Transcript Chapter 8 - Data Quality and Metadata

ESCM Chapter 8:
Data Quality and Metadata
United Nations Oslo City Group on Energy Statistics
8th Oslo Group Meeting, Baku, Azerbaijan
24-27 September 2013
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Outline of the presentation
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Background ESCM Chapter 8 outline
Comments from OG7 and VM3
Chapter structure and content
Changes to quality dimensions
Examples/issues of data quality
Metadata and examples
General Statistical Business Process Model
and examples of implementation
 Next steps
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Background – ESCM Chapter 8 outline
 Provide guidance on the compilation of energy
indicators and the preparation of metadata for
energy statistics:
 A. Data quality indicators for energy statistics
 B. Metadata
– Description of metadata specific to energy
statistics
– Presentation and dissemination of metadata
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Comments from OG7
 OG7:
• Specific energy issues that will affect quality.
• The challenges with consistency within energy statistics itself and in
relation to other statistical areas.
• The practice of using secondary sources for validating purposes.
• The use of the balances as a quality check.
• General Statistical Business Process Model to include
administrative data sources.
• Quality in a decentralised model vs. a centralised system.
• Guidance for countries that do not have yet a well developed
energy system.
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Comments from VM3
 VM3:
• Overall good description of data quality and metadata.
• Incorporate energy specific examples and references to
energy issues.
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Chapter structure and content
 Three main sections to the chapter
 Data quality
 Metadata
 Ensuring data quality and metadata using the General Statistical
Business Process Model (GSBPM)
 Each section followed by energy related examples
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Changes to quality dimensions
Updated data quality dimensions and indicators for
measuring quality from UN’s International
Recommendations for Energy Statistics.
 Static elements
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Relevance
Credibility/objectivity
Accuracy
Timeliness
Coherence
Accessibility
 Dynamic elements
• Non-response
• Coverage
• Sampling
Other dimension of quality for metadata:
• Interpretability
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Energy specific examples/issues of
data quality
 Statistics Austria’s country practice on Energy Balances for
Austria and the Laender of Austria.
 Report on Energy Supply and Demand (RESD) from
Statistics Canada.
 Use of standard concepts, definitions, classifications –
North American Industry Classification System
 The Swedish Official Statistics decentralized system.
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Metadata
 Important for assessing “fitness for use” and ensuring
interpretability.
 Required at every step of the survey process.
 Critical for enabling comparisons with other data.
 Used to prescribe definitions, concepts, variables and
standards.
 Supports the harmonization of international surveys and
data.
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Example of metadata
 Statistics Canada’s Integrated Business Statistics
Program metadata requirements
• Development, test, user acceptance testing,
production-simulation, and production environments.
• Two levels of metadata:
 Global: To support processes and functionalities
of all surveys.
 Survey: Specific to each survey depending on use
and purpose.
• Defining metadata to drive systems.
• Quality of metadata.
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Example of metadata characteristics to drive systems
and the GSBPM phases  1. metadata is defined in the design and
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GSBPM Design phase
2.1 Design outputs
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2.2 Design variable descriptions
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2.3 Design data collection
methodology
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2.4 Design frame and sample
methodology
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2.5 Design statistical processing
methodology
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2.6 Design production and systems
workflow
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build phases of development
2. high-level concepts underpin system
content and metadata is used to describe
concepts at various levels of abstraction
4. metadata is managed through a user
interface (or several); metadata concepts
underpin the browse functionality for
locating information objects, such as
variables and questionnaires, and their
relationships and attributes
5. uses centralized management supporting
single entry and single source of, e.g.
codesets: change once and propagate
across system wherever codeset is used,
and is flexible, not hard-coded
8. applies administrative metadata or
‘settings’ at different levels in the system
Example of metadata and quality dimensions
Criterion
Accuracy
Definition
Names, codesets and other metadata are created
and implemented according to the IBSP naming
convention, the hierarchy of identifiers and other
standards to ensure that metadata are aligned
with the established semantics that reflect the
business survey management.
Metric
Test different features of the system, such as the
Questionnaire Development Application, to verify
that the logic required by the various standards is
reflected in the system functionality. Gather
information during user testing regarding the
quality and suitability of codesets to their purpose.
Frequency
Every fiscal year.
Example
A questionnaire can be retrieved by its Survey ID,
SDDS number or name.
Level
System
Follow-up
Log any issues concerning the standards and
structure in the system and address the issues by
working with the Standards Division or by fixing
the problem in the metadata registry, as the case
dictates.
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Quality assurance must be built into
all stages of the survey process
General Statistical Business Process
Model (GSBPM) Survey Stages:
Quality Assurance
Framework
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1.
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Specify needs
Design
Build
Collect
Process
Analyze
Disseminate
Archive
Evaluate
Examples of GSBPM implementation
 Implementation of GSBPM on crude oil in the State
Statistical Committee of the Republic of Azerbaijan
 Statistics Canada Integrated Business Statistics
Program (IBSP) Initiative
• Redesign of the Business Statistics Program
• Approximately 120 business surveys including the content
redesign of 23 energy surveys.
• Common sampling, collection and processing methodologies,
and sharing of common systems and analytical tools.
• Will be done in several phases to be completed by 2016.
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IBSP - Monthly Refined Petroleum
Products Survey
 Data on the activities of refineries and other producers of
refined petroleum products.
 Consultations with clients and respondents across
Canada to identify the statistical needs for the future,
review of concepts, and respondents’ ability to provide
the information requested.
 New content includes biofuels contained in light fuel oil,
diesel and aviation fuel.
 Meetings with the IBSP collection, processing and
analysis working group to identify the processing
requirements.
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IBSP - Monthly Refined Petroleum
Products Survey
 Report is prepared outlining the sampling methodology,
collection strategy, content of the questionnaire and
collection edits, standards for classifications and
metadata, survey processing, data integration, edit and
imputation, adjustments and additions, estimation,
reports, dissemination, break in the series, and
documentation.
 The design of electronic questionnaires is starting and
field testing will take place this winter with
production/collection to be ready for January 2015.
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Next steps…
 Determine if need for different or additional energy related
examples and issues.
 Review structure, content, and length of chapter for
approval.
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Thank you!
Andy Kohut, Director
Manufacturing and Energy Division
Statistics Canada
Section B-8, 11th Floor, Jean Talon Building
Ottawa, Ontario
Canada K1A 0T6
Telephone: 613-951-5858
E-mail: [email protected]
www.statcan.gc.ca
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