This applies to data science research as well as any other analytic discipline. For centuries, scientific research was performed in Academia, by university professors managing their own labs. Much of the research was carried out by young scientists who just completed their PhD. The selection process has always favored the same type of personality. The basic rule is "publish or perish" which produces the following drawbacks:
Data Science Central Research Lab
With the tenure process, research directors must be careful not to engage in revolutionary experimentation, in order to please their grantors and faculty boards. They also spend a considerable amount of time chasing money, rather than doing research.
This hurts innovation. The private industry and some agencies have their own research labs. But they hire the same type of individuals: the kid that always had perfect grades at school, assuming that this is a predictor of research quality (and since they define what quality is, we are stuck in a loop here). Yet the private sector provides an alternative to Academia, though many times, research results are kept secrets and incorporated into patents.
The New Model
Here I propose an new approach to scientific research, and discuss how it could be implemented on a larger scale, via proper monetization. It consists of independent professionals performing their research and publishing in popular blogs rather than in scientific journals, and obtaining themselves the data that they need for their tests and experimentation (many data sources are free, many projects are posted on Kaggle, and research-oriented projects are posted on DataScienceCentral, some using simulated data). You can call it crowd-research.
The advantages are as follows:
In my case, I realized that publishing in blogs takes 1 hour per article, rather than 50 hours for scientific journals. At $1,000/hour (my hourly rate), and since scientific journals don't pay authors, it's a $49,000 saving per article, that is, hundreds of thousands of dollars saved per year. Also, my articles are shorter, published much faster, reach a thousand times more users, are easier to read (with source code that you can copy and paste, data sets that you can download), and written so as to be understood by many professionals from various applied disciplines, not just a dozen highly specialized theoretical experts. You can compare my article on data videos with one published by a traditional statistician, in a top traditional journal, independently and at the same time. I believe that mine is more useful, provide code to make much faster, longer videos, and is in essence, of superior value.
How to pay for this new type of research?
The money can come from various sources. As a data scientist interested in doing research, you have the following options; you can combine several of them:
If you spend 25% of your time in these money-making activities (listed above), 25% of your time in building your network and reaching out to clients, 25% on doing scientific research (including working on projects that support your research), and 25% managing your business (organizing, planning, operations, finance), you will soon make more money than working in a cubicle, and at the same time doing things that you enjoy, with a real control on your life.
I'll write more articles on how to get started with this career path, and offer mentoring, in the near future. For now, feel free to check out our research lab publications.