A Data-Driven Approach to Learning for the Future

Integrating Monitoring and Evaluation Into Science Philanthropy

In September 2016, Priscilla Chan and Mark Zuckerberg made a bold announcement — they were going to expand their philanthropic organization, the Chan Zuckerberg Initiative, into basic science. The mission for the work in basic science was just as ambitious — to support the science and technology that would make it possible to cure, prevent, or manage all disease by the end of the century.

Successful achievement of this goal will be transformational for the world, but the path to get there is risky and long. We can’t wait 80 years to know whether we pursued the right strategies and took the right steps to achieve our desired outcomes. We need to demonstrate progress along the way and use evidence to determine what is and isn’t working as we continue our journey. As we forge ahead in our work, we want to seize opportunities for learning and use these learnings to shape our strategies and activities to accelerate scientific progress, best serve the communities and fields in which we work, and drive us toward the greatest impact.

Earlier this summer, I joined the CZI Science team as the Science Program Manager for Monitoring, Evaluation, and Learning, taking on the charge of establishing the monitoring and evaluation (M&E) function for Science in an effort to further our commitment to using evidence to learn and improve our science work. I’m excited for this new challenge and to use monitoring, evaluation, and learning to help build the strength of this still young organization, as well as to share ongoing learnings with the wider scientific, philanthropic, and M&E communities.

Julia Klebanov, CZI Science Program Manager for Monitoring, Evaluation, and Learning.

Monitoring and evaluation can be embedded in all stages of a funding program by designing programs with clear hypotheses about how interventions and strategies will achieve the ultimate desired goals, continuously collecting relevant data for determining progress and success, evaluating what is and isn’t working, and using learnings to improve.

Using Evidence to Learn and Adapt

As a young organization, we acknowledge that we still have a lot to learn. We know that our ambitious goals are risky. Embracing risk requires us to also embrace uncertainty and to be diligent in using the results of our work and our experiences to course correct as necessary. Scientific advancements rely on evidence, and as a science funding program, we want this reliance on evidence to permeate all that we do — both internally and externally. We strive to maintain a data-driven mindset, and to use evidence and analysis to systematically learn and pivot when necessary.

Collaborating to Advance Monitoring and Evaluation

We believe collaboration accelerates the path to progress, both in the work that we fund as well as our internal work. CZI’s unique structure pairs engineering with grantmaking and policy and advocacy work to solve problems. This structure allows us to bring together data scientists, engineers, and experts in various areas of biomedical science to collaboratively advance our M&E work, drawing on the strengths of colleagues from across disciplines. By engaging various groups within the Science initiative, we can spread a culture of learning and evaluation throughout our organization — not just among those who are responsible for making funding decisions. We will work with our technology teams to not only advance their own M&E work, but also to collaborate on the development of new mechanisms for collecting and analyzing M&E data.

Committing to Sharing Learnings

We want to engage in M&E in a meaningful way and are committed to sharing what we learn from our efforts beyond CZI. We know that accelerating science will require the synergistic efforts of multiple players. It is in this spirit that we want to share with others in philanthropy both the learnings from our M&E work as well as our challenges, along with the practices we find most effective for implementing M&E in science funding. In addition, we will collaborate and exchange ideas with groups and individuals who aim to advance evaluation practices for science, including those from the M&E field, academia, and philanthropy.

What’s Next

M&E is an established field, and an increasing number of philanthropic funders are building out their M&E functions. Creating robust M&E practices will require using developments in the field to expand our knowledge and being thoughtful about how to apply these concepts to CZI Science’s ongoing work. Our desire to use evidence and evaluation to learn and improve will require us to grow our capacity in M&E and build a culture of evaluative thinking. We will need to test our hypotheses, accept that not everything we do will be successful, and commit to learning for improvement. With robust practices in place, we hope to use M&E to enhance our ability to learn and adapt, increase the benefit we provide to the communities we work with, and maximize our impact on accelerating science.

To learn more about our work at CZI, visit our website and follow us on Twitter.

Julia Klebanov, Science Program Manager, Monitoring, Evaluation, and Learning

Julia Klebanov has spent her career working in philanthropy, both as a grantmaker and M&E professional, with a specific focus on working with science funding programs. Prior to joining CZI, Julia held the position of Adaptive Management and Evaluation Officer for Science at the Gordon and Betty Moore Foundation. During her career, Julia has worked to bring science funders together to enhance M&E practices in basic science philanthropy and has also facilitated efforts to establish communities of practice for basic science evaluators. Julia enjoys being able to take a cross-disciplinary approach to her work, drawing on her background in biology, evaluation and project management.



Chan Zuckerberg Initiative Science

Supporting the science and technology that will make it possible to cure, prevent, or manage all diseases by the end of the century.