513
edits
Changes
Jump to navigation
Jump to search
Line 45:
Line 45: + + − <b>How to achieve:</b> − * Summarize how the architecture meets the data needs of the users and other key stakeholders including: − * How does the data asset contribute to outcomes/needs of the user and other stakeholders − * Gaps in the existing data assets to meet the needs of the users and other stakeholders and how the architecture addresses these gaps − * Gaps in data collection and analysis and how the architecture is addressing it so that ESDC can ensure that we are serving the members of our society − * Alignment to the data foundation of the ESDC information/data architecture practice − * Alignment to the theoretical foundation of the ESDC information/data architecture practice − − <b>Tools:</b> − * For Data Foundation – Implement: − * Data Catalogue − * Benefits Knowledge hub − * Data Lake (growth) − * Data Science and Machine Platform − * Stakeholder Requirements − * Solution Requirements − − <b>How to achieve:</b> − * Summarize how the architecture aligns to “collect with a purpose” including: − * What is necessary (as opposed to what is sufficient) to meet the stakeholder need − * Supporting Performance Information Profiles (PIPs) used to assess a progress towards target and broader objectives − <b>Tools:</b> − * Value Stream (Value Item and Value Proposition – Context on What we measure) − * KPI Linked to benefits outcomes and objectives − − <b>How to achieve:</b> − * Summarize reusability of the architecture’s data assets given: − * Context of data assets and user and stakeholder needs − * Data quality and fit for purpose − * Privacy and Security Regulatory Framework − <b>Tools:</b> − * Legislative / Regulations − − <b>How to achieve:</b> − * Summarize how the architecture meets the data quality requirements of third-party sources: − * Data quality meets fit for purpose − * Data quality dimensions including: − * Relevance, − * Timeliness − * Consistency, − * Reliability, − * Interpretability, − * Usability − * Data quality mechanism − <b>Tools:</b> − * Data Foundation – Implement (Leverage the standard definition) − * Data Catalogue − * Benefits Knowledge hub − * Data Lake (growth) − * Data Science and Machine Platform − <b>How to achieve:</b> − * Summarize how the architecture assists in defining key data management roles and their responsibilities to ensure data is correct, consistent, and complete including: − * Identifies the data steward responsibilities; − * Identifies the data consumer responsibilities, and; − * Identifies the data custodian responsibilities. − <b>Tools:</b> − * Stakeholders − * Business Process Model − * Functional Requirements − * Business Glossary − − <b>How to achieve:</b> − * Summarize how the architecture’s data assets demonstrate alignment with department's data governance and strategy including: − * Alignment to the data foundation of the ESDC information/data architecture practice − * Alignment to the theoretical foundation of the ESDC information/data architecture practice − <b>Tools:</b> − * Target state (solution data elements) − * Data Foundation – Implement (Leverage the standard definition) − * Data Catalogue − * Benefits Knowledge hub − * Data Lake (growth) − * Data Science and Machine Platform − * Theoretical Foundation − * EDRM (Conceptual and Logical) − * Business Glossary − * Departmental Data Strategy − − <b>How to achieve:</b> − * Summarize for each key data assets: − * Retention and disposition schedules − * Disposition process − <b>Tools:</b> − * Target state (solution data elements) − * Non Functional Requirements − * IM Best Practices and Standards − − <b>How to achieve:</b> − * Summarize how the architecture enables interoperability, reuse and sharing to the greatest extent possible within and across departments − * Summarize how the architecture avoids data duplication − <b>Tools:</b> − * Target State − * Data Foundation – Implement (Leverage the standard definition) − * Data Catalogue − * Benefits Knowledge hub − * Data Lake (growth) − − <b>How to achieve:</b> − * Summarize the alignment to departmental/GC: − * Data taxonomy structure − * Data classification structure − <b>Tools:</b> − * Data Foundation – Implement (Leverage the standard definition) − * Data Catalogue − * Theoretical Foundation − * EDRM (Conceptual and Logical) − * Business Glossary
GC Enterprise Architecture/Framework (view source)
Revision as of 12:01, 24 February 2021
, 12:01, 24 February 2021no edit summary
== Information architecture ==
== Information architecture ==
<b><i>[https://wiki.gccollab.ca/GC_Enterprise_Architecture/Framework/DataGuide click here for Information/Data Architecture guide] </i></b>
Information architecture includes both structured and unstructured data. The best practices and principles aim to support the needs of a business service and business capability orientation. To facilitate effective sharing of data and information across government, information architectures should be designed to reflect a consistent approach to data, such as the adoption of federal and international standards. Information architecture should also reflect responsible data management, information management and governance practices, including the source, quality, interoperability, and associated legal and policy obligations related to the data assets. Information architectures should also distinguish between personal and non‑personal data and information as the collection, use, sharing (disclosure), and management of personal information must respect the requirements of the ''Privacy Act'' and its related policies.
Information architecture includes both structured and unstructured data. The best practices and principles aim to support the needs of a business service and business capability orientation. To facilitate effective sharing of data and information across government, information architectures should be designed to reflect a consistent approach to data, such as the adoption of federal and international standards. Information architecture should also reflect responsible data management, information management and governance practices, including the source, quality, interoperability, and associated legal and policy obligations related to the data assets. Information architectures should also distinguish between personal and non‑personal data and information as the collection, use, sharing (disclosure), and management of personal information must respect the requirements of the ''Privacy Act'' and its related policies.
=== Collect data to address the needs of the users and other stakeholders ===
=== Collect data to address the needs of the users and other stakeholders ===
* assess data requirements‑based program objectives, as well users, business and stakeholder needs
* assess data requirements‑based program objectives, as well users, business and stakeholder needs
* collect only the minimum set of data needed to support a policy, program, or service
* collect only the minimum set of data needed to support a policy, program, or service
* reuse existing data assets where permissible and only acquire new data if required
* reuse existing data assets where permissible and only acquire new data if required
* ensure data collected, including from third-party sources, are of high quality
* ensure data collected, including from third-party sources, are of high quality
=== Manage and reuse data strategically and responsibly ===
=== Manage and reuse data strategically and responsibly ===
* define and establish clear roles, responsibilities, and accountabilities for data management
* define and establish clear roles, responsibilities, and accountabilities for data management
* identify and document the lineage of data assets
* identify and document the lineage of data assets
* define retention and disposition schedules in accordance with business value as well as applicable privacy and security policy and legislation
* define retention and disposition schedules in accordance with business value as well as applicable privacy and security policy and legislation
* ensure data are managed to enable interoperability, reuse and sharing to the greatest extent possible within and across departments in government to avoid duplication and maximize utility, while respecting security and privacy requirements
* ensure data are managed to enable interoperability, reuse and sharing to the greatest extent possible within and across departments in government to avoid duplication and maximize utility, while respecting security and privacy requirements
* contribute to and align with enterprise and international data taxonomy and classification structures to manage, store, search and retrieve data
* contribute to and align with enterprise and international data taxonomy and classification structures to manage, store, search and retrieve data
=== Use and share data openly in an ethical and secure manner ===
=== Use and share data openly in an ethical and secure manner ===
