Managing Transparent Institutional Data

Data governance is the foundational element of the university’s data strategy. Data governance is the formal integration of people, processes, and technology across the University to manage institutional data – a framework establishing trust of the data, eliminating data silos, building a common framework, and enhancing the use of data analytics for informed decision-making. Through data governance, transparency of information provides campus with the knowledge of what data is available at the university, where it is held and who has access. In addition, data governance identifies gaps in data, data quality, data integrity, and compliance issues acting as guardrails for data security, data privacy, and regulatory matters.​ 

Contact Us

Anna Miller

Director, Data Governance

(470) 578-5762 

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Data Governance Organization and Structure

The University System of Georgia (USG), Business Procedures Manual 12.0 Data Governance and Management, establishes minimal expectations for organization and structure as well as common definitions for roles and responsibilities.  Required elements include:​

  • Data Governance Committee(s)​
  • Roles and responsibilities:​
  • Data owner is defined as the university’s president/chief executive officer.​
  • Data trustees are the executives of the university who have overall responsibility for the data processed in their area(s). ​
  • Data stewards, designated by the data trustees, are personnel responsible for the data processed for a designated functional area. ​

Institutional data governance structures are developed in alignment with the USG expectations and within the context of the institution.​ 

Data Governance Guiding Principles​

  • Asset. KSU is the owner of all university data assets. The governance framework delegates responsibilities for data trustees and data stewards.​ 
  • Accessible. Data should be made accessible and data governance should be as least restrictive as possible while minimizing risk to the university (e.g., privacy, security, compliance).​ 
  • Efficient. Data governance processes should be standardized and as automated as possible to keep current with changes in data and data processes.​ 
  • Continuous Improvement. Policies and processes and procedures should be regularly evaluated as data and university needs change over time to ensure continued effectiveness and to seek opportunities for improvement. Data governance is an ongoing process.​ 

 

Scope​: The tenets and practices of the data governance framework are focused on enterprise data, which is used for accreditation, compliance, and findings impacting the institution.​