How to set up a data governance framework for your NGO’s indicators

Data governance is the system of rules, roles, and processes that define how data is collected, stored, protected, and used. It is essential because it acts as the foundation for your indicators ensuring they are accurate, ethical and usable. Setting up a governance framework is not simply a matter of compliance, it is the way to ensure that your work will be able to inform decision-making.

In this article we look at:

• Why data governance?
• What is included in a data governance framework?
• Five steps to get started with building a governance framework
• Managing challenges
• Data governance with ActivityInfo

Why data governance?

A governance framework can help your NGO respond to questions related to:

• Ownership of data for each indicator
• Responsibility roles for quality control
• Discrepancies in indicator definitions across projects
• Beneficiary data protection, and many more

Without it, your organization might face challenges such as data loss, contradicting reports, and low data quality. For example, an education program might count as a ‘trained instructor’ anyone who attended a workshop, whereas a livelihoods program might count only certified participants.

With a governance framework you can set consistent indicator definitions, assign clear data ownership and responsibilities, establish quality assurance and standards for documentation and set rules for data access and privacy.

What is included in a data governance framework?

Roles and responsibilities

It is useful to start with the question of ‘who does what’. Setting roles in relation to the data is useful. For example:

• Data owners: Program managers who are accountable for the accuracy of their program’s indicator data.
• Data stewards: M&E staff who ensure data quality and manage documentation.
• Data custodians: IT or database teams responsible for system security and storage.
• Data users: Program and management teams who analyze and interpret the data.

The RACI matrix (Responsible, Accountable, Consulted, Informed) can help map these responsibilities for each indicator or data process.

Data standards and indicator definitions

To improve consistency create an indicator dictionary or metadata sheet, and treat it as a living document if there are changes in priorities. Take a look at our article “A quick guide on indicator metadata’ for some inspiration. In summary, this document can include:

• Indicator name and type (output, outcome, impact)
• Definition and unit of measure
• Formula for calculation
• Disaggregation categories
• Data source and frequency
• Responsible staff

Data quality rules

Another aspect that should be covered is ensuring that you are collecting high quality indicators that are accurate, consistent, complete and timely. In platforms such as ActivityInfo, you can embed the validation checks in the data collection forms so as to automatically validate data added or imported to the system. Data quality assessments can also support you in this direction. You can also ensure there is a standardized process for data quality review on a monthly or quarterly basis.

Data security and data privacy

There are various best practices for ensuring data security and privacy that your organization should follow. Ensure there is a formal approach and that all stakeholders involved are trained.

Develop a simple data protection policy defining who can access raw data and under what conditions. Train staff on anonymization and ethical data handling. In ActivityInfo make sure to use refined permissions to restrict access by role.

Take a look at our Webinars for more information on data security and data privacy.

Data lifecycle management

A governance framework should cover what should happen to data in every step of the data lifecycle.

• Collection: who collects and verifies the data.
• Processing: how it’s cleaned and compiled.
• Storage: how data is stored.
• Analysis: when and how results are produced.
• Sharing: who receives reports and in what format.
• Archiving: how old data is stored or retired.

Five steps to get started with building a governance framework

1. Assess your current situation

Do an internal audit of your data practices: where is data stored, how consistent are indicator definitions, who checks quality? Identify “pain points” as these will help shape your governance priorities.

2. Form a governance team

Include representatives from M&E, programs, IT, and leadership. Assign a data governance focal point who champions the process.

3. Draft simple policies and procedures

Start with short, actionable documents such as a data ownership matrix, a data quality protocol, a short privacy and access policy.

4. Integrate governance into existing tools

If you aren’t already using a platform such as ActivityInfo that has built-in tools to support your governance framework, make sure to adjust your tools accordingly. For example, collect information about the data source and the responsible person, store data version and track changes, and include validation checks.
4. Train and communicate
Run short, focused sessions for field staff on data entry and validation. For managers, emphasize how governance improves reporting credibility and efficiency.

5. Review and adapt

Plan review meetings on an annual or quarterly basis to go over what works and what doesn’t work in your governance framework. Encourage staff to provide feedback and create a culture where governance has the role of the enabler. Update policies based on what you learn and accommodate the needs of new projects.

Managing challenges

Limited staff or resources
If your M&E team is small, and everyone already juggles reporting, data cleaning, and field supervision, governance can feel like an extra burden.

If this is the case, start small. Pilot governance on a few high-priority indicators or one project. Automate basic validation rules and ownership tracking. Focus first on processes that save time (e.g., preventing rework or lost data). Gradually scale as staff gain familiarity.

Staff turnover
When an M&E officer leaves, indicator definitions, data sources, and file locations might be lost if they aren’t documented properly. As a result, the new colleague might spend considerable time trying to rebuild the logic.

To avoid this scenario, document key data processes and indicator definitions in a shared online folder or platform like ActivityInfo. Keep a short “data handover checklist.” Build institutional memory through a centralized indicator dictionary and consistent naming conventions in your databases.

Donor-specific requirements
Different donors might want similar indicators reported in different ways. For example, one by household, another by person, a third by percentage. So the team ends up maintaining multiple spreadsheets, and alignment is a nightmare.

To tackle this challenge, develop a single NGO-wide data governance standard that defines your “core” indicators and formats. Then, map donor requirements to your internal structure instead of reinventing new datasets. Use tools (like ActivityInfo’s multi-form hierarchy) to aggregate once and export differently for each donor.

Resistance to change
Sometimes, field and program staff might see governance rules as extra work or bureaucracy. They might skip data validation steps or use old forms “because it’s faster.”

In this case, it might help to frame governance as a way to make their work easier. Fewer data errors mean fewer rechecks and last-minute donor queries. Share quick wins such as reporting time reduced and involve teams in designing policies so they feel ownership rather than compliance pressure.

Overlapping or unclear responsibilities
M&E staff assume the program manager reviewed the data, while the manager assumes the M&E team did. Errors slip through unnoticed.

Here, you can create a simple RACI matrix (Responsible, Accountable, Consulted, Informed) for each indicator or process. Integrate role assignments directly into tools (e.g., assign “data owner” via user fields in ActivityInfo). Clarify who signs off on each step of the data lifecycle.

Fragmented systems and data silos
Different departments use different tools such Excel, Google Sheets, and online databases that don’t “talk” to each other. This leaves you with data scattered across multiple systems.

To start, map all existing data sources and identify overlaps. Where possible, centralize into one platform or at least harmonize naming and indicator IDs. Use unique identifiers for indicators and beneficiaries to ensure consistency across systems. ActivityInfo is an ideal platform to tackle siloed data thanks to its relational database model.

Data governance with ActivityInfo

In ActivityInfo, a wide range of functionalities support data governance. Here are some of them:

• Defined user roles and permissions allow you to control who can view, edit, or approve data, reinforcing accountability and data ownership.
• Standardized form design, validation rules, and mandatory fields ensure indicator data is collected consistently and meets agreed definitions.
• The platform’s automatic audit trail and record history add transparency as every change is logged and recoverable.
• ActivityInfo’s hierarchical database structure helps align indicator data across projects and partners, while metadata fields let you document data sources and verification notes.
• Approval workflows with Reviewer’s fields introduce a layer of internal quality control before data becomes official.
• Finally, centralized dashboards make it easy to track completeness and timeliness in real time, transforming governance from a policy into an everyday practice.

Would you like to learn more about ActivityInfo and how it can support your work? You contact us to arrange a demo tailored to your needs.