Introduction

When discussing data, almost, if not all, industries have data as a vital asset for organizations. This is evident in food security, for instance, FAO, FEWS NET, and other NGOs working in humanitarian assistance.

Managing data is crucial for making informed decisions about food distribution, crop production, and disaster preparedness. This is however only true with proper governance, without which the value of data diminishes and risks such as inaccurate forecasts, non-compliance, and inefficiency increase.

This week, we are exploring how to effectively build a data governance framework, and what better sector to explore than food security and humanitarian assistance?

Source: International relations

Definition: A data governance framework tailored for humanitarian assistance organizations provides a structured approach to managing data assets effectively. We will explore how to build a comprehensive data governance framework step by step, ensuring it aligns with organizational goals and industry best practices.

Key benefits of a data governance framework for humanitarian assistance organizations include:

• Improved accuracy of food and disaster-related data.
• Enhanced compliance with reporting standards and donor requirements.
• Better decision-making based on trusted data.
• Increased efficiency in data collection, management, and reporting.
• Mitigated risks associated with data inaccuracies and mismanagement.

Understanding the Fundamentals of Data Governance

Definition: Data governance is the collection of processes, policies, standards, and roles that ensure data is managed and used effectively, securely, and consistently across an organization.

Key Components of Data Governance for Humanitarian Assistance Organizations:

• Data Quality: Ensuring accuracy, consistency, and timeliness of food distribution, disaster response, and beneficiary data.
• Data Privacy and Security: Protecting sensitive data such as beneficiary records, donor information, and field reports.
• Data Stewardship: Assigning responsibility for managing datasets related to resource allocation, disaster tracking, and impact assessments.
• Data Lifecycle Management: Managing data from collection (e.g., field surveys, disaster reports) to archival or deletion.

Stakeholders Involved: Successful data governance requires collaboration among:

• Data Owners: Project managers in organizations like FAO and WFP, as well as field coordinators in humanitarian NGOs.
• Data Stewards: Individuals responsible for day-to-day data quality monitoring and enforcement of standards.
• Executives: Decision-makers within NGOs, government agencies, or international organizations.
• IT and Data Teams: Teams managing data systems, platforms, and integrations.

Building the Data Governance Framework.

Photo by Google DeepMind on Unsplash

Step 1: Define Your Data Governance Goals and Objectives

Start by identifying why data governance is essential for your organization and what you aim to achieve. Your goals should align with broader objectives, such as improving humanitarian response and resource management.

Examples of Data Governance Goals

• Enhance the quality and consistency of disaster response and beneficiary data.
• Ensure compliance with international reporting standards, such as those set by FEWSNET, FAO, and WFP.
• Create a centralized repository for data collected from field surveys, disaster reports, and donor submissions.
• Reduce inefficiencies caused by fragmented data collection and storage practices.

Step 2: Assemble a Data Governance Team

A dedicated team ensures the success of your framework. This team will develop policies, oversee implementation, and ensure ongoing compliance.

Key Roles and Responsibilities:

• Executive Sponsor: A senior leader within the NGO or partner organization (e.g., FEWSNET, FAO, or WFP) who champions the initiative.
• Data Governance Council: A cross-functional group that makes strategic decisions and sets priorities.
• Data Stewards: Manage operational aspects, such as monitoring the quality of beneficiary data or disaster response reports.
• IT and Data Teams: Implement technical solutions and provide ongoing support for tools and systems.

The framework should encourage cross-departmental collaboration to address diverse needs and perspectives, particularly between technical teams and field staff.

Step 3: Conduct a Data Assessment

Before designing your framework, understand the current state of your data landscape. This includes identifying strengths, weaknesses, and opportunities for improvement.

Steps for Data Assessment:

• Identify Data Sources: Catalog data sources, including disaster response reports, beneficiary surveys, market price datasets, and donor submissions.
• Evaluate Data Quality: Assess the accuracy, completeness, consistency, and timeliness of critical datasets.
• Map Data Flows: Document how data moves across systems, from field collection tools to centralized databases used by organizations like FAO or FEWS NET.
• Identify Gaps: Highlight areas with missing or unreliable data, such as incomplete beneficiary records or outdated disaster response plans.

This assessment will serve as a baseline for measuring progress.

Step 4: Develop Policies and Standards

The policies and standards you develop will form the backbone of your framework, guiding how data is managed and used.

Key Policy Areas:

• Data Access: Define who can access sensitive data, such as beneficiary records or donor reports, and under what conditions.
• Data Usage: Establish guidelines for ethical and compliant use of data.
• Data Security: Specify measures to protect sensitive information from breaches or unauthorized access.
• Data Retention: Set rules for how long data should be kept, particularly donor-specific or project-specific data.

Standards:

• Create standard naming conventions and data definitions for disaster types, geographic regions, and beneficiary categories.
• Define metadata requirements to document data assets.
• Ensure policies are actionable and enforceable.

Step 5: Implement Technology and Tools

Technology plays a crucial role in supporting data governance initiatives. Select tools that align with your organization’s needs and scale.

Essential Tools:

• Data Catalogs: Centralize metadata for disaster response reports or field survey data.
• Data Quality Tools: Automate quality monitoring and flagging anomalies in critical datasets like food distribution records.
• Metadata Management Tools: Track information about data, such as collection methods and limitations.
• Data Security Solutions: Protect sensitive information, particularly beneficiary-level data collected during field operations.

Integrate these tools into existing IT infrastructure used by your organizations to ensure interoperability.

Step 6: Establish Data Governance Processes

Clearly defined processes are necessary to operationalize your framework and ensure consistency.

Photo by Nikolas Noonan on Unsplash

Key Processes:

• Data Change Management: Establish workflows for approving changes to datasets, such as new data collection methods.
• Issue Escalation and Resolution: Define steps for reporting and addressing data accuracy or consistency issues.
• Data Quality Checks: Implement routine checks to ensure high standards for key datasets.

Document all these processes to ensure clarity and repeatability.

Step 7: Provide Training and Awareness

A successful data governance framework depends on the buy-in and understanding of all stakeholders. Provide training to ensure everyone understands their roles and responsibilities.

Training Focus Areas:

• Importance of data governance and its impact on humanitarian outcomes.
• How to comply with data policies and standards.
• Role-specific responsibilities, such as data entry protocols for field officers.

Promote a culture of accountability and continuous improvement across organizations.

Step 8: Monitor and Measure Success

Regular monitoring and evaluation ensure your framework remains effective and adapts to evolving needs.

Define Metrics:

• Data quality scores (e.g., accuracy, completeness).
• Compliance rates with data policies and donor reporting requirements.
• Number of resolved data issues.
• Efficiency gains in data management processes.

Continuous Improvement:

• Conduct regular reviews, data quality checks, and audits.
• Update policies and processes based on feedback and changing requirements.

Challenges and Best Practices

Challenges:

• Resistance to change from staff unfamiliar with governance frameworks.
• Resource constraints, such as limited budgets or staff capacity.
• Balancing flexibility with control when dealing with diverse data sources.

Best Practices:

• Secure executive sponsorship to drive buy-in.
• Start small with a pilot program and scale gradually.
• Communicate the benefits of data governance to all stakeholders, including donors.
• Leverage industry standards and frameworks for guidance.
• Foster a culture of collaboration and accountability across organizations and regions.

Conclusion

Building a data governance framework is a strategic investment that improves decision-making, reduced risks, and enhanced organizational efficiency. For humanitarian assistance organizations such as FAO and FEWS NET, WFP data governance directly impacts their ability to address global challenges like hunger, disaster response, and resilience-building.