A big problem in business intelligence is the shortage of qualified professionals. That includes developers, analysts, data scientists and managers.
The current strategy many employers use to hire for these positions focuses on experience. They will require so many years experience in R, Python, and / or SQL. If it's a data scientist job, they will often include a masters degree as a requirement as well.
That's a good strategy to start off with. You need experience to build a strong foundation upon, but attempting to fill an entire team or department with these unicorns is expensive and almost impossible.
The problem is that programmers have a wide array of job opportunities outside of business intelligence. You may have the one advantage that data science is a "sexy" topic right now, but so are many other cool areas of expertise these developers can go into.
Take my brother as an example. He's a Java developer and he'd probably be pretty good at business intelligence, but he gets to make computer games for a living. How the hell do you compete with that?
Instead of exclusively trying to fill positions with these unicorns, I would take a duel approach. First, hire the experienced, technical person that can serve as a mentor. (And pay him or her enough so they stick around!)
Second, hire new graduates with degrees in mathematics or economics.
Why? Because those college graduates are more likely to have the aptitude to learn business intelligence. They gained the math skills needed for this profession in college and can learn the technical aspects from more experienced developers.
This setup worked well on the first business intelligence team I worked on at Cerner. There were two or three developers with over ten years experience and a gaggle of entry level graduates with degrees in economics and math. The experienced developers were expected to be teachers to the newer employees. I don't think this was an intentional strategy by Cerner, but it seems to work well.
Many of the people I worked with on this team went on to other successful positions in this field. None had any background writing SQL or building databases when they started, but all had the aptitude to learn.
What Makes Math and Econ Majors Easy to Train for as BI Professionals?
The first is their strong foundation in statistics. Upper level economics courses typically involve two or three statistics courses. I don't know if math courses do the same, but anyone who majors in math has the aptitude to understand statistics easily.
Math skills are less important early in a business intelligence career because that time will be spent learning about the nature of data and how to program it.
However, machine learning and predictive analytics relies on statistics. You simply can't develop those programs without it. Once your econ grads learn about data, they'll start thinking of how to do these things almost intuitively.
The second reason is that learning mathematical notations is a good preparation for learning computer code. One of the things that determines how well someone does in a math oriented major is the ability to learn what an equation is saying without actually doing it.
Being able to visualize the math in your head and how it's affecting the variables is not something that comes easy to most people. For example, most people who do well in math can understand the following equation. Can you?
This translates well into learning code because it's crucial to remember functions and how they manipulate or utilize variables. An experienced programmer can read the following like a mathematician can read the equation above.
Lastly, math and economics may be STEM majors, but they're (usually) still part of liberal art schools. That means those students take history, philosophy, and literature classes. Those classes are great at teaching students to take little bits of information and piecing them together into a larger narrative.
In other words - it teaches them to think about the big picture and communicate it.
That's something many organizations crave in their employees. It's even more critical for a career in business intelligence.