In 1962, J.W. Tukey wrote his famous paper “The Future of Data Analysis” and promoted exploratory data analysis (EDA), a set of simple techniques conceived to let the data speak, without prespecified generative models. In the same spirit, J.P. Benzécri and many others developed multivariate descriptive analysis tools. Since that time, many generalizations occurred, but the basic methods (SVD, k-means, etc.) are still incredibly efficient in the Big Data era.
On the other hand, algorithmic modeling or machine learning is successful in predictive modeling, the goal being accuracy and not interpretability. Supervised learning proves in many applications that it is not necessary to understand, when one needs only predictions.
However, considering some failures and flaws, we advocate that a better understanding may improve prediction. Causal inference for Big Data is probably the challenge of the coming years.
It is a little presumptuous to want to make a panorama of 50 years of data analysis, while David Donoho (2017) has just published a paper entitled “50 Years of Data Science”. But 1968 is the year when I began my studies as a statistician and I would very much like to talk about the debates of the time and the digital revolution that profoundly transformed statistics and which I witnessed. The terminology followed this evolution–revolution: from data analysis to ...