Eugene Yan's latest essay, the first of two parts, takes a close look at how Agile methodologies fit data science work. It frames Agile as neither a universal fix nor a useless ritual, but as a set of practices whose value depends on context. The piece promises a deeper examination of strengths and weaknesses rather than a one-sided verdict.
Because this is Part 1, the analysis is introductory in tone. It identifies areas where Agile works well in data science and areas where it tends to break down, though the specific examples and conclusions are not available in the summary. Since there is only one source, there are no conflicting views to weigh; the piece stands as the author's own assessment.