Difference between revisions of "GC Enterprise Architecture/Standards/Information Architecture"
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Latest revision as of 14:29, 24 July 2020
This page is a work in progress. We welcome your feedback. Please use the discussion page for suggestions and comments. When the page is approved and finalized, we will send it for translation. |
2. Information Architecture
This is a definition for GC Information Enterprise Architecture
Data Collection
- Ensure data is collected in a manner that maximizes use and availability of data
- Ensure data collected aligns to existing enterprise and international standards
- Where enterprise or international standards don't exist, develop Standards in the open with key subject matter experts
- Ensure collection of data yields high quality data as per data quality guidelines
- Ensure data is collected through ethical practices supporting appropriate citizen and business-centric use
- Data should only be purchased once and should align with international standards
- Where necessary, ensure collaboration with department/agency data stewards/custodians, other levels of government and indigenous people
Data Management
- Demonstrate alignment with enterprise and departmental data governance and strategies
- Ensure accountability for data roles and responsibilities
- Design to maximize data use and availability
- Design data resiliency in accordance with GC policies and standards
- Use Master Data Management to provide a single point of reference for appropriate stakeholders
Data Storage
- Ensure data is stored in a secure manner in accordance with the National Cyber Security Strategy and the Privacy Act
- Follow existing retention and disposition schedules
- Ensure data is stored in a way to facilitate easy data discoverability, accessibility and interoperability
Data Sharing
- Data should be shared openly by default as per the Directive on Open Government
- Ensure government-held data can be combined with data from other sources enabling interoperability and interpretability through for internal and external use
- Reduce the collection of redundant data
- Reduce existing data where possible
- Encourage data sharing and collaboration