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 I want you to share with our, our listeners a bit about your motivation, the visions, and what you see are the challenges and why particularly you emphasize that for the industry and the business sector.