Data literacy is not statistics. It is the working capability to look at a chart and know what question it answers, to notice that the denominator changed, to ask where a number came from before repeating it, and to say "this does not support that conclusion" in a meeting. Most of it is skepticism and vocabulary rather than technique, which is why it can be taught to a commercial team in weeks and why a tool rollout never produces it.
In practice. Literacy is built on the organization's own data, in the context of decisions people already make. A one-hour session that walks a sales team through their own pipeline report — what each field means, where it breaks, which comparisons are invalid — outperforms a generic course by a wide margin.
Where it goes wrong. Literacy is bought as a license for an e-learning platform and reported as completion rates. Completion measures attendance, not capability. The measure that matters is whether the questions asked in review meetings get better, and that is observable within a quarter if anyone is watching for it.