Data Champion Guiding Principles

At its core, we believe evaluation is a learning process which should be used to help guide our programs and services. Because of this, we believe it is critical for our organizations to focus on data needed to develop and refine our program models, assess progress, and guide ongoing programmatic improvements all while centering equity. Our first responsibility must be to our priority population , as we seek to do better for those we serve.

In practice this may look like:

1. Focus on data that can be used to guide program implementation and improvement. Gain clarity around why certain data is collected. Does the data help us to better understand what is working and what’s not regarding our programs?


2. Collect and utilize both qualitative and quantitative sources of data and recognize that various programs and contexts will require different data needs.


3. Prioritize regularly collecting data from the priority population; ensuring constituent voices are heard is critical to better understanding client outcomes and program benefits. Seek out opportunities to involve the priority population in the development of data collection tools, including survey and interview questions, etc., along with data analysis and interpretation and adequately compensate them for their input and efforts.


4. Provide front-line staff and managers with opportunities and tools (data placemats, etc.) to incorporate data review and learning in real-time to ensure equitable decisions are being made.


5. Share the data back with our clients and constituents, and the actions we intend to take because of their feedback.


6. Include supporting documentation, such as a data dictionary or explanation of feedback process, so that the evaluation strategy lives beyond any one person and can continue to be enhanced.

Creating and sustaining an inclusive, participant-driven evaluation practice takes time, effort, and intentionality. This can be perceived as more challenging and costly than traditional evaluation practices, however, the long-term benefit of building trust with your priority population and creating programs that have lasting, meaningful impact far outweigh the short-term costs of time, effort, and money.

In practice this may look like:

1. Center the priority population in all stages of the process and be transparent about the impact of historical and systemic oppression as major barriers to communities thriving.


2. Intentionally learn from the priority population about the changes they would like to see and invite them to share how they think it can be achieved. Have them teach us their barriers and strengths and compensate them adequately for their time and effort.


3. Center the community as experts in their own experience, rather than the evaluators as experts in measuring impact. Work to understand our own limitations and seek to increase our cultural competency to support our priority populations.


4. Strive for frequent, clear, and transparent communication with organizational leadership and stakeholders where we are:
   1. Centering the voice and experience of the priority population;
   2. Centering the voice and experience of the priority population;
   3. Highlighting the strengths and assets of this group; and
   4. Contextualizing findings that show “no or low” outcomes through the lens of historical and systemic oppression.

Data & evaluation work makes our programs better; and by better we mean more meaningful, relevant, and impactful for the priority populations we serve. This work is often siloed in one department or with one individual seen as the ‘expert’ or becomes an afterthought for meeting reporting and monitoring requirements. Data & learning champions know that evaluation serves many purposes and that at its core, evaluation is a way to understand what whether programs are having the intended impact on the people your programs serve. Evaluation work is everyone’s work because the information gathered should inform practices across the organization to better achieve equitable outcomes for staff involvement and in service to the priority population.

In practice this may look like:

1. Create ongoing opportunities to share data with all members of the organization (ex: staff meetings, lunch and learns, office hours).


2. Incorporate feedback loops to ensure program-level, front line staff and priority populations can provide input on programmatic decisions that are informed by data.


3. Support organizational leadership normalizing the practice of regularly talking about data through an equity framework, both within and outside of the organization. Support talking about data that highlights both successes and growth opportunities for the organization


4. Support practices that foster a culture of learning throughout the organization, not just in data and evaluation program work. This includes marketing and communications, talent acquisition and management, performance evaluation, and finance, operations, and development.

A tension exists in data & evaluation work between consistency of implementation (rigor & fidelity) and being culturally responsive as a pathway to equitable delivery. Best practices are widely understood to be normalized as the right way to do things; however, we know that believing there is only one right way to do things is a characteristic white supremacy culture . Normalizing transparency around the tension and naming biases and inequitable practices when they come up can serve as an antidote and support organizations navigating this tension in the work.

In practice this may look like:

1. Learn and share best practices related to data and evaluation with team members, managers, leadership, the board, etc. Create space for the priority population who is most affected by the evaluation to participate in these learning opportunities.


2. Work to gain clarity regarding roles, responsibility and ownership of data and evaluation systems; everyone involved in projects should be clear about the ways they can contribute.


3. Provide opportunities for staff at all levels to develop the necessary knowledge, ability and skills related to collecting, analyzing, and utilizing data for learning and improvement.


4. Develop a process to consider evaluation findings from diverse perspectives, including perspectives from individuals in the priority population.


5. Discuss with the priority population their views of the evaluation, and whether it is accurate and consistent with the team’s observations and strategies. Create a feedback loop to share the data back with clients and constituents, and the actions we intend to take because of their feedback.


6. Prioritize learning about the priority population from community leaders, members of the community, other culturally competent evaluators with experience in the community, and assess how the specific qualities of this population impact how the evaluation should be conducted.

While data and learning champions come from a wide variety of backgrounds, training, and lived experiences, one thing they have in common is the propensity to ask curious, critical questions about the data and evaluation process within their organization. Data champions confront their biases to better support a culture of responsible, ethical data practices that elevate the needs of the priority population.

In practice this may look like:

1. Create opportunities to have meaningful conversations around data and evaluation across all levels of the organization. This work should not be done in a silo.


2. Contribute to a culture of learning by asking questions and identifying areas for improvement in the data and evaluation process. Acknowledge that sometimes a data-related meeting will raise more questions than it answers… and that’s ok!


3. Normalize asking questions throughout the program development process to strengthen and align evaluation strategies with intended program impact; think about learning questions during the initial stages of program development and align them with program objectives.


4. Ask questions related to data ethics, data sharing, and cultural competence, and talk about assumptions related to cultural competency, bias, and race equity.

Example Questions to Ask about the Data & Evaluation Process

• Have we clearly defined our priority population?
• What is our theory of change – what do we know about how our program will make an impact?
• What does the research literature say about our program model, and what bias and assumptions can we identify?
• Is the program being implemented as designed? If not, do we know why?
• Does our understanding of client success align with that of our priority population?
• Do we have the appropriate policies and procedures to guard client data and ensure compliance with state, federal and local policies?
• Are all staff aware of these policies, and is there space for continued education and support?
• Do we have appropriate data sharing agreements in place with other nonprofit and/or public sector entities when needed?
• Have we selected or created data collection tools that support equitable evaluation practices (reduction of biased language, rooted in priority population culture, etc.)?
• How are we ensuring that our clients understand their rights related to the collection and use of their data?
• How are we including client voice and feedback regularly and incorporating it into our program model, and are they being adequately compensated for their time? How are these changes being communicated back to clients?

For data and learning champions to serve as effective leaders, we must continue to build our own skills and abilities and invest in our continued learning and the learning of those around us doing the work. We must be open to learning and unlearning alike and understand that data & evaluation work that centers community voice and equitable practices is a journey; there is no handbook for how to do this ‘correctly’. Being open to learning and sharing your successes and challenges is a key part of building a learning culture, for yourself and your organization.

In practice this may look like:

1. Incorporate ongoing assessment of our individual strengths, weaknesses, and leadership styles to ensure we’re constantly checking our own power and privilege in our data and evaluation work.


2. Find ways to bring professional development opportunities for data and evaluation to all key stakeholders in this work (clients and priority population, direct front line staff, agency leadership and board).


3. Regularly seek out meaningful input from program staff and the clients we work with to ensure deep understanding of how program implementation is actually working on the ground; commit to use the findings we learn to improve the program and experience.

The evaluation sector is changing, and equitable evaluation practices that center community voice are the goal. Data and Learning Champions are change agents across all levels of the organization. We need each other to challenge and strengthen our practices and ourselves.

In practice this may look like:

1. Encourage peers and colleagues to think deeply about data & evaluation through an equity lens, and its impact on program design and implementation. We know that changing organizational culture is a slow process that takes incremental, steady work. Keep going.


2. Support staff who may want to learn; this could look like mentoring, resource sharing, or creating space for collaborative learning within your organization.


3. Be purposeful in seeking out allies and advocate for equitable access to resources for those who want to be involved with data & evaluation work. Identify and work to remove obstacles for those who want to participate in this work but might not have access or opportunity.


4. Be aware of your power and use it to make space for others. Elevate diverse voices without tokenizing. Be inclusive and transparent in decision-making processes.


5. Continually seek out opportunities to learn and share that learning back with your team – be intentional about turning individual opportunities into shared learning.


6. Be intentional. Listen deeply. Support your team and learn together. Show up and do the work.