The second installment of Eugene Yan's series on data science and agile focuses on moving beyond generic agile prescriptions. Instead of forcing standard software development rituals onto data teams, Yan proposes taking what is useful from agile and reworking it around the way data science actually unfolds.
The source frames this as an effectiveness question: frameworks should help teams deliver, not add ceremony. In particular, data science work is less predictable than typical software tasks, so iteration, discovery, and changing direction should be built into the process rather than treated as exceptions. The emphasis is on choosing and adjusting methods rather than following agile by the book.
Since there is only one source, there is no alternative framework to compare against. The article positions itself as a practical continuation of a longer discussion, with the focus firmly on adapting known agile techniques to fit data science's exploratory character.