I've spent a good chunk of a half-century-long career, now winding down, doing the kind of work our author describes. Most of it was on my own initiative. I'd n

I've spent a good chunk of a half-century-long career, now winding down, doing the kind of work our author describes. Most of it was on my own initiative. I'd notice some kind of anomaly in the logs of whatever app I was working on, investigate it, and generate a report program highlighting it. Most of the time people ignored this stuff. That took some getting used to. But occasionally one of these little report programs would show a solvable problem. The usual example was a whole bunch of customers using the app in some way unpredicted by product designers and developers, and so missing out on a valuable feature of the app. Often we were able to add some kind of workflow feature, or add a UI afffordance to help customers take advantage of existing features. (Other times the little reports detected performance trouble. Always consider using time-of-week as an x-axis when working on these little reports.) The hard thing about doing all this is that I, and my colleagues, never knew ahead of time which data anomalies would be actionable and which were just fun facts to know and tell. Sometime along the way, that part of my work was dubbed "data science". That's about the same time a bunch of enterprisey software entrepreneurs discovered that "dashboards" generate sales because they appeal to front-office folks with control over money. Irony: I developed a bunch of integrations for a really expensive data-analysis product using Jetbrains tools I paid for personally. I always thought of that part of my jobs as diagnostic and exploratory. What can we learn from how this system works in the real world? In my case, of course, I also had some responsibility for the systems generating the data I analyzed. My advice to others doing this: 1. Always always assume you'll be called on to generate recurring reports with your little report programs. Make or buy a report-generating tool that can, at a minimum, deliver CSV files by email. 1. Indulge your curiosity. Especially with strange and incomplete data sets. Don't think of your task as "generating a report from bogus data for people who don't give a s**". Think of it as "figuring out how to make sense of the process from the data it captures as it operates". 2. Learn all you can about the processes you're analyzing, be they failed logins to SaaS apps or ambulance-calls to wrong addresses, or whatever. 2. When you have nothing much left to learn at a particular employer, teach somebody else how to do your job and then move on.