Just because modern data science and artificial intelligence (AI) techniques produce results, it does not mean that more traditional way of social science (i.e. custom data sets created for a specific study) should be discarded. Data science has its own blind spots. A well-tempered combination of the two approaches should be of great benefit to those who seek evidence-based governance.
Andrew Feldman expounds on what he calls digital-age social science, a hybrid of data and social science that combines the strengths and hides the weaknesses of both methods, in Government Executive:
Results-focused government today requires digital-age social science, which is a hybrid between traditional social science and more cutting-edge data science. It recognizes the strengths and weaknesses of each approach and the synergies between them.
Traditional social science, for example, can be expensive, involving the collection of survey data from sometimes thousands of sample members. Yet customized data is very helpful for answering specific research questions. Data science, on the other hand, uses much cheaper existing data—often called “big data.” But overlooking the limits of repurposing data can easily produce meaningless correlations, not useful insights.
Digital-age social science uses both approaches smartly. It means that social scientists who want to answer research questions as accurately, quickly and efficiently as possible need to pay attention to all of the ready-made data that exists today. And data scientists who want to produce impactful research need to work with government officials to identify the most important problems or questions to be examined.