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This document outlines the Data Governance framework for Sequoias CCD, emphasizing the structure, guiding principles, and roles and responsibilities essential for effective data management and decision-making across the district.
This work is in response to District Objective 4.1 which is to increase the effective use of data and transparency in decision-making at all institutional levels from 2021 - 2025.
Action item 4.1.1 - Improve Data Governance practices, including the establishment and publication of clear definitions, responsibilities, and roles as well as data access, data entry, methodologies, and validation/correction protocols.
Action item 4.1.2 - Establish and publish procedures to ensure constituents know where to find needed data, have access to all relevant data, and ensure the data is regularly updated.
Working with the above as a guide, College of the Sequoias is embarking on a new chapter in managing data and supporting data informed decisions. We are doing this by taking good ideas from the past and building upon them with new modern architecture to bring about a governance process that ensures the right people are at the table in making decisions about data definitions, policies, usage, and maintenance. In addition, putting a process in place to discuss, track, and approve changes in definitions, coding structures, and data usage in a way that is useful for the institution, supports transparency, and is not overly burdensome.
Organizational Structure
Data Governance is a process that would require a group be formed that would report to the President or designee and function as an “Operational Group” as defined in the Governance and Decision-Making Manual.
This group would be called the Data Management Council.
Guiding Principles
Inspired by (Hopper, 2022) and (Meteyer, 2021)
Data Accuracy and Integrity:
Principle: We prioritize the accuracy, consistency, and reliability of data throughout its lifecycle.
Rationale: High-quality data is critical for informed decision-making and maintaining credibility.
Transparency and Accountability:
Principle: We commit to transparency in our data processes and hold ourselves accountable for data management.
Rationale: This approach builds trust and supports compliance.
Collaboration and Inclusivity:
Principle: We foster collaboration and ensure inclusivity in data-related decisions.
Rationale: This enhances effectiveness and incorporates diverse perspectives.
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Framework diagram inspired by (Hopper, 2022)
Roles and Responsibilities
Inspired by (The complete guide to data governance roles and responsibilities, 2021)
Data Management Council
The Data Management Council is the group that brings together the appropriate individuals who fill the roles depicted in the framework above representing Applications, Research, the Data Steward, the Data Governance Lead, plus the Data Managers and Data Experts from the domain(s) whose data issue is being worked on by the group.
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Data Governance Process and Swim Lanes
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Diagram inspired by (Ozturk, 2015)
1. Feasibility Assessment: Research and Applications staff assess each request to determine if it falls within the data governance scope and can be realistically implemented.
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3. Business Specifications Completion: In cases of incomplete business specifications, the relevant staff and the originating constituents are responsible for collaborating to finalize these specifications on an as-needed basis.
References providing ideas and inspiration for the COS Data Governance model
Business Procedures Manual | 12.2 Governance Structure | University System of Georgia. (n.d.).
http://www.usg.edu. Retrieved December 6, 2023, from https://www.usg.edu/business_procedures_manual/section12/C2819
Data Governance Manual 2016 The Data Governance Manual outlines the purpose, structure, goals,
participants, and responsibilities of OSDE’s Data Governance Program. (n.d.). Retrieved December 6, 2023, from https://sde.ok.gov/sites/ok.gov.sde/files/Data Governance Program Manual 03112016.pdf
Data Governance. (n.d.). Arcadia University. Retrieved December 6, 2023, from
Data Governance Model. (2020, January 21). http://www.swarthmore.edu .
https://www.swarthmore.edu/data-governance/data-governance-model
Firican, G. (2021, December 20). Why data governance is a must for any organization. LightsOnData.
https://www.lightsondata.com/why-data-governance-is-a-must-for-any-organization/
Firican, G. (2023, May 17). The importance and benefits of data ownership in data governance. LightsOnData.
https://www.lightsondata.com/data-ownership-benefits-importance/
Hopper, A. M. (2022). Practitioner’s Guide To Operationalizing Data Governance. John Wiley & Sons.
Kimachia, K. (2022, September 8). An overview of data governance frameworks. TechRepublic.
https://www.techrepublic.com/article/data-governance-framework/
Meteyer, J. (2021, November 17). Data Governance - History, Present, and Future. Vimeo.
https://vimeo.com/647121328/8313d96fa5
Office of the Provost. (n.d.). Definitions: Data Governance and Data Domains - University of Rochester. Office
of the Provost. Retrieved December 6, 2023, from https://www.rochester.edu/provost/university-data/data-governance-overview/what-is-data-governance/
Ozturk, M. (2015). Partnership for Success: Geeks, Nerds and Techies Collaborate. College of the Sequoias.
https://www.cos.edu/en-us/Research/Documents/CAIR%20Geeks.pdf
The complete guide to data governance roles and responsibilities. (2021, November 22). LightsOnData.
https://www.lightsondata.com/the-complete-guide-to-data-governance-roles-and-responsibilities/
The Current State of Data Governance in Higher Education. (n.d.). http://Www.researchgate.net .
Welcome to Data Governance! - University of Maine System. (n.d.). Data Governance.
https://www.maine.edu/data-governance/
Who belongs on a high-performance data governance team? | TechTarget. (n.d.). Data Management.
Retrieved December 6, 2023, from https://www.techtarget.com/searchdatamanagement/feature/Who-belongs-on-a-high-performance-data-governance-team