What is Data Governance?

Data governance comes downs to having clear agreements about who's responsible for your data, how it should be handled, and what standards you'll follow. And yes, you need it—even if you're small, scrappy, and stretched thin.

What Is Data Governance?

Data governance is your framework for managing data as an asset. It answers questions like:

• Who can access sensitive information?

• What happens when someone leaves and takes institutional knowledge with them?

• How do we ensure program data is accurate before reporting to funders?

• Who decides what "program participant" actually means across different initiatives?

Think of it less as bureaucracy and more as the rules of the road that keep everyone safe and moving in the same direction.

Why You Need Data Governance

Trust is your currency. Donors, beneficiaries, and partners trust you with their information and resources. Poor data governance erodes that trust—through data breaches, inaccurate reports, or misuse of personal information.

But beyond risk management, good governance makes your work easier:

Clarity reduces chaos. When everyone knows where data lives, who owns it, and how to use it, you spend less time hunting for information and more time using it.

Quality becomes consistent. Clear standards mean your data is reliable when you need to make decisions or demonstrate impact.

Efficiency improves. You stop duplicating data entry, reconciling conflicting reports, or redefining the same metrics over and over.

Compliance becomes manageable. Whether it's donor privacy, beneficiary protection, or grant requirements, governance makes compliance systematic instead of stressful.

The Core Elements

You don't need a massive framework to start. Focus on these essentials:

1. Data Ownership and Stewardship

Every dataset needs a clear owner—someone accountable for its accuracy, security, and appropriate use. Your development director might own donor data. Your program manager might own participant outcomes. Define these roles explicitly.

2. Access and Security Policies

Who should see what data, and under what circumstances? Not everyone needs access to everything. Create tiered access based on roles, and document how sensitive data should be stored and shared.

3. Data Quality Standards

Define what "good data" looks like. How should dates be formatted? What fields are required? When should data be entered? Simple standards prevent big headaches later.

4. Documentation and Definitions

Create a data dictionary that defines key terms. What counts as a "program participant"? How do you calculate "retention rate"? Shared definitions prevent confusion and ensure consistency across teams.

5. Retention and Deletion Policies

How long should you keep different types of data? When and how should it be deleted? This protects privacy, reduces storage costs, and ensures compliance with regulations.

Where to Start: Practical First Steps

Step 1: Inventory your data. List all the places data lives—spreadsheets, databases, paper files, external platforms. Note what type of data each contains and who currently manages it.

Step 2: Identify your highest risks. Where is sensitive data stored? Who has access? What would happen if that data was lost, leaked, or corrupted? Start with governance for your riskiest data first.

Step 3: Document one clear policy. Don't try to govern everything at once. Pick one area—maybe access controls for beneficiary data, or data quality standards for your CRM—and create a simple, written policy.

Step 4: Assign ownership. Make sure someone is explicitly responsible for each major dataset and for maintaining the policies you create.

Step 5: Communicate and train. Governance only works if people know about it. Share your policies with the team and provide basic training on what's expected.

Step 6: Review and refine. Set a reminder to revisit your governance practices every 6-12 months. What's working? What needs adjustment?

Common Pitfalls to Avoid

Making it too complicated. Start simple. A one-page policy people actually follow beats a 50-page manual that sits in a hidden Google Drive.

Treating it as IT-only. Data governance is an organizational responsibility. Program staff, leadership, and operations all play a role.

Setting policies without input. Talk to the people who actually work with the data daily. Their insights will make your governance practical instead of theoretical.

Forgetting to enforce it. Policies without accountability become suggestions. Build governance into workflows and onboarding processes.

Need Help Building Your Framework?

Data governance isn't about adding red tape—it's about building trust, clarity, and consistency into how you handle information. And in a sector where impact and accountability matter deeply, that's not optional.

We help mission-driven organizations create practical, right-sized data governance frameworks that protect what matters without overwhelming your team. From data audits to policy development to staff training, we'll help you build governance that actually works for your size, capacity, and mission.

Want to Learn More?

Make sure to check out The Data Governance Playbook substack by Charlotte Ledoux! It is a “go-to resource for mastering Data Governance through practical tips, expert insights, and a touch of humour.”