Data Governance Standards — DVSupport.Network
High-level data governance and privacy principles for organizations integrating with DVSupport.Network.
Data Governance Standards
Purpose and Scope
These data governance standards provide a shared framework for how participating organizations collect, maintain, use, and share information within coordinated domestic violence response networks. The focus is on operational alignment across agencies rather than legal compliance standards, which remain the responsibility of each organization’s leadership and counsel.
The standards apply to:
- Client- and case-related data used for coordination across agencies
- Program, service, and capacity data used for planning and referrals
- Aggregate data used for monitoring, evaluation, and reporting
- Internal operational data that influences inter-agency coordination
Core Principles of Data Governance
Participating organizations are encouraged to align their data-related practices with the following principles:
- Purpose limitation: Collect and use data only for clearly defined operational and coordination purposes.
- Proportionality: Limit data collection to what is operationally necessary for service delivery and coordination.
- Quality and accuracy: Maintain data that is as complete, accurate, and current as reasonably achievable.
- Confidentiality by design: Integrate privacy and confidentiality safeguards into processes and systems from the outset.
- Accountability: Establish internal roles and review mechanisms for data-related decisions and practices.
- Transparency to partners: Clearly describe how data is collected, shared, and managed within inter-agency agreements.
Data Accuracy Requirements
Accuracy standards support reliable coordination, appropriate referrals, and credible reporting. At a minimum, organizations can define and document:
- Authoritative sources: Identify which system or dataset is considered the “source of truth” for each key data element (e.g., program capacity, service availability, contact roles, inter-agency MOU status).
- Validation rules: Apply basic validation (formats, required fields, standard value sets) to reduce incomplete or inconsistent records.
- Verification practices: Use standardized procedures for verifying identity and key attributes before creating or updating sensitive records, consistent with internal policy.
- Change tracking: Log material changes (e.g., case status, program capacity, referral eligibility) with timestamps and responsible user identifiers.
- Reconciliation cycles: Conduct periodic reconciliation between systems (for example, referral platform vs. case management system) to identify mismatched or outdated entries.
Privacy and Confidentiality Principles
Given the sensitivity of domestic violence-related information, privacy and confidentiality are central operational requirements. While specific legal obligations vary by jurisdiction and funding stream, organizations can align on the following high-level principles:
- Minimum necessary data: Share only the information required to coordinate services or fulfill a defined operational function.
- Role-based disclosure: Limit access to sensitive data to personnel whose roles require it for defined tasks.
- Contextual consent practices: Where applicable under internal policy and law, obtain and record consent for data uses and disclosures that go beyond core service delivery or are required in inter-agency coordination.
- Segmentation of sensitive fields: Isolate particularly sensitive categories of data (e.g., detailed incident descriptions, addresses, legal strategy notes) in systems with more restrictive access where feasible.
- Confidential communication channels: Use designated secure channels for inter-agency transmission of any data that can reasonably be linked to individual cases or staff safety concerns.
- Non-disclosure by default: Treat unapproved release of sensitive information as a policy deviation that triggers review and remediation.
Inter-agency memoranda of understanding (MOUs) or data-sharing agreements can describe in clear, operational terms:
- What categories of data may be exchanged
- Permitted purposes for using shared data
- High-level confidentiality expectations and escalation paths for concerns
Update Frequency Expectations
Timely updates are essential for accurate referrals, capacity planning, and risk-aware coordination. Networks can define tiered expectations by data type:
- Real-time or same-day updates:
- Critical status changes (e.g., service availability, program closure, major eligibility changes)
- Corrections to significant data inaccuracies impacting current coordination
- Weekly or bi-weekly updates:
- Program capacity indicators (beds, appointment slots, legal clinic availability)
- Key contact information for coordination leads and on-call roles
- Changes to operational hours or referral pathways impacting current cases
- Monthly or quarterly updates:
- Service menus and eligibility criteria
- Organizational policies that affect coordination workflows
- Aggregate program metrics used in joint reporting
Each participating organization can document internal procedures that ensure:
- Designated staff roles for updating specified datasets
- Clearly defined cut-off times for data relevant to shared reports
- Notification protocols when updates will be delayed or incomplete
Organizational Roles and Responsibilities
Clear assignment of responsibility supports accountability and predictable coordination. Example role allocations include:
- Data Governance Lead (or equivalent):
- Oversees internal adherence to shared data governance standards
- Serves as primary liaison with partner agencies on data governance issues
- Coordinates periodic reviews and updates of internal procedures
- Program Data Stewards:
- Maintain accuracy of program-specific information (capacity, eligibility, services)
- Validate data quality before submission to multi-agency systems or reports
- Implement data correction workflows for their program areas
- IT / Systems Administrators:
- Configure access controls, logging, and backup practices in line with governance standards
- Support secure data transfer methods between organizations and systems
- Frontline and Supervisory Staff:
- Enter and maintain accurate data within defined timelines
- Flag data anomalies and potential confidentiality risks through internal channels
- Follow documented protocols for information sharing with partner agencies
High-Level Security Expectations
Security practices help protect data from unauthorized access, alteration, or loss. While specific technical measures will vary, participating organizations can align around these high-level expectations, recognizing that they do not constitute legal or technical advice:
- Access control: Implement user authentication and role-based access to systems containing sensitive or personally identifiable data.
- Secure transmission: Use secure, encrypted channels where feasible for transmitting sensitive information between agencies and systems.
- Logging and monitoring: Maintain audit trails of access to sensitive systems and review logs for unusual or unauthorized activity according to internal policy.
- Device and storage protections: Apply reasonable protections for devices and storage media that hold operational or case-related data (e.g., passwords, restricted physical access, secure disposal methods).
- Backup and continuity: Maintain backups and continuity plans so that essential operational data can be restored in the event of system failure or incident.
- Incident response processes: Establish internal procedures for responding to suspected data incidents, including steps for internal escalation and partner notification where appropriate.
Coordination committees can periodically review whether security expectations remain proportionate to the sensitivity of data being exchanged and the resources of participating organizations.
Data Retention Practices
Retention practices should balance operational utility, legal and funder requirements (as determined by each organization), and confidentiality risks. Without prescribing specific timelines, networks can align on the following practices:
- Retention categories: Classify data into categories (e.g., case records, referral logs, aggregate reports, audit logs) with defined retention ranges consistent with internal policies.
- Purpose review: Periodically review retained data to confirm that ongoing retention aligns with current operational needs.
- Secure disposition: When data reaches the end of its retention period, ensure it is removed or destroyed using methods appropriate to its sensitivity and storage medium.
- Inter-agency alignment: Where data is shared across agencies, clarify in MOUs which party is responsible for long-term retention and how each will manage deletion or archival.
- Special categories: Consider whether certain categories of highly sensitive data warrant shorter retention or additional safeguards, subject to legal and contractual requirements.
Data Correction and Quality Improvement
Structured correction and quality improvement processes support reliable coordination over time. Recommended components include:
- Correction channels: Define how staff and partner agencies can report suspected inaccuracies (e.g., outdated contact information, misclassified program capacity, duplicate records).
- Verification and approval: Establish internal review steps for verifying correction requests, especially where changes relate to sensitive or high-impact data elements.
- Timeframes: Set reasonable, documented target timeframes for addressing different types of corrections (e.g., urgent corrections within two business days; non-urgent within an agreed period).
- Feedback loops: Share recurring data quality issues with relevant supervisors and governance bodies to inform training, process refinement, or tooling improvements.
- Documentation: Record significant corrections, including who initiated the change, who approved it, and when it was implemented.
Inter-Agency Governance and Review
To keep standards practical and current, multi-agency networks can:
- Designate a data governance or information management subcommittee
- Review these standards on a recurring schedule (e.g., annually or bi-annually)
- Collect structured feedback from participating organizations on implementation challenges
- Document agreed updates and communicate changes through established coordination channels
This approach supports consistent, predictable practices across organizations while allowing flexibility to account for varying capacities, systems, and regulatory environments.