One of my colleagues (well, strictly speaking my boss' boss) recently crystallized a very important and key idea for my colleagues: the difference between biomedical research data science and tech business data science. I gave his ideas some thought, and decided to pen down what I saw as the biggest similarities and differences.

The goals between the two "forms" of data science are different:

• The end goals of business data science
• The end goals of research data science

There are issues that I'm seeing in the data science field. Some of the problems I have seen thus far.

• Model-focused enhancements are saturating
• Experimentation is getting a bit out of hand

And what I think is needed:

• The goal of scientific model building is high explanatory power
• The place for overparametrized models is tooling
• Finding the appropriate model to apply is key

The key difference, I think is that The end goals of business data science is about capturing value from existing processes, while The end goals of research data science is about expanding new avenues of value from unknown, un-developed, and un-captured business processes. The latter is and has always been an investment to make; in a well-oiled system, the former likely generates profit that can and should be invested in the latter.

I think that the place where overparametrized models make the most sense for application are not in models with "high explanatory power" (see The goal of scientific model building is high explanatory power). Rather, their best realms of application are a bit more pedantic. We don't always make these large, overparametrized models to explain the world, per se, but to automate some task that could have been done by a human being. In other words, we come right back to the notion of using computing to automate routine tasks. Basically one of the two classes of tasks:

1. Generating representations of input data, which falls under the umbrella term of Representation Learning.
2. Automation of routine, manual tasks, via methods such as semantic segmentation.

Both of these are things that would have originally required human intervention.

Seems to me, then, that many so-called "AI" applications are merely (yes, merely) about automating tasks that would have otherwise taken much longer with humans.

Other references:

• The end goals of business data science

What are the end goals of research data science?

One of them is being able to explain the world, ideally in a causal fashion. (See: The goal of scientific model building is high explanatory power)

The kind of data scientist we need for this kind of work is different from that of business data science (see: The end goals of business data science).

The context in which research data science operates is one where business processes (and their corresponding business outcomes) are oftentimes not well defined. Thus, defining the ROI is a less straightforward task than it might otherwise be. As such, we need to view research data science as an investment for the future, just like any research organization is.

Also a thing to read: Researchers think mechanistically about the world