The most challenging aspects of purposeful Monitoring, Evaluation, Research and Learning (MERL) are most often: 1.) selecting informative metrics and 2.) performing MERL efficiently. Many individuals in MERL assume that technology and big data will dramatically improve both the selection of informative metrics and MERL efficiency. However, as a community, we also understand that existing structural constraints in the MERL ecosystem may either hinder or render ineffective technology-enabled tools. In addition, the biases present in large data sets may mean that metrics selected using machine learning are no more informative than those already in use and that MERL will ultimately be ineffective at improving institutional performance. In this interactive panel, we will discuss the existing structural barriers to the successful implementation of novel technology in the MERL space and what realistic expectations should be regarding the ability of big data to improve MERL outcomes.
In this interactive discussion, our participants will both try and answer the question:
Will technology and big data improve MERL or will existing structural issues in our communities, paired with known biases in existing data sets, make MERL all-the-more challenging?
Session slides
