommodates data sources such as administrative data, register-based statistics and also
Census and mixed sources.
Mr. Vale stresses that GSBPM is not a linear model. Rather, it is a matrix through which there are many
possible paths.
Do statistical organisations find it useful? Perhaps, when it comes to comes to statistical models,
implementation is the sincerest form of flattery.
If that is the case, GSBPM has earned praise in over 100 countries. Recent users include statistical
organisations in Botswana, Russia, the U.N. and Turkey. In Estonia, it is used to cost resource allocation
in statistical projection. Armenia has documented its census of population using GSBPM as well.
GAMSO: breaking new ground, on a parallel path
GSBPM provides a framework for all aspects of statistical production. However, statistical organisations
carry out many activities that are not directly related to producing data.
In response to a need for a tool to enhance communication and decision-making in areas such as human
resources and financial management, among others, GAMSO was created.
The Generic Activity Model for Statistical organisations was launched in 2015 in New York. It extends
and complements GSBPM by adding other activities which are needed to support statistical production.
GAMSO contains four broad areas: production; strategy and leadership; capability management and
corporate support.
For production, GAMSO is exactly equal to and consistent with GSBPM.
The strategy and leadership component provides organisations with a structure for defining vision,
governing and leading, and managing strategic collaboration and cooperation, including international
cooperation.
The facet on capability management defines capability as an ability that an organisation, person or
system possesses. Typically, it requires an organisation to have a combination of people, processes and
technology. Seasonal adjustment would be one example of a capability.
GAMSO can be used for resource planning. In Estonia, it is being used in Human Resources. It can also
be deployed to assess the readiness of an organisation to implement modernisation, support risk
management or implement enterprise architecture.
Statistical organisations are beginning to use GAMSO. Feedback will be collected and the project will be
reviewed in 2016.