The information herein has been put together in order to fulfill the requirements of 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 that 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.
Data Domains
Different types of data naturally fall into specific areas which we are defining as data domains. As we address data governance concerns, we will focus on data in terms of these domains. This will allow us to have the needed discussion with the right people and have decisions owned by the appropriate domain owners. The domains we have identified are defined in the diagram below.
Governance Models
Links to books and articles we have reviewed:
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/
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
What is Data Governance?
Data governance is a critical function in any organization that deals with data. It involves creating and maintaining policies, procedures, and standards that govern how data is defined, collected, stored, used, and shared. In a data-driven world, data governance becomes increasingly important.
Data Governance Tasks:
Creating and enforcing data standards, policies and procedures
Establishing and maintaining the business verbiage (i.e. the business glossary)
Ensuring data quality and accuracy
Working with the data security and privacy teams
Overseeing data lineage and data provenance (origin)
Developing the data governance framework
Establishing, leading, taking part in, or supporting the data governance council/committee (depending on an individual’s role within the data governance framework)
Why is Data Governance important?
Data governance helps to ensure that data is usable, trustable, accessible, protected, and treats data as an asset to be utilized to identify trends, cost savings, behaviors and so much more. Effective data governance leads to better data analytics, which in turn leads to better decision making and improved operations support. It also helps to avoid data inconsistencies or errors in data, which lead to integrity issues, poor decision making, and a variety of organizational problems. A decision based on bad data is just a train wreck in the making. A train wreck that could have been prevented.
Data governance also plays an essential role in regulatory compliance, ensuring that organizations are consistently compliant with all levels of regulatory requirements - this is key for minimizing risks and reducing the likelihood of regulatory fines for non-compliance.
At its core, data governance leads to improved data quality, decreased data management costs, and increased access to data for all constituents. The result is better decision making and better business outcomes.
COS Governance Models Drafts
Data Governance Working Draft
Documenting Data Governance
Data Management Council Work
Status of data governance tasks will be housed within Jira where tasks regarding data elements or issues can be assigned and moved through the system to be worked on and defined until ultimately approved by a VP. Items will come to the governance process through various means and be entered into the system and move through the various stages as a request or need in the following order:
To Do
This is the initial entry point for governance items where the need/requirement will be documented after it has been reported by the constituent.
Draft
Initial work will be done to Identify the data domain(s) in which the data element would reside. Assigned and work is beginning within technology, research, or via a constituent. business specifications for new items, rebuild existing elements using new data warehouse,
SIG (Applications and Research Teams) Column
Discussion to make sure the data or the issue meets COS standards, is feasible and to set priority for work. Additional information may be requested from the constituent
Data Governance Column (Data Management Council)
Items that have been deemed feasible, prioritized, and initial work has been done will be here where discussion is needed with constituents, data domain representatives, Applications, and IR to determine if the work done satisfies the need or if the issue needs to be kicked back to a previous point for more work to be done. An item that meets the approval of the group will be placed in the next column.
VP Approval Column
An item in this column is waiting for final approval by the Domain Owner (typically a VP or member of Senior Management). The Domain Managers appointed by the Domain Owner will present the issue to the Domain owner through their normal reporting mechanism to ensure the correct individuals understand and sign off on governance decisions. Once approved, the data item in question will be moved to production status and implemented.
Historical model for improvements in reporting systems at COS.
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