Organizations succeed with analytics only when good data and insightful models are put to regular and productive use by business people in their decisions and their work.

Actionable data is a buzzword that I have used and heard for many, many years. However, in practice it is much harder to produce actionable insight for business users. C-level executives are always trying to dissect old information which can be very useful, but only in cases where the answer will have some actionable insight. If the answers to question have interesting insight, but don't provide any action, it creates a time consuming chase in how to make something out of the data.

Organizations have to educate themselves of what is capable of their data. It's no use to ask questions that the data will not be able to turn into actionable insight. Organizations should spend time on questions that can be solved with the current capabilities, in other words the low hanging fruit. Once there is no longer any low hanging fruit, enhance the analytics with new tools and modeling capabilities to find the next round of low hanging fruit.

If you want to put analytics to work and build a more analytical organization, you need two cadres of employees:

Analytics professionals to mine and prepare data, perform statistical operations, build models, and program the surrounding business applications.
Analytical business people who are ready, able, and eager to use better information and analyses in their work, as well as to work with the professionals on analytics projects.

Very true. An organization can hire all the analysts they can handle, but if the analysts have no business acumen and the business has no data acumen, there will always be a disconnect.

Organizations need to have both the business and analysts working together to find the best answers. Data Scientists can find very interesting items for them, however the business side may provide insight that shows the data scientists work is a known insight. The business may be working very hard to solve a problem that the data scientist can solve, taking intuition out of play and using data in the place of trial and error.

In my history I have always liked business and data analysts reporting to the same group. Many organizations don't like this structure because it can lead to a group "grading their own paper". However, the tight integration of the teams produces results more efficiently. Analysts are not wasting time chasing problems that don't exist and business people can bounce ideas off of analysts for quick insight before making decisions.