Last Sunday, The New York Times published an excellent essay by Matthew Crawford entitled "The Case for Working With Your Hands"
Crawford has a motorcycle repair business - and a PhD from the University of Chicago. In his essay, he wrote about the many merits of working with your hands (e.g., so far, most of it can't get exported to Mumbai), and on the soul-crushing nature of so much knowledge work - enervating, seemingly make-work tasks so far removed from real-world anything that it's hard to explain exactly what it is that you do.
The section where he describes a job that he had as an abstracter, responsible for summarizing the contents of generally arcane academic articles - where his work was a white collar version of Lucy and Ethel on the chocolate-making assembly line - got me thinking about some of the worst projects/jobs I worked on during my long career.
Super-modeling: My first job out of business school was working for a small company that built econometric forecasting models. These models - supposedly - helped Fortune 500 companies run their businesses by providing them with a scientific approach to determining what comes next. Talk about pseudo-science! My job as a junior forecaster was to "pump the fit" of the models we built. Without getting into a treatise on regression analysis here, the "fit" is the measure of how well the behavior of a certain set of variables explains what happens to whatever it is you're interested in. E.g., an increase in the price of sugar might explain - both logically and mathematically - an increase in the price of soda.
This simple little example uses a bit of logic - we all "get" (because we're mighty knowledge workers with mighty knowledge) that there's a correlation between the price of sugar and the price of Mountain Dew.
When we were building our models, however, logic was the first thing to go.
Because our economists used the "goodness of fit" to explain why our models were so darned good, they expected us to come up with models that demonstrated "goodness of fit," even when goodness had nothing to do with it.
Thus, we would throw data on variable after variable into the equation to see if we could pump fit up to the desired 99% level. (99% of the variance in A explained by the variance in B. I think I've got this right: it's been 25 years since I had to think about what R-squared means.)
I remember working on a model for one of the RBOC's (regional Bell telcos) that was going to be used to forecast how many phones would be uninstalled. Now, logically, deaths, moves, and business closings might be microeconomic factors that could influence phone "exstallations". At the macro level, it might be a variable like rate of increase of GNP, or the unemployment rate.
Somehow, nothing that was logical did the mathematical trick for us.
We apprentice modelers were instructed by our supervising PhD economist to "keep trying" to find something that worked.
Well, the variable that worked best seemed to be swimming pool installations.
I so would have liked to see our economist explaining that to the RBOC. I'm sure he would have stroked his beard, chuckled a bit, and said, "Somewhat counter-intuitively...."
At least when I worked in the shoe factory, I learned how to polish the raw edges of a combat boot.
Making sense of the completely nonsensical: I worked for many years for a software company that actually ended up making products that, more or less, made sense. But when I first started there, my job was to write the descriptive sections of the business plans that helped our chairman find enough money to keep us going, quarter to quarter, while we tried to figure out how we were going to become "The Next Billion Dollar Software Company." (This was the early 1990's, when a billion dollar software company was a big deal.)
Most of my job entailed trying to make our company understandable to our potential investors. This was no small task, given that the company was an agglomeration of a bunch of small software and consulting companies, each of which (sort of) made sense on its own. Put it all together, it spelled WTF.
Just how do you rationalize software companies that make: financial planning applications; fixed income analysis tools; an accounting application; a marine shipping analysis tool. All built in different countries. That accounting app? Designed in and for Norway, takk. All built on different platforms, using different underlying technology.
Layer on the decision support consulting firm, the breakthrough object-oriented software factories, and the nifty development tools we were building...
All this in a 300 person company surviving on the miniscule revenue stream from our operating wings (i.e., the piddling little software companies and consultancies that actually went into the market and tried to make money), and on the kindness of strangers willing to throw in a million here, a million there, on a month-by-month basis.
But in order to keep tapping the checkbooks of those kind strangers, we had to have a business plan that spoke to the synergy among all our little wings. Virtual synergy became my specialty.
Then those kind strangers decided to band together and bring in a turnaround guy. In our first encounter, the first thing the turnaround guy said to me was, "When I read what you've written about the company, it almost begins to make sense."
It was then that it dawned on me that I'd spent the better part of a year creating paper synergy.
At least when I was a waitress at Durgin-Park, I learned how to arm-carry a half-dozen fully loaded prime rib platters.
Data-mining for fools gold: A couple of years ago, I had an opportunity to do some contracting work for a market research company. At first, it looked like a dream gig. The job was to comb through tons of largely qualitative research and summarize the key points, which were then presented to the firm's clients - mostly large consumer goods and services companies - in a nicely packaged PowerPoint.
All that wonderful qualitative research to comb through. For a couple of hours, I thought I'd died and gone to heaven.
Then I realized that the company expected me to not only get through the data in a couple of hours, but to produce a slick PowerPoint to go with it. Formatting mattered, neatness counted.
Talk about Lucy and Ethel on the assembly line....
I realized that, over time, I would have figured out the knack for skimming the data and extracting the nuggets. But would the nuggets have been the real gold, or the fools gold that caught my eye on the breeze-through.
All in all, I couldn't for the life of me figure out why the market research company didn't charge more for data analysis, which would have given us analyzers more time to really go through the great gobs of information they had in a thorough, thoughtful manner. And, more to the point, I couldn't figure out why the marketing folks in those consumer goods and services companies didn't just go through it themselves: there had to be a lot of gold in them thar hills. As a marketer, I know that I would have killed (metaphorically speaking) for such a rich information source of market feedback on the products I'd worked on over the years.
Then there was the hoopla with the PowerPoint formatting, which - it seemed to me - was more important than the actual content. As a content person who tends to go with one word being worth a thousand pictures, I knew that we weren't going to end up with a beautiful friendship and, indeed, I parted company with this company after my trial period ended.
At least when I was a grill cook I learned how to change the fryolator grease without scalding myself.
While I haven't actually had any skilled, work-with-my-hands jobs, I am certainly thankful for the many "real" jobs I had in my youth - often dirty, always hard. Everyone should have at least a couple of jobs along the way where they actually end up sweating.
And I'm also thankful now that, while I'm still a knowledge worker, most of the companies I work for make technology products that, if not necessarily understandable to someone outside of the world of technology, make a lot of sense to the folks they're aimed at. And produce real - not abstract - benefits and value for those folks.
Anyway, Matthew Crawford has a book out this week, “Shop Class as Soulcraft: An Inquiry Into the Value of Work.” From his essay in The Times, it sounds like it'll be worth a read.