The Integrated Business
Statistics Program at
Statistics Canada
Marie Brodeur
SNA seminar in the Caribbean
St. Lucia: February 2014
Statistics Canada • Statistique Canada
Corporate Business Architecture
 Three objectives:
• Efficiency: A harvestable efficiency on ongoing
operating costs of 5% within 5 years;
• Robustness: A reduced, unduplicated set of robust
systems and processes that are properly maintained
and documented; and
• Responsiveness: Improved responsiveness in
delivery of new statistical programs
Statistics Canada • Statistique Canada
Key Architectural Principles
 Corporately optimized decision-making:
• Governance to define and apply
 Centralization of informatics, statistical
processing, methodology support and frame
infrastructure is the corporate default
• IT resources under one management
• Centralized management of software and hardware
infrastructure
Statistics Canada • Statistique Canada
Key Architectural Principles 2
 Mandatory use of generic corporate services
(collection, operational support, dissemination, etc.)
 Creation of new generic corporate services where
appropriate
 Maximize re-use:
• Smallest possible number of business processes
• Smallest possible number of enabling computer
systems
Statistics Canada • Statistique Canada
Key Architectural Principles 3
 Maximize deployment of electronic data reporting
solutions
 Minimize tool kits
 Eliminate re-work
• Meta-data driven
 Manage statistical information
• Common Information Management policy
framework
• Data service centres
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IBSP Project Background
 Why IBSP?
•
•
•
•
•
Aging infrastructure
Lack of Flexibility
StatCan faced financial pressures
Respondent burden
Higher than historical turnover in personnel
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Project Background 2
• Re-think the way in which we produce business
statistics
• Adopt an approach that ensures coherence from start
to finish and across the different programs that
produce business statistics
• The IBSP is an important transformational project:
 Examined from a conceptual point how we want to
function
 Operationalize the conceptual approach
 Develop infrastructure
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Objective of Project
 Develop generic model for producing business
statistics
• Improve quality, in particular the coherence aspect,
across the different programs
• Robust infrastructure
• Less expensive to maintain
• Flexible to respond to client needs
• Reduce respondent burden
 Return efficiencies to the corporation
Statistics Canada • Statistique Canada
IBSP Pillars
 Based on the Generic Statistical Business Process
Model (GSBPM)
 Use of a common frame (BR)
 Use of tax data universe for the estimation of financial
information
 Use Electronic Data Reporting as the principal mode of
collection
 Use of common processing methodology and metadata
driven process
 Establish an earlier cut-off to improve timeliness
 Establish a Data Service Center for warehousing
statistical information
 Increase governance across all areas involved in
statistical data output
Statistics Canada • Statistique Canada
The Generic Statistical Business Process Model

To successfully achieve integration across
many programs and processes requires:




Continuous support from Senior leaders
Very strong governance over life of project
Extensive collaboration across the organization
Ability to negotiate and adapt: generic solutions have
limitations
Statistics Canada • Statistique Canada
Scope of Project
 Suite of approximately 150 existing business
surveys covering manufacturing, services, retail,
agriculture, capital expenditure, energy and
R&D ; ad-hoc surveys as well
 Covers all survey activities from frame to
dissemination
• Financial and activity based
• Establishment and enterprise
Statistics Canada • Statistique Canada
System of
National
Accounts
Subject Matter
Areas
IBSP
Methodology
CANSIM
Enterprise Architecture Integration Platform (EAIP)
Tax
Business Register
GenSys
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Collection
Horizontal Integration
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Vertical Integration
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How to Achieve Integration - tools

Generic processors for:





Sampling
Edit and imputation
Estimation
Moving towards one collection platform
Common analytical tools


Used by subject matter analysts and the System of
National Accounts analysts;
Simplifies staff mobility
Statistics Canada • Statistique Canada
How to Achieve Integration Content – Concepts and Output



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Harmonized common content across business
surveys – it is now an enforced standard
Harmonized questions to extent possible
One mapping between tax information and
survey concepts with the Chart of Accounts
Reduced content by 20%
Statistics Canada • Statistique Canada
Coherence Across Different
Programs

Why coherence?



Output from different programs integrated into the
System of National Accounts
Output from different programs compared by users
Eliminates potential overlap in coverage and helps
identify coverage gaps
Statistics Canada • Statistique Canada
Project Management

To successfully achieve integration across
many programs and processes requires:




Continuous support from Senior leaders
Very strong governance over life of project
Extensive collaboration across the organization
Ability to negotiate and adapt: generic solutions have
limitations
Statistics Canada • Statistique Canada
Methodology
 Use of the Business Register
• Establishment and entreprise based
 Two-phase sampling for commodity and activity
based surveys
 For the rest Stratified Ramdom sample
• Allocation industry by geography
• Take-all strata for complex enterprises
• Take-some strata for simple establishments
Statistics Canada • Statistique Canada
Methodology
 Focus on commodities and
activities through two-phase
strategy
• Collect key information to
Update the Business Register
And select a targeted sample
• Applicable mainly to activity and
commodity surveys
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Large
Sample
Gather information on
activity/commodity and
select target sample
Send full
questionnaire to
sub-set
Use of Tax Data and Sampling
 Population divided into two components:
• The very complex
 Census of these enterprises
 Financial and characteristics information collected
 The rest
• Tax data will be used for the financial information
• Collect characteristics information
• The large enterprises with a simple structure
• Pilot project to use tax data
Statistics Canada • Statistique Canada
Metadata
 The backbone of all survey processes
 Developed semantic model which illustrates and
documents all survey concepts and their
properties and relationships with other concepts
within a domain of knowledge
 Developed standard nomenclature and
numbering system for survey variables, cells,
code sets
• Will be re-used for all surveys to be integrated into
IBSP
Statistics Canada • Statistique Canada
Collection
 Develop electronic questionnaire
 Modular Approach
 Implement common editing strategy
• Active collection management
• Resolve as much as possible failed edits through
automated editing and imputation to reduce follow-up
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Collection
 Collection primarily via EDR
• Modular Approach
• Built in edits
• Spreadsheets sent via e-file channel for some large
enterprises



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Reduce the collection window
Still keep paper collection for small businesses
Around ??? Units collected
4 million $ Collection budget
Statistics Canada • Statistique Canada
Data Processing
 Implementation of common editing strategy
 Resolve as much as possible failed edits through
automated editing and imputation to reduce follow-up
 Systems
• Common systems platform
• Improve generalized systems
• Re-use some existing systems
 Create Data Service Center
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Streamline the Survey Process –
The Rolling Estimates
 Current processing model for annual business
surveys
Sampling
Collection
Processing
Analysis
Dissemination
• Several occurrences of manual interventions through
the processes
• Estimates and quality indicators are only produced near
the end
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Streamline the Survey Process –
The Rolling Estimates 2
 Proposed processing system - iterative
• Collection, processing and analysis done in parallel
• Quality indicators used to dynamically manage collection
• Basic principle: no manual intervention inside an iteration
Collection
Dissemination
Sampling
Processing
Analysis
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Data Service Center
IBSP or
Dissemination
IBSP
IBSP or BSS-CR
CAPTURED DATA
MICRO DATA
MACRO DATA
DISSEMINATION
DATA
Processes
Processes
Processes
Processes
Quality Assurance
Macro Corrections
Data Analysis
Confidentiality
Transformations
CANSIM Load
- Quality Assurance
- Data Analysis
- Publication
-
Capture Edits
Coding
Quality Assurance
Progress Reports
Transformations
-
Quality Assurance
Micro Corrections
E&I
Allocation
Estimation
Aggregation
Cube Creation
-
EDR
CLIENT
CUSTOMER
RESPONDENT
Paradata Repository (Data about Processes)
Metadata Repository (Data about Data)
Other Repository (BR, Tax, SNA, …)
Statistics Canada • Statistique Canada
Conclusion
 5 year developmental project
 Generate 2.5 millions of efficencies
 Better coherence among survey programs,
SNA and administrative
 Currently in the field for collection
 Project will continue for several years
Statistics Canada • Statistique Canada