It seems as if every organization wants to enable analytical-decision making and embed machine learning into operational processes. What can you do with data science? It looks like anything is possible. What can you really do? Probably a lot less than you expect. Vendors promise easy-to-use tools and services but they rarely deliver. The products may be easy but the work is still hard.
Using data science to solve problems depends on many factors beyond the math: people, processes, the skills of the analyst, the technology used, the data. Technology is the easy part. Deciding what to do and how to do it is more difficult. Despite this, fancy new tools get all the attention and budget.
People and data are the truly hard parts. People, because many believe that data is absolute rather than relative, and that analytic models produce an answer rather than a range of answers with varying degrees of truth, accuracy and applicability. Data, because managing data is a nuanced, detail-oriented and seemingly dull task left to back-office IT.