Measurement, learning, and evaluation
Using data to make smarter decisions
Measurement, learning, and evaluation (MLE) puts the “L” for learning squarely in the mix of using data to make smarter decisions. The emphasis on data-driven decision making requires careful attention to how data are produced, how to set the basis for rigor and the many shapes that can take, and how data are synthesized and become part of strategic, programmatic and operational decisions.
It’s more than monitoring and evaluation
Previously, the common approach to this field was monitoring and evaluation (M&E), with clear distinctions made between the two. Monitoring focused on outputs and performance, while evaluation focused on effectiveness and accountability.
Today, building on experience and results of myriad development projects and social innovations, a new paradigm is taking shape. This new paradigm:
- Creates a more agile and fluid flow between monitoring and evaluation so that all data have value, and the right level of questions is asked at the right time in the life cycle of a program or initiative
- Shortens the through-line between the initial production of data and the follow-up integration of data into decision making, so that stakeholders and decision makers have access to meaningful data
- Aligns the type of data being collected with the types of questions that need to be addressed, so that there can be actionable intelligence
- Considers timing issues, so that data are available in the rhythm of decision making
- Allows for failure, so that failure can provide key learnings that contribute to getting it right
- Values knowledge products, so that collective intelligence grows in the field of social impact
In short, MLE is not new. However, it is a part of the new paradigm that calls for a more relevant and strategic extension of what formerly was found under the header “monitoring and evaluation.”