Statistical Metadata Strategy and GSIM Implementation in

Canada

Statistics Canada

2

Background: Corporate Business Architecture (CBA)

Five of the architectural principles of the CBA review of particular significance to statistical metadata are:

• metadata-driven processes; • maximizing reuse of existing corporate systems;• optimise use of corporate services;• enforcing reuse of concepts and classifications and • governance of data and metadata architectures.

3

Background: Metadata Architecture Modernization (MAM) Strategy for Statistical Metadata Management

was approved in 2013 Strategy is being implemented through a CBA

project: the MAM project approved in 2014 goal is to improve interoperability between our

systems and improve the management of metadata

4

Processes use

metadata

Standardized metadata in

all processes

Metadata is

managed

Enterprise-focused

governance

Single point of access to metadata

Standard formats meet user needs

Users have the metadata

they need

Information architecture supports use

GOALS from the strategy

Initiate IMDB

re-design project

Develop high-level

architecture

Develop centres of

responsibility

Fill metadata

gaps

Initiate single

point of access

Adopt structure

and content

standards

Communication and training

ACTIVITIES of the MAM project

Project activities based on the Strategy

Fill policy gaps

Elaborate Architecture

for IMDB (high level)

Develop operational governance

5

Supports information architecture governance

Standardized metadata for collaborative design and

execution

Links to dissemination

standards (e.g. DDI, SDMX)

Accelerates standardized

interfaces between services (e.g.

Collection)

StatCan’s “Plug and Play” architecture – EAIP & components

Registry support - standardized metadata,

data framework

Strategy, governance,

policy revision

MAM is part of corporate approach to transformation

6

Move towards common components and capabilities

7

GSIM 1.1 at Statistics Canada Information architects and project teams working on the design of

new systems or the development of services for existing systems:• adopted to specify, design, and implement components for

integration into “plug’n’play” architectures and link to standard formats (e.g. DDI, SDMX).

• GSIM Objects will be the inputs/outputs of GSBPM statistical sub-processes implemented as services within StatsCan’s CBA Service Approach.

Standards Division for structuring metadata, as a common vocabulary (concepts group and statistical classification model)

8

Classification Exchange Model

DDI

CMS IMDB

Dynamic HTML

Publication

<XML/>ESDC

(external client)

CMS Classifications

ConcordancesConformances Non-CMS

Classifications

GUI

Static HTML

CommonClassification

Model

ExchangeService

CMS Classifications

Census

NDM

Multiple Formats

Transformation

XHTMLCSV

CCS

IBSPStandard

Classifications

CensusClassifications

CensusDB

ClassificationManagement

User

User

SharePoint Environment

SDMX

9

Other Common Models

SSME

IMDB

IBSP Metadata

DB

<XML/>

CommonQuestionnaire

Model

ExchangeService

<XML/>

CommonSurveyModel

ExchangeService

QDT Questionnaires

IBSP Questionnaires

Dynamic HTML

Publication

Static HTML

NDMMetadata

DB

Transformation

Transformation

QDT and IBSP Questionnaires

SurveyMetadata

SurveyMetadata

<XML/>

CommonVariableModel

ExchangeService

Census

CensusDB

User

SharePoint Environment

QDT

QuestionnaireApp

Social Surveys

BusinessSurveys

User

User

DisseminationTools

User

StandardVariables

and Concepts

CensusVariables

and Concepts

StandardVariables

and Concepts

CensusVariables

and Concepts

Dissemination

DDI

Multiple Formats

XHTMLCSV

SDMX

10

Work supporting the architectural transformation Creation of a governance committee to manage

information architecture Review and updates of policy instruments Training and communication

11

The Information Architecture Governance Committee A high-level governance committee is being developed to ensure a

corporate approach to statistical metadata management. This committee is expected to:• Coordinate the operational governance of the Agency’s

information architecture • Coordinate the adherence to principles and standards for

statistical information and metadata management• Assist and advise on the development and application of the

corporate information architecture• Assist and advise on the review of policies and procedures. • Identify challenges, issues and opportunities in the development

and implementation and coordinate these in a timely manner

12

Statistical Metadata Policy Proposal

Directive on Statistical

Standards and Frameworks

Directive on Informing

Users of Data Quality and

Methodology

Policy on Statistical Metadata

Policy on Information

Management; related

directives

Requirements, roles and responsibilities for: • Creating, managing

and monitoring content and metadata standards

Requirements, roles and responsibilities for:• Creating statistical

metadata to support survey and statistical programs

High-level governance, roles and responsibilities for designing, creating and maintaining statistical metadata

Directive on Informing

Respondents

Policy on Microdata

Access

Policy on the review and testing of

questionnairesId

entif

y lin

ks t

o

13

Lessons learned

Partnership between business managers and enterprise architects is critical

Cultural issues and has necessitated that we take a federated approach to metadata management

It is important to get the business requirements as well as the IT requirements for metadata systems

Governance, policy instruments and training are needed for transforming the way we do business

Communication and engagement helps to “pave the way”

14

Thanks