Well - there _is_ a bit of Student's Paradox involved in requirements-gathering for machine learning or analytics, and it's not always apparent that you're using the right algorithm for the problem or what specific metrics you actually need to be optimizing. Most of the time when I've gone through User Story collection processes, the actual end-state users describe is vague, not based on hypothesis testing, and not supportable by the data available onhand. A big part of this process is discovering what the data _can_ tell you, and if necessary to rewrite the technical requirements entirely to align with the actual business need.

Even when a customer's given me a "clean" dataset, I've had to write 400+ lines of code to do the feature engineering on a relatively straightforward logistic regression. Then there's all the other times when a customer asks me to deploy one type of algorithm, and their business problem is actually solved by an entirely different class of algorithm entirely.

Zayd over at Stanford has a nice blog post [1] describing why machine learning is several more dimensions of complexity compared to traditional software development. There _is_ a specific set of Data-first skills that is complemented by dev and CS experience, but a fundamental reason why ML projects fail is due to lack of appreciation for the many different skillsets needed to succeed.

[1] http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.ht...