Important: The GCConnex decommission will not affect GCCollab or GCWiki. Thank you and happy collaborating!

GC Enterprise Architecture/Standards/Information Architecture

From wiki
Jump to navigation Jump to search


<<Business Architecture

Application Architecture>>


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