Accurate, timely and relevant information is crucial to making decisions about granting. Grant managers and their teams collect, analyze and share troves of information to maximize the potential impact of philanthropic funds. Feedback¹ from grantees and partners, as well as that from the people most affected by grantmaking decisions, are critical sources of information, with grant administrators increasingly recognizing that feedback helps make grantmaking more effective and equitable. As the preeminent organization in the space, Feedback Labs emphasizes a feedback loop, or a continual cycle that requires input from both service users and providers – at its core, feedback is a two-way process with several intentional steps necessary to fully realize its value.² 

Feedback is often collected across different channels and can come in difficult-to-analyze text. This can lead to disarray, eventually making the feedback data difficult, costly and time-consuming to process and use, which can make closing the feedback loop unfeasible. So how can grantmakers and their grantees work together to manage feedback and leverage the benefits of it? We argue that grantmakers can maximize the potential of feedback (and avoid being overwhelmed by it) by treating feedback as data. 

What do we mean by ‘feedback as data’?

Data is any statistics, facts or ideas that can be managed and analyzed to inform discussion, decision-making, or other deliberative activities initiated by an organization. A successful feedback process generates large quantities of this potential data, typically falling into one of three categories:

1. Participant responses: These are collected through forms and surveys done at different points of interaction between stakeholders, like events and workshops. Information might be collected by implementing partners or directly from clients served. Some organizations might use online and digital tools like SurveyMonkey or Google Forms, while others rely on paper forms or even informal conversations captured through audio recordings or as case manager notes.

2. Information about participants and implementing partners: Demographic data is increasingly important for funders seeking to ameliorate systemic disparities between different groups, advance principles of equity and justice and remedy entrenched power imbalances. 

3. Metadata: Metadata are data that describe the characteristics of an associated dataset. In other words, data about data. Metadata allows users to understand how a given dataset can be used and interpreted, and the kinds of questions that can be answered with the data. Metadata allows a data user to understand attributes of a dataset, such as data collection methodology, contact information for the data provider, intended uses of the dataset, software requirements for data processing and/or limitations of the dataset (e.g. possible measurement error). 

In short, different feedback processes collect extremely valuable information that can be used as data to inform our decision-making 

The starting point: Data governance for feedback

Data is constructed, it doesn’t exist on its own outside the systems that created it. On its own, data is inert and has nothing but potential value, which may only be achieved through decisions around how it is managed. However, data is not neutral and possessing it is a form of power. It is the product of decisions made by different actors who hold different values and perspectives. When thinking of feedback as data, we must recognize the decisions and dynamics around the way data is governed and the different actors involved in this decision-making process. 

For example, in order to understand the impact and reach of their investments, a funder may decide to collect key demographic information such as race, gender, socio-economic status and other identifiers about individuals impacted by a given project. Another funder might choose not to collect this information, applying a broad approach to different populations. Feedback data is no different – who participates in a feedback loop, and how the feedback is collected, analyzed and shared are all consequences of a structured (or sometimes unstructured) decision-making process. These decisions about what data to collect influence the broader narrative and perception of the challenges that are being addressed. In other words, decisions to not collect demographic information can inform a narrative to suggest that a challenge is not disproportionately impacting a particular population, simply because the data is not collected. 

A data governance approach provides a perspective and guidance for this decision-making process. It helps identify necessary and missing stakeholders, it leads to a more transparent process and it solidifies a systematic approach to data agreed upon by all those involved in the process.  Decisions made around data and technology throughout a data lifecycle (from planning to implementation and long-term maintenance) will determine whether they are useful and whether they support stakeholder’s needs and objectives.   

The bottom line: Feedback is a journey and data governance is your compass 

Feedback is a journey. If a grantmaker or philanthropic organization decides to engage in an honest and meaningful feedback process, then they are expected to engage different actors, with different approaches and different experiences in pursuit of systems change. This means the process will have a myriad of opinions and ideas shared throughout. Similarly, the way the feedback data is collected, used, shared and analyzed during the process needs to be adaptable, transparent and inclusive to maximize its potential.  

We propose processes that purposefully talk and walk hand-in-hand with all stakeholders involved in feedback. We then co-create and collaborate to develop a feedback loop that lays out a path forward to creating a better, more inclusive, and more sustainable future for the communities and the people we serve. A data governance model supports the loop, grounding the feedback data through tools, guides and use cases in a way that is transparent, structured and inclusive. Our proposed approach is informed by 4 high-level values:

1. Feedback data must be centred on target population needs for effective data governance: we need to consider both the needs of those engaging with the data, as well as those the data is referring to and affecting the most. Vulnerable populations in particular, including individuals with intersecting identities such as women in visible minority communities, often remain uncounted in data and are underserved.

2. A holistic approach to identifying the necessary conditions for a successful feedback data process is necessary: Ecosystems around philanthropic initiatives have existing norms and practices around collaboration, integration, and data management. Feedback data work should adapt to the digital capacity, practices and culture that currently exists and that the stakeholders require to sustain a successful feedback data process.
3. Human-centred, participatory design improves quality, cooperation and buy-in: An external perspective from organizations like Open North is valuable in facilitating dialogue and maintaining neutrality while assessing data norms and collaboration practices, but any sustained ecosystem change must be informed by those who are most impacted. Facilitating meaningful stakeholder participation either directly or through representatives and advocates ensures the needs and aspirations of stakeholders form the basis of all recommendations regarding feedback data. 
4. Policy should be fuelled by data-informed decisions that are effective, responsible and collaborative: The decisions that people will take to shape policy must be data-informed, and the data used must be relevant, accurate and actionable. 

In short, a data governance approach to feedback has the potential to break down the dichotomy between trust-based philanthropy and data-driven decision-making. Laura Steele has brilliantly articulated how both processes can be interwoven together through a data justice approach. Steele points out that community members’ perspectives collected through feedback and consultation, alongside “hard data” will lead to more accurate information that is a better reflection of the lived experiences of clients served. Data governance for feedback data is an immediate and practical way to do this.

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1. Feedback, also referred to as a feedback loop, is a “two-way stream of communication between someone who designs a program or service and someone who uses that program or service”. 
   Feedback Labs. “What is Feedback?” Accessed May 29, 2024. https://feedbacklabs.org/about-us/what-is-feedback/ ↩︎
2. Feedback Labs has multiple resources available for those interested in improving or introducing feedback loops into their work, including an introductory Feedback 101 guide ↩︎