1. Judy Lee Enterprise Statistics Division Statistics Canada I 1 Developing Metadata Standards in an Integration Project at Statistics Canada United Nations Economic Commission for Europe Workshop on International Collaboration for Standards-Based Modernization (Geneva, Switzerland 5-7 May 2015) Governing and Maintaining Statistical Standards

2. Statistics Canada’s Corporate Business Architecture Corporate Context  Corporate Business Architecture launched in 2010 Objectives: Efficiencies, Quality, Robustness Efficient, Robust, and Responsive business architecture Reduce operating cost, enhance quality assurance, improve responsiveness Maximize re-use; eliminate re-work: metadata driven systems  Review of Statistics Canada’s Business Statistics Program Develop generalized model for producing business statistics Shared and generic corporate services and systems for collecting, processing, disseminating and storing statistical information. Global optimum supersedes local preferences 2

3. Integrated Business Statistics Program 3  IBSP Objectives and Business Outcomes Generic model with improved quality and coherence across programs Robust Infrastructure; Reduce Cost and maintenance Flexible to respond to client needs; Reduce respondent burden  IBSP Key Components Maximum Use of Tax Data; Content Harmonization; Electronic Questionnaire as the primary mode ; Two-phase sampling; Rolling Estimates; Common Metadata Driven Tools and Generalized Systems; Top Down Analytical Approach  IBSP Scope, Partners, and Dependencies Over 90 existing business surveys covering manufacturing, services, retail, agriculture, capital expenditure, energy and research and development; financial and ad hoc surveys Partners: 8 subject matter divisions + 8 service provider divisions  IBSP project of development and integration

4. IBSP Metadata Value Proposition 4  IBSP Metadata Objectives and Business Outcomes More efficient, flexible tools; reduction of manual intervention Coherence from questionnaire development to processing to dissemination More harmonized data and metadata definitions Ensures consistently applied standards and structure Shared metadata repository(s) across systems and partners Promotes uptake / integration of large volume of surveys Facilitates training, maintenance and knowledge transfer Aids in development of common information model and the Enterprise Architecture Integration Platform (EAIP)

5. IBSP Variable Naming Framework 5 Goals and Expected Outcomes:  Standardization, Coherence, Usability  Consistent, coherent, and logical naming framework One variable to many question text/wording; Root variables and sub-variables  Logical generation of cell numbers from variable names Naming Structure by Variable Type  Statistical Variables: Anchored on Statistical Concepts to measure;  Process Control and Design Variables: Anchored on GSBPM  Identification Variables: Anchored on level of statistical activity and “What it identifies”  Derived Variable – A statistical variable with a formula  Transformed Variables – Transformed Variable Code set

6. IBSP Variable Semantic – Deconstructing Variable 6

7. IBSP Statistical Concepts 7

8. 8 Business AttributeFinancialEconomicSocialPhysical AdministrativeAssetDispositionLabourResource Use Business-Activity Capital ExpenditureValue AddedPopulation Business-performanceEquitySupply Business-SizeExpenseAdjustment Business-StructureLiabilityInput ProductProfit-loss Output Client of BusinessRevenue Geographic-locationDisposals Funding Organization Performance Use

9. Statistical Variable Naming Convention - Structure 9 CONCEPT (PRIME WORD) CLASS WORDS (Chronology, Measurement, Identification, Text) Modifiers (“Last”, “First”, ) Other (“By” Classification)

10. Statistical Variables – Examples of Cell Number Ranges 10

11. Results to Date – Statistical Variables and Sub Variables by Concept 11 Concepts Wave 1 Variables Wave 1 Sub Variables Wave 1 Total Wave 2 Variables Wave 2 Sub Variables Wave 2 Total Grand Total administrative64114 178 12122 200 adjustment56338394 asset120 business-activity3572 107 100160260 367 business-performance1016 26 business-size16 91322 38 business-structure12 3 37578 81 capacity-utilization246 6 capital-expenditure36673 709 265379 788 client-of-business11119130 Disposal6312 318 Disposition51477878301 Equity202 2 expense12089 209 82743825 1034 geographic-location426 30 26727753 783 input4418422 liability707 7 net-profit101 1 performance-use6149155 profit-loss2 22 product1052369 2474 86908994 3468 resource-use347 7 revenue451256 707 46290336 1043 supply40942894698 Grand Total8503929 4779 14061609817504 22283

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13. IBSP Content Metadata Outcomes  IBSP Content Modular approach to Content Harmonization  IBSP Variable Naming Framework Statistical Variables systematically generated based on Statistical concepts  Content Metadata stored in One Relational Database Variables Names, Cell Numbers, Mnemonics, Question Texts, Response Sets Promotes coherence, searchability, harmonization and delivery  Delivery of Content Metadata: Automated delivery to other systems and databases: Collection, Processing, Integrated Metadatabase 13

14. Conclusion and Next Steps 14 1.Unprecedented opportunity to name variables consistently from collection to just before dissemination. 2.Naming Framework has proven to be robust and expandable Possible expansion of bandwidths for future waves Will accommodate a total of almost 100 economic surveys 3.Integration of naming functionality into core IBSP system for consolidation phrase 4.Development, Implementation, and Governance centralized at project level. 5.Strong governance and Change Management control 6.Significant impact on usability, searchability, and interoperability