Case study: Statistics Canada

Statistics Canada is undergoing an agency-wide modernization initiative to promote organizational efficiency, increase robustness of systems and processes and shorten implementation time for new projects.  One of the key principles guiding this review is: Create metadata at the beginning of every process and use them throughout the project life cycle.  A working group with members representing all phases of the statistical business process has been developing a strategy for statistical metadata management and an action plan to support these principles.  The goals being considered for the strategy relate to four themes: drive, make available, structure and manage.  Actions required to implement the strategy are expected to cover:

• Establishing governance;
• Creating centres of expertise;
• Adopting standard structure and content specifications (including GSIM);
• Developing a metadata portal to identify authoritative sources of metadata and make them available to users, and;
• Identifying metadata gaps required to enable metadata-driven processes.

GSIM is being adopted to specify, design, and implement components that will easily integrate into “plug’n’play” solution architectures and seamlessly link to standard exchange formats (e.g. DDI, SDMX). It is important to note that GSIM does not make assumptions about the standards or technologies used to implement the model, which leaves the Agency room to determine its own implementation strategy.

Statistics Canada is beginning to use GSIM’s Concepts and Structures Groups as the main classifiers of metadata. These groups contain the conceptual and structural metadata objects, respectively, that are used as inputs and outputs in a statistical business process. The Structures group defines the terms used in relation to data and their structure. The Concepts group defines the meaning of data, providing an understanding of what the data are measuring.

Work focuses on aligning the new GSIM-based classification with other internal metadata classification models currently in use. For instance, IBSP identifies the following types of metadata:

-          Reference metadata: Describes statistical datasets and processes.

-          Definitional metadata: Description of statistical data (with meaning to business user community) E.g., concepts, definitions, variables, classifications, value meanings and domains.

-          Quality metadata: Quality evaluation of a dataset or individual records; helps users assess the fitness of associated data for their specific purposes. E.g., CV, rolling estimates, analysts comments about the quality of a set of records.

-          Operational metadata: links between the concepts and the physical data.

• Processing specifications: Capture, edit and output specifications and processing flags.
• Processing results: Content, outcomes, outputs of processing.
• Paradata: Data from the collection operation or statistical analysis used to support decision making in the survey process or statistical analysis. These include system logs, history files and comments.[1]

-          Systems metadata: Low-level information about files, servers and infrastructure that allows the physical IT environment to be updated without re-specification by the end user.