Rich Carder

I’m coming to RLD from Wellspring Philanthropic Fund, where I worked for over 11 years. Most recently, I was the Manager of Data Strategy and Analytics, a role that encompassed designing monitoring and learning systems that collected and surfaced key information to inform strategic decisions and adaptations, developing analyses and reports to allow us to better understand and reflect on our grantmaking, pioneering new techniques for extracting structured data on accomplishments and learnings from unstructured narrative text, and setting up data infrastructure to support these needs.

Before that, I worked in the Learning and Evaluation department for six years, which planted the seeds for the eventual more data-focused role. This included a greater focus on implementing organizational learning agendas, supporting strategy development, and leading in-house grantee perception surveys. I also initially worked in the IT Department, which allowed me to build up technical and programming skills that I later applied to the data analytics and evaluation roles.

Since 2019, I have also led the DC Chapter of DataKind, a non-profit that provides pro bono data science to mission-driven organizations. We partner with organizations that may not have in-house data staff, helping them identify data and AI opportunities, recruiting and managing teams of pro bono experts to develop data systems, sharing analyses and insights, and building data capacity.

The Director of Data Strategies and Insights role at RLD was a natural next step for me. It merges the big picture systems focus from my time in Learning and Evaluation with the analysis and technical skills I developed in data strategy and IT, as well as the capacity-building focus I’ve honed with DataKind. This is an incredibly exciting opportunity requiring a unique blend of strategic vision and hands-on technical ability, which I am eager to bring to the organization.

The first is fostering a strong culture of ongoing learning and reflection through the use of data. This is a key prerequisite for everything in the role and requires building great relationships and a high level of trust both with program staff internally, and with grantees and others in the ecosystems we work in.

Another critical responsibility is asking good questions and creating processes that encourage everyone to hold up a mirror to their work. “What don’t we know?” should guide the types of data and evidence we need to find and analyze. Putting processes in place that regularly surface these questions is vital to continuous learning. A few principles I like to adhere to when it comes to data:

We should not default to bad proxies just because they’re easier to measure.

• We view data as a tool for continuous learning.
• The best data we have is the lived experience of the communities our work is intended to benefit.
• We should not collect data for the sake of collecting data.
• Trust-based philanthropy and data-driven philanthropy are not mutually exclusive.
• Ultimate meaning-making should sit as close to affected communities as possible.
• Data flows should not be one-directional; if we’re collecting data from a community, it’s our responsibility to share back what we’re learning.

Everything is driven by the goal of creating tangible and durable benefits for the people and communities we serve. Listening is key—data initiatives need to be informed by community perspectives and voices to ensure they’re actually moving us in the right direction.

Internally, data initiatives should result in insights that program staff can use to refine their strategies. While it’s difficult to define specific initiatives before strategies are fully developed, the overarching point is that data initiatives will be responsive to emergent needs, not one-size-fits-all.

The lived experience of the people and communities our work is intended to benefit is the most important data we have. There’s often a perceived tension between being data-driven and listening to or trusting what communities and grantees are telling us. I fundamentally disagree with the notion that the two are mutually exclusive. When we listen rigorously to many perspectives and systematically document what we hear and learn, that’s incredibly rich data to guide our work that should never be treated as second class to anything.

On the flip side, foundations often ask stakeholders and communities for input over and over, which can lead to fatigue, redundancy, and a level of extractiveness. Improving how we document, reuse, and then share back what we’ve learned is also an opportunity to reduce the burden our quest for input can result in while providing value back to those who lent their voice in the first place.

My goal when it comes to evaluation is to learn what is working and what isn’t so we can adapt our work and strategies to make them more effective. The way the sector traditionally has gone about counting the people served by a program or making grantees report on standardized impact metrics doesn’t really give us actionable data on what to do next, if it tells us much of anything at all.

We need to work with grantees and think through together whether there could be data that could help them answer any burning questions, better understand the communities they work with, drive program outcomes, or illustrate impact appropriately. If support for grantees to bolster data collection and utilization efforts towards those ends could be helpful, we’ll work to build out that capacity. From the funder perspective, this may not result in apples to apples metrics across a portfolio of grantees that we can easily aggregate up into one large number. But it will result in much higher quality data that’s more relevant to the work our grantees are doing and which has the potential to yield much greater insights.

In addition, in some cases, we may embed additional evaluation capacity into a program or initiative if our ongoing data collection mechanisms aren’t sufficient to answer the questions we have. In other cases, publicly or privately held data may reveal trends over time that our work has contributed to—or point to areas where progress hasn’t been made, suggesting the need for further investment or new approaches.

A foundation like ours has the opportunity and positionality to triangulate between these data sources to build out a more comprehensive picture of our issue areas, ultimately informing strategies and decision-making both for our foundation and in the broader ecosystems that we work in.

The considerations aren’t all that different. We want to put state-of-the-art technology to work in service of our mission, just as the corporate sector does in service of profit. Both sectors share many of the same needs:

• Top-notch data security to safeguard information provided by grantees.
• Great user interfaces that encourage staff to actually use the tools.
• Data portability so we can collect data easily, combine it with data from other systems, and maximize its utility.

One key difference in the nonprofit technology sector is the ethos of sharing solutions –  tools and systems developed by one organization are often open-sourced for others to use. I want RLD to contribute to this practice, moving the sector forward by learning and sharing with others and making anything we develop available to organizations facing similar challenges.

AI is a major trend, and we’re transitioning from hype and high-level discussions about its potential to concrete use cases where the technology is proving genuinely helpful (while also being mindful of its risks and how to mitigate those).

For example, AI is transforming how we collect and utilize qualitative data. Previously, asking for narrative grant proposals or reports—or even replacing written deliverables with conversations—meant we couldn’t roll up information across proposals or reports without significant manual work. As a result, much of the learning from years of grantmaking remained locked in an ocean of unstructured text.

Now, we can use AI to extract structured information from any source or format. This capability reduces the burden on grantees while allowing us to more effectively utilize the information we’re gathering. It’s a powerful shift that enhances how we learn and make decisions.

We have a fantastic team and are approaching this work with a commitment to thinking big and systemically—while fully acknowledging that we don’t have all the answers.

I’m impressed by the thoughtful way the organization is developing its strategies and engaging with the communities in which we will be working, dedicated to centering the voice and experience of those most directly affected by the issues we’re hoping to address.

On a personal level, it’s an incredibly exciting opportunity for me that data is seen as a foundational backbone of all parts of the organization’s work. It’s an amazing privilege and also a great responsibility to be in a position to help move us in the direction of that vision.

Ultimately, success means creating positive change in Chicago through our grantmaking and collaboration with grantees and communities. Leadership in service of that goal will come from many, and from all angles and levels. The sum of this shared leadership can be measured by the strength of our relationships, the depth of our networks, and our collective power to drive change and improve lives.

When it comes to hobbies, I don’t have one I do obsessively, but I dabble in many. I see a lot of live music, and have on and off again relationships with biking, hiking, audiobooks, ultimate frisbee, board games, trivia, and rock climbing. Occasionally, I’ll dive into a data side project, like trying to make art out of mountain elevation data or working to perfect fantasy sports player rating models. Regardless of whatever hobby-phase I’m in at a given time, my wife and two dogs are at the center of everything for me. Recently, we discovered a love for European-style inn-to-inn walks and are on the lookout for ways to recreate these in the US. My aunt Jude has joined us for a few along the England-Wales border and in the Scottish Highlands, and despite being in her 80s is faster than us and beats us to the pub on each stretch – a true inspiration and perhaps the ultimate goal to keep working towards.