- Michael Bamberger Development Evaluation Advisor Michael Bamberger has a Ph.D. from the London School of Economics and more than 40 years of experience working on development evaluation around the world. In recent years he has worked on opportunities and challenges for the integration of ICT and big data into the evaluation of development programs. From 1965-78 he worked with NGOs […]
- Peter York Principal and Chief Data Scientist, BCT Partners Pete York has over 20 years of experience as an evaluation consultant and researcher working with government agencies, philanthropies, corporations and nonprofit organizations. For the past 8 years, he has built predictive, prescriptive and causal evaluation models using large administrative datasets and machine learning algorithms in the fields of child welfare, juvenile justice, mental health, […]
We are living in a world that is increasingly dependent on big data and data science in every aspect of our personal lives and our economic, political, and social systems. Big data also plays an ever more important role in research and evaluation, in large part because there are powerful new user-friendly analytic methods that make all the world’s rapidly growing data accessible and more meaningful to an increasingly wider range of audiences. With the rapid expansion of big data and analytics, it is time for the two fields of program evaluation and data science to come together in order to more rapidly and cost-effectively learn what works, improve social solutions, and scale positive impact as never before.
Data science makes it possible to collect a vastly increased range and volume of data more easily, quickly, and economically. The ability of big data to include all of those in an entire population, rather than just a relatively small sample, makes it possible to avoid many kinds of selection bias and enables disaggregation of the sample to cover many different sub-samples and categories. The technologies and now-affordable infrastructure of big data mean that evaluation studies can be conducted more rapidly and cheaply while advancing our understanding of the complexity of social problems.