Active Industrial Learning: What We've Learned—and What We'd Like to Learn From You

First of all, Hamet and Miguel, thank you so much for doing this podcast.

And, uh, as the editor-in-chief of Harvard Data Science Review, I obviously particularly appreciate that both of you serve as a co-editor for this column called Active Industry Learning.

So for...

the first question we have is for listeners who haven't read your column, how do you two describe the Active Industrial Learning column in terms of its mission, in your words, and, uh, what kind of gaps, you know, were you trying to fill when you took over, uh, this column?

Maybe I'll kick this off, Miguel.

So I've been with the column, I think, for about three, three and a half years, uh, roughly.

And of course, the world has changed tremendously in those three and a half years, and I'm sure we're gonna, you know, get into that.

But, uh, obviously, a lot of, uh, data science has been, since I guess the term came about over a decade, uh, decade and a half ago, has been applied, right? So organizations using the variety of tools, techniques, technologies that we maybe collectively call data science to make better decisions, to drive their organizations more, you know, efficiently, more effectively.

It really is a, is an applied discipline from a lot of different perspectives.

So I think the column aims to bring that learning from applying data science globally across organizations, sectors, uh, you know, individuals, practitioners, and see how we can share that with the broader ecosystem, so people can benefit from each other's learnings.

So not only from all the great research that is shared across different parts of HDSR, can we bring a lot of insight from the field in how it's being applied in real life? And of course, AI has changed a lot of things for us.

But Miguel, you came on board, uh, I think about a, a year ago.

Uh, so maybe it was slightly different, uh, from your perspective.

I don't know what you saw when you first came on board.

Yeah.

Hamet, thank you.

I, I think when I, I came on board, um, first of all, I was very honored to be invited, and I, I think what I saw was, uh, an opportunity to really highlight some of the elements that many times are not discussed in traditional publications that are geared towards enterprise corporate business leaders, which are where, where usually where things break, right? So, like, our Active Industrial Learning, uh, column focuses on applications of data science to businesses, and I, I like the phrase we have as a part of the description, which is, "With an eye toward practical considerations that make data science initiatives more likely to succeed," and obviously AI initiatives more likely to succeed.

And I think this is very important because you can talk about the technology, but really what usually is not discussed is those nuances that, again, make things break.

So I think the focus of those insights from the people that are publishing and, and the highlight of, of those insights for people that are reading, I think is what really was appealing to me and which, which I see in my work, uh, with, with executives, with companies, that becomes the key success factor to increase the probabilities of value capture with AI and data.