Let's assume you're paying a developer $100k per year - not atypical in this day and age. As a business, I want that developer, who is likely making more than a

Let's assume you're paying a developer $100k per year - not atypical in this day and age. As a business, I want that developer, who is likely making more than anyone else in the organization, to be working on "the most important thing" in our business at a given time. This might mean a new checkout flow, making sure the online shopping cart works, or scaling a website to accomodate 1000 users at once. There are many tasks which engineers can easily do, and which most non-technically people are loathe to perform, but are not the most important thing that an engineer ought to be doing in a business. The easiest example is reporting. A given marketing department will want sales information. They'll want charts and graphs describing conversion rates, customer acquisition costs, etc. There are far more ways that the marketing department will want to slice-and-dice data for analysis than a team of developers can possibly produce. Engineers don't want to be relegated to creating reports. And anyway, the report you just asked your engineer to create may have just cost you $1000. So if we were able to bring these marketing people to a level of proficiency where they could use SQL to generate reports, Excel to manipulate that data, and VBA to do analysis (which I believe could be accomplished with 80 hours of training - a drop in the bucket over the course of one's career), then we can let the engineers do the things they are good at ("make sure passwords are secure") and the marketing folks have the minimum proficiency they require to do their jobs. This is common in companies like Amazon; "the engineers wouldn't help me run reports, so I had to learn myself." Nowadays, business decisions are based on numerical analysis of behavior. (Click-through rates of advertisements. Conversion rates from campaigns. Number of lawns per high school student per summer.) This trend is unlikely to go away. In practice, "numerical analysis" means "schlepping data into Excel and running the numbers." And that skill of "running the numbers" without having to ask an engineer (who is both expensive and uninterested) seems, to me, of great practical value. Obviously I have cornered one specific example that I consistently deal with. But it is not too difficult to imagine that these tools of analysis can help automate any number of tasks, in many disciplines. It is not uncommon for linguistics (graduate) students to learn programming to better analyze the meter of poetry. Optimistically, I hope that "non-technical" will be able to use the tools of our trade in new and interesting ways that we never imagined - the same way a carpenter might use everyday materials in ways that are very innovative to laymen.