Many organizations, motivated or pushed to prove that their programs work, have de­veloped M&E systems that are too big, leaving staff with more data than they can manage. In addition, the data that are available often resemble a “box-ticking” exercise, failing to provide the informa­tion needed to support decisions, learning, and im­provement. For other organizations, data collection efforts are too small, providing little to no information about program performance, let alone their impact on people’s lives. Even organizations with credible impact measurement often lack data that really gets at why and how their programs perform as they do—evidence which could be used to improve operational decisions in a timely way.

Right-Fit Evidence Diagram: Learning vs. Cost

Seeing these challenges, two researchers—Mary Kay Gugerty and Dean Karlan—set out to identify a set of principles organizations could use to identify the right time to engage in impact evaluation, and—just as importantly—build systems that provide information that supports learning and improvement. The result is the CART principles (Credible, Actionable, Responsible and Transportable) detailed in a new book, The Goldilocks Challenge.

Based on these principles, IPA launched the Right-Fit Evidence initiative to complement our traditional randomized evaluation work and help find the right-fit between collecting too much data that doesn’t get used and not collecting enough. The initiative provides resources and consulting services for organizations, donors, and governments in designing and supporting the implementation of cost-effective, appropriately-sized M&E systems. IPA provides support to design a learning agenda, collect the right M&E data, and put the findings into action. Read more about our services »

IPA has also developed an online toolkit highlighting best practices and providing cases and lessons on applying the CART Principles to M&E.

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

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