Data is a valuable resource that, when effectively managed and utilized, can transform how we work. Data governance positions data as a strategic asset by establishing clear roles, processes, and the right technology for secure and shared use of data. This structured approach ensures that consistent policies and standards exist across various departments, faculties and units, enabling data for analysis, insights, AI and other innovative uses.
In addition to delineating responsibilities for different types of data, data governance defines the standards and guidelines that govern data definitions, quality, access, sharing, usage, and overall management of data through its lifecycle. By following these comprehensive guidelines, Queen’s ensures that data is governed, readily accessible, and meets requirements for compliance and security.
Leadership and Operating Model
Data Governance is a subset of the Data Strategy work being conducted at Queen's. Under the direction of the Data Governance Council (DGC), the Data Strategy and Governance (DSG) Office dedicates itself to establishing a vision, mission and governance framework, and defining objectives.
Vision
Data is governed and managed as an asset and used to provide insights in support of the university’s business capabilities and strategic goals.
Mission
To develop university-wide data accountability, leadership structure, standards and processes to improve data collaboration, quality, literacy and shared utilization, while ensuring data security, compliance and privacy.
Framework
The data governance framework defines leadership, accountability and responsibility related to data use, sharing and handling. It is supported by an operating model and relevant policies and procedures, all of which help build and foster a culture of data excellence where the right users get access to the right data at the right time through well-defined processes.
It sets the direction for security, accountability, and trustworthiness of data and provides a framework to support the goal of high quality and optimized use of data.
Data Accountability and Oversight: Through establishing clear data roles including stewardship, leadership, and decision-making structures.
Leverage value of data and break down silos: Through common policies, standards and processes for data access sharing and usage.
Enable discoverability: Through glossary of data terms, metadata, and making data visible and searchable.
Ensure compliance: Minimize legal and reputational risks through security, privacy, and data lifecycle management policies.
Establish trust: Through processes and standards for data quality that promote trust in data accuracy, completeness, and data consistency.
Data Governance
| Data Stewardship | Data Policies and Standards | Data Use, Security, and Compliance | Data Quality |
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