This article is intended to reset the priorities for analysts from hard skills and frameworks to a broader understanding of the value of analytics. It will be helpful to data professionals of any level. As in the good old days, reading books is the best way to understand things better. 

Online education has revolutionized the way we learn now. The process is as democratized as it never was; millions of users have access to a variety of courses. It is a significant trend that is difficult to overestimate. There are several things that could be improved with this approach, though. The first one is that the increased number of programs makes it more challenging to choose a good and trustworthy one. The second significant problem - the courses are usually short, basic, and very practical. This format converts better than others. The students who did such studies typically tend to think about the profession as a set of actions rather than trying to get deeper. 

Analysts, like many other IT specialists, are also very often those who learn by doing. Learning by doing is an excellent way to boost your knowledge. But this boost may be much faster and have much higher quality if supplemented by previous generations' wisdom. 

It is worth reading to enhance the knowledge from online courses or structure your knowledge in analytics gained in your workplace.

To be honest, there are not so many previous generations of data analysts in the current understanding of the term. The term data-driven has only been used in the business context since the early 2000s. It means that there are only 20 years of literature on the topic. But several books are a must-read for any analyst and head of analytics especially.

If you don't have time at all, take two books from practitioners - two books by DJ Patil, the second one co-authored with Hilary Mason: "Building Data Science Teams" and "Data-Driven - Creating a Data Culture" - both published by O'Reilly. Thus, you will have a chance to understand, in general, what a good analytics team should look like, what it should do, and how the decisions should be dated in a company.

If you have more time - please refer to the most cited works about data-driven decision-making - such pillars as Davenport's "Competing on Analytics" or papers by Brynjolfsson. These fundamental works mean the same for modern data analytics as Odyssey for contemporary literature. 

If these two are Odyssey, Anderson's "Creating a Data-Driven Organization" is Uliss. Anderson builds over all the previous literature and perfectly explains all dos and don'ts. 

Having read these books, you will have a clear vision of where you want to move your organization from the point of view of data usage for decision-making and will avoid some of the pitfalls that can wait for you in this way.