Generic Statistical Information Model
(GSIM)
Jenny Linnerud
jal@ssb.no
This webinar on GSIM (Generic
Statistical Information Model) is part
of a series of lectures on the main
projects undertaken by the High
Level Group for the Modernization
of Official Statistics (HLG-MOS)
Vision of the High Level Group
What is GSIM?
• It is a strategic approach and a new way of thinking,
designed to bring together statisticians,
methodologists and IT specialists to modernize and
streamline the production of official statistics.
• It is a reference framework of internationally agreed
definitions, attributes and relationships that describe
the pieces of information used in the production of
official statistics (information objects).
• This framework enables generic descriptions of the
definition, management and use of data and metadata
throughout the statistical production process.
What is the relationship betweeen
GSIM & GSBPM?
• GSIM and GSBPM are complementary models for the production and
management of statistical information.
• GSBPM models the statistical production process and identifies the
activities undertaken by producers of official statistics that result in
information outputs.
• GSIM helps describe GSBPM sub-processes by defining the information
objects that are used by them, that flow between them, and are created in
them in order to produce official statistics.
What is an information object?
• GSIM is a model of objects that specify information
about the real world (“information objects”).
• Examples include data and metadata (such as
classifications), as well as rules and parameters needed
for production processes to run (e.g. data editing
rules).
• GSIM identifies ca. 110 information objects, which are
grouped into four broad categories
Statistical
Support
Program
Statistical
Program
Business
Process
Data Set
Information
Resource
Referential
Metadata Set
Exchange
Process Step
Web Scraper
Channel
Data
Structure
Variable
Structures
Referential
Metadata
Structure
Questionnaire
Exchange
Channel
Product
Concepts
Business
Statistical
Need
Administrative
Register
Population
Concept
Unit
Statistical
Classification
GSIM Development 2012
• GSIM sprint in Slovenia, February
• GSIM sprint in Republic of Korea, March
• Integration workshop in the Netherlands,
November
GSIM v1.0 December
Developing the GSIM
17 different organisations
What are the benefits of using GSIM?
• GSIM enables statistical organizations to rethink how their
business could be more efficiently organized
– by defining information objects common to all statistical
production, regardless of the subject matter area,
• Improves communication between different disciplines
involved in statistical production
– within and between statistical organizations;
– between users and producers of official statistics.
• Generates economies of scale
– reuse of information can improve comparability of statistics
• Enables greater automation of the statistical production
process
• Validates existing information systems
In Statistics Norway we are also using GSIM to communicate
with other government agencies and with IT consultants.
Statistics Norway’s participation in
GSIM Implementation
• GSIM v1.0 Brochure and Communication
document in Norwegian
• Informal task force on metadata flows in the
GSBPM - ca. 20 GSIM information objects were
mapped to the phases in GSBPM v4
• GSIM v1.0 discussion forum
• GSIM Statistical Classification Model -> GSIM v1.1
December 2013
• Trying out GSIM v1.1 within the RAIRD project
GSIM implementation 2013-2015
8 countries provided GSIM Case studies in 2015
- Canada, Finland, France, Germany, Italy,
New Zealand, Norway, Sweden
http://www1.unece.org/stat/platform/display/CASES/GSBPM+and+GSIM+Case+Studies
• GSIM Statistical Classifications is the part of the
model that statistical organisations have
implemented most
GSIM in Statistics Norway - Vision
GSIM should lead to:
• A foundation for standardised statistical metadata
use throughout systems
• A standardised framework for consistent and
coherent design of statistical production
• Increased sharing of system components
Remote Access Infrastructure for
Register Data (RAIRD)
• Statistics Norway and the Norwegian Social Science
Data Services (NSD) aim to establish a national
research infrastructure providing easy access to large
amounts of rich high-quality statistical data for
scientific research, while at the same time managing
statistical confidentiality and protecting the integrity of
the data subjects.
• The work is organized as a project, RAIRD – Remote
Access Infrastructure for Register Data, and funded by
the Research Council of Norway. See: www.raird.no
RAIRD Information Model (RIM)
• RIM is an implementation of the Generic Statistical
Information Model (GSIM) v1.1.
• We have based RIM on the GSIM Design Principles
• RIM extends GSIM with 27 Information objects
that are mainly specialisations e.g. to include
different types of agents (producers,
administrators and researchers)
• RAIRD is a project that is still in progress with
completion planned in 2017.
Potential Benefits of RAIRD
•
•
•
•
Simplify the approval process
Provide quicker access to analysis results
More Masters students will use our data
Simplify large, complicated studies by
providing exploratory analysis in an early
phase
• More research and use of our data
Contents in 2017
•
•
•
•
•
Demography
Education
Income
Labour market
Social security and benefits
Better transfer of knowledge within Statistics
Norway
Overview of
the main
components
Event
History
Input
Data Set
Event
History
Data
Store
Provisional
Output
Analysis
Data Set
Input
Metadata
Set
Data
Catalogue
Disclosure
Control
System
Load API
SSB Data
Mgt.
System
Virtual
Statistical
Machine
Final
Output
Browser
Browser
Browser
Browse Data Catalogue
User Operations
User Views
VIRTUAL RESEARCH ENVIRONMENT
Metadata
Researcher cannot see the data -> Simplifies the
approval process
Metadata is the interface to the data
Metadata
Analyse data
Statistical Confidentiality in RAIRD
Joint UNECE/Eurostat Work Session on Statistical
Data Confidentiality on 5-7 October 2015 at
Statistics Finland
- Topic (v): Practicum: Case Studies and
Software
How do I find out more?
UNECE - GSIM Wiki
http://www1.unece.org/stat/platform/display/gsim/Generic+Statistical+Information+Model
?
Questions
Thank-you to Peter Frayne for contributing
questions in advance 
The End