Data Equity Principle 8
Establish inclusive data governance structures and practices with transparent decision-making processes.
Description
Data governance involves developing and monitoring guidance related to how data are collected, stored, analyzed, processed, made available for public use, retired, reviewed, and returned to data providers with action-oriented steps. Inclusive data governance structures involve representation from groups providing data, have a clear purpose, and have transparent, equitable decision-making processes. Effective data governance also requires trust between partners sharing data and shared accountability.
Governing bodies such as committees, boards, or councils determine how decisions about data will be made, who will be responsible for implementing those decisions, and how community input will be incorporated. E-W data systems should establish participatory governance structures that include representation from the affected communities. They should reflect diverse perspectives and can include agency leaders; institutional leaders; and members of the public such as educators, parents, researchers, and/or students. These representatives should take care to ensure all members can participate meaningfully in decision-making—for example, by assigning equal weight to all members’ votes.
Governing bodies are also responsible for establishing data management and data access guidelines that reflect data privacy and security best practices (see Data Equity Principle #2 for more guidance on privacy and security). This could involve weighing the risks and benefits of both restricting and opening access to data. Data access refers to who can securely obtain, view, or use data, and for what purposes. There are legal, practical, and equity considerations for determining data access, which can range across contexts. For example, sharing administrative data with E-W system partners or researchers can increase the risk of a data breach, yet not sharing data can make it more difficult to understand and address a problem of practice, at least without duplicating data collection efforts that burden communities. At a minimum, communities should have access to their own data (abiding with any privacy or confidentiality rules). But access differs from ownership. To shift power dynamics and honor communities’ own goals and visions, communities should be involved in governing the collection, ownership, and use of their data. This is a key principle of Indigenous data sovereignty, for example.1 If applicable, governing bodies should consider secure methods by which they can return data to communities (the data owners) after a project ends to support continued use of their data for other purposes.
In addition to involving communities in governance processes, transparency is also critical for equitable governance. The governing body’s mission and vision, internal roles, and decision-making processes should be well documented and made publicly available. Decisions about data management and data access should also be transparent, using consistent standards or criteria to guide and communicate key decisions. In a similar vein, governing bodies should establish accountability mechanisms by identifying and documenting who (or what organization) is responsible for managing data at various stages of the data life cycle, along with any consequences of breaching confidentiality, violating data-sharing agreements, or otherwise mismanaging data.
Finally, as education technology rapidly evolves, governing bodies must retain flexibility to stay on top of emerging trends and threats to data quality, privacy, and security. For example, rapidly evolving AI technologies and their use in educational settings have implications for students’ data privacy. Governance systems can help develop guidelines for how to use data in AI models to mitigate risks such as using students’ data without their consent. Ways for governing bodies to incorporate diversity of expertise and stay current on emerging topics could include establishing term limits for board positions, staggering terms, and engaging external subject matter experts to advise on evolving topics.
1See this 2018 resolution from the National Congress of American Indians: “Support for U.S. Indigenous Data Sovereignty and Inclusion of Tribes in the Development of Tribal Data Governance Principles”
Inclusive data governance in California
California has developed a transparent, inclusive decision-making and governance structure as it develops a new Cradle-to-Career data system. Members of the public (including policymakers, educators, researchers, and advocates) make up nearly half of the Cradle-to-Career governing board, with the remaining seats reserved for representatives from institutions that provide data to the system, such as the University of California system. The governing board makes decisions by a two-thirds vote, and each member of the board has one vote. The board meets quarterly and all meetings are open to the public, per California’s Bagley-Keene Act. The governing board is responsible for the following:
- Ensuring the Cradle-to-Career data system serves its intended purpose
- Providing operational oversight of the Office
- Overseeing participation in the Cradle-to-Career data system and governance structure
- Recommending the types of information available through the Cradle-to-Career data system
- Recommending improvements to the mechanisms for accessing information in the Cradle-to-Career data system
- Monitoring technical, legal, and data implementation
Applying this Principle
Clarify and document the purpose and guiding principles of the governing body. Assess which groups should be represented on the governing body, based on who provides data and who will use data from the system.
Invite multiple contributing groups to participate in the governance process, including proximate leaders from affected communities, to ensure data collection and use practices honor communities’ goals and visions. Establish clear roles and responsibilities for decision-making, keeping in mind that communities should have the right to govern the collection, ownership, and use of their data.
Convene the governing body to develop clear processes and guidelines for data use. Establish clear processes and protocols for determining which data are collected in the system and how, including linking data from new entities. Communicate key decisions with affected communities and with the public.
Establish clear processes and protocols about data security and access. Train those with access to data on relevant laws and best practices. Practice data minimization; only give users access to only the minimally necessary data elements and data sets.
Be On The Lookout
Data-sharing between organizations and linking in longitudinal data systems can give users access to important data elements needed to assess and address disparities, however, it comes with risks. Any time data are shared, users must follow data governance policies by establishing a memorandum of understanding or data-sharing agreement and reviewing any consent documentation to ensure data-sharing is permissible. Communities (such as students and educators) should understand the value of providing their data and be able to use it for their own benefit (for example, to make decisions about their educational options). Misuse of data or security breaches can erode trust and public confidence or damage relationships between agencies, which can cause setbacks for data system improvement efforts and have negative long-term consequences.
Additional Resources
- Guide for Sustaining P-20W+ Data Governance. This guide includes tools, templates, and checklists for establishing a sustainable P-20W+ data governance infrastructure, an essential component in ensuring that state longitudinal data systems meets partners' information needs.
- Roadmap for Cross-Agency Data Governance. This Data Quality Campaign road map offers key considerations and a road map for states to establish and implement effective data governance across agencies.
- The Art of the Possible: Data Governance Lessons Learned from Kentucky, Maryland, and Washington. This Data Quality Campaign paper highlights how three states have broken down siloes and improved relationships across state agencies.
- Indigenous Data Governance: Strategies from United States Native Nations. This journal article explains the concepts of Indigenous data sovereignty and governance, and describes the value and challenges of shifting authority over Indigenous data to Indigenous peoples. The article includes Tribal case studies and discusses relevant federal laws and Tribal organizations.
- Data Governance Overview. This resource from the Institute of Education Sciences defines data governance and offers recommendations for structural components and values of effective data governance.
- Forum Guide to Data Governance. This manual from the National Forum on Education Statistics presents information on the value of data governance programs to state education agencies and provides best practices and examples for agencies looking to implement new data governance programs.
References
The framework's recommendations are based on syntheses of existing research. Please see the framework report for a list of works cited.