I'm sure it's still very popular in engineering. It's still very much the lingua franca for numerical methods applied to engineering problems - there are decades of materials solving engineering problems written in MATLAB.
The 10X was not just that it made it easier and faster to make exceptionally beautiful plots. Rather it was that it made me realize for the first time that software was, fundamentally, about an implementation of ideas. That the underlying idea, together with how well it's communicated through the API design and documentation, really matters. Reading Wilkinson's description of the grammar of graphics, and then seeing that translated to a code library... That was amazing. It defined what I think software is, as someone who was new to the world coming from an engineering background where coding was just an easy way to do linear algebra.
More likely people will make excuses for us and say 'well it was a different time' even though comments like yours and articles like this are clear exceptions.
There is a certain condescending sympathy that we afford to people just because they lived in the past. It comes from a misplaced desire to avoid anachronism, but ends up committing anachronism anyway.
How to Measure Anything is a fantastic book. Here are the most significant insights you learn in the book
- how to measure anything; Hubbard actually comes through on the promise of the title - after finishing the book you will truly feel that the scope of what you can measure is massive. He does this by a change in the definition of what it means to measure something, but you realize his definition is more correct than the everyday intuitive one.
- value of information; Hubbard gives a good introduction to the VOI concept in economics, which basically lets you put a price on any measurement or information and prioritize what to measure
- motivation for 'back of the napkin' calcs; through his broad experience he has seen how a lot of the most important things that affect a business go unmeasured, and how his approach to 'measuring anything' can empower people to really measure what matters.
Reading this book provided one half of what I have been searching for for a long time - a framework for thinking about data science activities which is not based on hype, fundamentally correct and still intuitive and practical.