GC Mainframe Strategy 2.0
For many, the word 'mainframe' conjures images of antiquated technology employing large spools of magnetic tape for data storage and punch cards to execute commands in COBOL, the same programming language your uncle used. While this is no longer the mainframe's reality, given its reputation, it would surprise some to find out that many of the GC's critical business applications supporting security and financial transactions are all mainframe-based. The GC is not unique in this regard. The banking and insurance sectors still rely heavily on mainframes to help day-to-day banking, shopping, and credit card transactions. Despite their dependable service history, the mainframe suffers from a growing risk; a shrinking pool of skilled IT professionals supporting this technology and a small market of companies still supporting and innovating on mainframe platforms. For that reason, with the fullness of time, it is not a question of if the GC will stop using mainframes, but a question of when. That may come in a few years for some organizations, but it may be decades for others. Technological advancement has also made it possible to process mainframe workloads with the same or more significant computing power through modern and alternative technology. Further research is required regarding the security of those platforms and the subsequent cost effectiveness. The first version of the GC mainframe strategy provided an overview of the mainframe's status as technology across other industry sectors and then looked at their usage within the GC. It presented the risks associated with operating mainframes that the GC would need to manage continuously. The most pernicious of those was a workforce with mainframe skills. The second version of the strategy provides a migration approaches path for GC departments and makes recommendations to SSC and departments to incrementally approach migration, from discovery to architecture planning. The strategy will outline questions each department should be asking themselves as they consider their mainframe applications' future. The key to this is the question of fit for purpose. Given the strategic direction of your organization, do your mainframe applications continue to support that course? If not, what migration options exist? Finally, the strategy provides recommendations to improve the stewardship of mainframes and the applications they host.
Datasets available for download
Additional Info
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| Last Updated | October 20, 2025, 02:55 (UTC) |
| Created | October 20, 2025, 02:55 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Title
Title for the Dataset.
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GC Mainframe Strategy 2.0 |
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Description
A description of the dataset.
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For many, the word 'mainframe' conjures images of antiquated technology employing large spools of magnetic tape for data storage and punch cards to execute commands in COBOL, the same programming language your uncle used. While this is no longer the mainframe's reality, given its reputation, it would surprise some to find out that many of the GC's critical business applications supporting security and financial transactions are all mainframe-based. The GC is not unique in this regard. The banking and insurance sectors still rely heavily on mainframes to help day-to-day banking, shopping, and credit card transactions. Despite their dependable service history, the mainframe suffers from a growing risk; a shrinking pool of skilled IT professionals supporting this technology and a small market of companies still supporting and innovating on mainframe platforms. For that reason, with the fullness of time, it is not a question of if the GC will stop using mainframes, but a question of when. That may come in a few years for some organizations, but it may be decades for others. Technological advancement has also made it possible to process mainframe workloads with the same or more significant computing power through modern and alternative technology. Further research is required regarding the security of those platforms and the subsequent cost effectiveness. The first version of the GC mainframe strategy provided an overview of the mainframe's status as technology across other industry sectors and then looked at their usage within the GC. It presented the risks associated with operating mainframes that the GC would need to manage continuously. The most pernicious of those was a workforce with mainframe skills. The second version of the strategy provides a migration approaches path for GC departments and makes recommendations to SSC and departments to incrementally approach migration, from discovery to architecture planning. The strategy will outline questions each department should be asking themselves as they consider their mainframe applications' future. The key to this is the question of fit for purpose. Given the strategic direction of your organization, do your mainframe applications continue to support that course? If not, what migration options exist? Finally, the strategy provides recommendations to improve the stewardship of mainframes and the applications they host. |
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Keywords/tags categorizing the dataset.
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Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
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Dataset Size
Dataset size in megabytes.
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Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
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Published Date
Published date of the dataset.
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2021-11-29 |
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Time Period Data Span (start date)
Start date of the data in the dataset.
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Time Period Data Span (end date)
End date of time data in the dataset.
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GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
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Access category
Type of access granted for the dataset (open, closed, service, etc).
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License
License used to access the dataset.
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Open Government Licence - Canada |
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Limits on use
Limits on use of data.
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Location
Location of the dataset.
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https://open.canada.ca/data/en/dataset/7f5e27ba-80f6-4aca-bfab-9ce88801ef74 |
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Data Service
Data service for accessing a dataset.
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Owner
Owner of the dataset.
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Treasury Board of Canada Secretariat | Secrétariat du Conseil du Trésor du Canada |
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Contact Point
Who to contact regarding access?
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Contact Point Email
The email to contact regarding access?
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Publisher
Publisher of the dataset.
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Publisher Email
Email of the publisher.
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Author
Author of the dataset.
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Author Email
Email of the author.
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Accessed At
Date the data and metadata was accessed.
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Identifier
Unique identifier for the dataset.
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Language
Language(s) of the dataset
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Link to dataset description
A URL to an external document describing the dataset.
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Persistent Identifier
Data is identified by a persistent identifier.
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Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
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Contains data about individuals
Does the data hold data about individuals?
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Contains data about identifiable individuals
Does the data hold identifiable data about individual?
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Portal Type
Platform type of the source portal.
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Version
Version of the datatset
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None |
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Source
Source of the dataset.
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None |
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Version notes
Version notes about the dataset.
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Is version of another dataset
Link to dataset that it is a version of.
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Other versions
Link to datasets that are versions of it.
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Provenance Text
Provenance Text of the data.
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Provenance URL
Provenance URL of the data.
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Temporal resolution
Describes how granular the date/time data in the dataset is.
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GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
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GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
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Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
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Community Permission
Community permission (who gave permission).
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The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
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Number of data rows
If tabular dataset, total number of rows.
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Number of data columns
If tabular dataset, total number of unique columns.
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Number of data cells
If tabular dataset, total number of cells with data.
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Number of data relations
If RDF dataset, total number of triples.
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Number of entities
If RDF dataset, total number of entities.
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Number of data properties
If RDF dataset, total number of unique properties used by the triples.
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Data quality
Describes the quality of the data in the dataset.
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Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.
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