Design with Purpose: A Data-Driven Guide to Building Effective Programs
In our last post, we discussed how nonprofits can use data to build a strong foundation for designing impactful programs. Once you’ve done the hard work of understanding the problem and setting clear goals, you can start to think about how to design the programs and activities that effectively address the needs in your community. In this post, we'll explore how a structured and data-centric approach can lead to programs that are effective, sustainable, and built to measure and improve.
Mapping a path to impact
Does your organization have a theory of change?
It can be helpful to ground the design process in your organization’s theory of change. A theory of change articulates how your organization’s work creates positive change. In other words, “if we_____, then _____ will happen.” You’ll want to keep this in mind as you progress in the design process to make sure your program aligns with your organization's overall mission and philosophy.
From Theory to Action: Building a Logic Model
You can translate your theory of change into a more tangible program plan using a logic model. This framework maps out the connections between your program's resources, activities, and intended results.
Start by outlining your:
Inputs - What resources will your organization commit? This might be staff, volunteers, funding, supplies, or something else.
Activities - How will these inputs be utilized to drive toward the intended outcomes? This might be holding workshops, distributing supplies, or advocating for policy changes. Other things to consider:
Address barriers - Think back to your research. Did you identify any barriers to accessing services? If so, build solutions into your processes. For example, if transportation is a known issue, consider offering mobile services or providing transportation assistance.
Prioritize equity and inclusion- As you design your processes, consider how to ensure equitable access and address potential disparities. For instance, if your data revealed that a certain group faces unique barriers, you might tailor your activities to meet their specific needs.
Outputs - What are the direct, measurable results of your program activities? This might include the number of people served, workshops conducted, resources distributed, or even website visits. Outputs are typically the easiest things to measure and provide a tangible indication of your program's reach.
Outcomes - What changes are observed as a result of your program’s outputs? Outcomes might include improved health outcomes, increased knowledge or skills, enhanced social connections, policy changes, or reduced poverty. While outcomes can be more challenging to measure than outputs, they reflect the true impact of your program.
Connect outputs to outcomes - It's important to establish a clear link between your outputs and outcomes. What evidence suggests that your activities and outputs will lead to these outcomes? This might involve drawing on existing research, best practices, or previous program evaluations.
Consider the broader, long-term impact your program aims to contribute to. This might be a reduction in systemic inequalities, improved community well-being, or environmental justice. While impact can be difficult to measure directly, it reflects your program's ultimate contribution to your organization's mission and theory of change.
Don’t forget about the data!
Make sure you have a plan to capture data at each step of your logic model. Continuous data collection helps to monitor progress, identify challenges, and make adjustments as needed. You might consider various data collection methods, such as surveys, interviews, focus groups, publicly available data, and operational or administrative data. Your data capture plan should enable you to measure progress toward your SMART goals.
It’s easy to breeze past this step while focused on program design, but outlining your data and measurement plan will set you up for success later on!
Best Practice: Revisit those goals.
Remember those goals you set? It’s always good to kick the tires on those once you’ve finalized some of the details. Think of your logic model as a two-way street. While it helps you visualize how your activities lead to outcomes, it can also be used in reverse to sense-check the feasibility of your goals.
Imagine your organization aims to increase high school graduation rates for students in a low-income community. Your desired outcome is to raise the graduation rate by 10% within three years. Based on research and past program data, you know that students who attend after-school tutoring programs three times per week are 25% more likely to graduate.
Starting with your logic model and working backwards:
Translate outcomes to outputs: To achieve your 10% increase, how many students will you need to provide tutoring for? Are there enough students who meet your inclusion criteria?
Assess your inputs: Do you have enough tutors, funding for space and materials, and partnerships with schools to reach that number of students?
What happens if your analysis reveals a gap between your desired outcomes and your available inputs? You have a few options:
Adjust your goals: If your initial goal seems out of reach with your current resources, consider setting a more attainable target.
Increase your inputs: Explore ways to secure additional funding, recruit more volunteers, or strengthen partnerships to boost your capacity.
Refine your activities: Can you change your target audience, or optimize your program design to deliver services more efficiently or reach a wider audience with the same resources?
Spotlight on The Advocates for Human Rights
Using data to end violence against women
As an example of some of the concepts outlined here, I’ll shine on the work of an incredible organization, the Advocates for Human Rights. The Advocates’ WATCH program uses court monitoring to advocate for the human rights of survivors of gender-based violence, including domestic abuse, sexual assault, and sex trafficking.
AHR’s work stems from a clear Theory of Change:
Without monitoring and accountability, systems fail to protect women. By integrating new and existing volunteers, The Advocates will continue to use court monitoring and documentation to change and implement laws to end violence against women.
This theory of change can be further broken down into the following logic model:
Inputs: AHR invests staff and funding, as well as volunteer support to monitor court proceedings, analyze findings, and advocate for the human rights of survivors.
Activities: Volunteers monitor court proceedings and capture data. As part of this process, Impactful Insights worked with AHR to analyze data to track patterns and trends in court practice.
Outputs: The data captured by volunteers, combined with in-depth analysis is used by AHR to identify issues impacting survivors, guide focus areas for their work, and advocate to courts and other systems actors.
Using this data and analysis, WATCH publishes reports with the intent that its findings and recommendations spark a broader discussion about systematic changes within the court system. This is intended to lay the groundwork for further research around the appropriate handling of cases of violence against women and children.
Outcomes: Through its monitoring and reporting, courts are held accountable to adherence to best practices and overall responses involving violence against women and children, ensuring consistent handling of these cases to maximize victim safety and offender accountability in every courtroom.
Impact: By grounding their program in data, AHR is working towards their long-term goal of systemic change within the legal system to end violence against women.
Measure, Learn, and Improve
You've laid the groundwork, designed your program, and are ready to put it into action. To truly understand your program's effectiveness and maximize its impact, you need a solid measurement plan in place. An intentional measurement plan translates data to actionable insights, and helps to:
Demonstrate impact: Show funders and stakeholders the value of your work.
Drive improvement: Identify what's working and what's not to make data-informed adjustments.
Ensure accountability: Track progress towards your goals and hold your program accountable for results.
The good news is, by integrating data throughout your design process, you already have some key pieces for a strong measurement plan:
Clear goals and success criteria: You’ve set SMART goals, and know how you'll define success.
An overview of the data you'll need and how you'll capture it: Your logic model has helped you identify key data points and potential sources.
From here there are a few steps you can take to finalize your approach:
1. Develop a Measurement Schedule
Timeline: Establish a clear timeline for data collection and analysis, aligning it with your time-bound goals. When will you collect data? When will you analyze and review results?
Incorporate decision points: Identify key decision points in your timeline where data will be used to inform adjustments or next steps.
Outputs: Determine what form your measurement outputs will take. Will you create a data dashboard? A slide deck? A comprehensive report?
2. Create a Communication Plan
Audience: Who needs to see the data? Your team? Funders? The community?
Frequency: How often will you share results?
Format: What's the best way to communicate the results to each audience? Consider reports, live presentations, infographics, or even social media.
3. Establish Benchmarks
Baseline data: Gather baseline data before your program starts so you can track changes over time.
External benchmarks: Consider comparing your program's performance to similar programs or national averages for useful context and to identify areas for improvement.
Remember, program design is not a one-time event; it's an ongoing process of learning and improvement. By letting data be your guide, you can ensure your programs remain relevant, responsive, and impactful for years to come.
