Both monitoring and evaluation build increasingly on information gathered by mobile technologies, social media and remote-sensing data (see also "More than plug and play – Digital solutions for better monitoring and evaluation"). On the side of data analysis, so called “machine learning” is especially innovative. Setting out from computer algorithms, machine learning predicts trends based upon the processing of large data sets. On their own, new and larger data sets are not a panacea. Often, they only reflect major trends and probabilities without useful contributions to the questions of attribution and causality (Noltze and Harten, 2017). However, great potential lies in the integration of big data and machine learning in complex evaluations and in the triangulation of such data sets with case studies and cross-case analysis.

More than combining existing methods

In light of the broad range of impact evaluation methods, their strengths and weaknesses, as well as new forms of data, there is a huge untapped potential of integrating multiple methods in complex evaluations (Bamberger et a., 2016).