The development sector is gradually paying more attention to the potential for data and evidence to support program management, learning, and improvement. However, it can be challenging for organizations to find a Monitoring, Evaluation and Learning (MEL) approach that fits their needs. IPA's Right-Fit Evidence Unit supports NGOs, social businesses, funders, and governments to be smarter producers and users of data and evidence, with the objective of strengthening their learning pathways and improving their programs.

The Challenge: too much, too little, or the wrong data


Many organizations, pushed to prove that their programs work, have developed M&E systems that burden staff, collecting more data than they can manage and failing to support decision-making. For other organizations, data collection efforts are too limited, providing little to no information about program performance. Even organizations with credible impact measurements often lack data that gets at why their programs perform as they do and how to improve them.

Ill-suited funding processes and reporting requirements often unintentionally discourage experimentation and iteration from implementers. As a result, organizations are seldom incentivized to question and improve what they do.

On the funder side, despite being the primary audience of M&E reports, data collected rarely allows funders to learn across portfolios. In addition, ill-suited funding processes and reporting requirements often unintentionally discourage experimentation and iteration from implementers. As a result, organizations are seldom incentivized to question and improve what they do.

Overall, it is a challenge to find an approach to MEL that allows funders to learn from their portfolios while encouraging implementers to answer their own learning questions and act on them—all of this while ensuring good stewardship over resources.

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Right-Fit Evidence: maximizing the learning-to-cost ratio


As a response to some of these challenges, Professors Dean Karlan and Mary Kay Gugerty identified the CART principles (Credible, Actionable, Responsible and Transportable) as a set of principles that can guide the creation of right-fit MEL systems. More details on this framework are available in our online toolkit and Gugerty and Karlan's book.


Based on these principles, IPA launched the Right-Fit Evidence Unit to complement our traditional randomized evaluation work. The unit provides resources and advisory services to support the design and implementation of MEL systems that maximize the learning-to-cost ratio.

Right-Fit Evidence Diagram: Learning vs. Cost

You can read about our main services here, and see examples of engagement here.

The Right-Fit Evidence Unit employs four key principles for monitoring and evaluation, known as the “CART” principles.
Credible
Collect high quality data and analyze the data accurately.
Actionable
Commit to act on the data you collect.
Responsible
Ensure the benefits of data collection outweigh the costs.
Transportable
Collect data that generate knowledge for other programs.

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