Dealing centers profit
The problem is this: every business is divided into parts that are considered cost centers, and parts that are considered profit centers. Cost Centers are, well, everybody else.
You really want to be attached to Profit Centers because it will bring you higher wages, more respect, and greater opportunities for everything of value to you. If you want to work there, work for that.
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The tech industry is unique in that tech workers can work in both parts of the company. If you're working on Search at Google, for instance, you're working in a profit center; if you maintain servers at Citibank, you're pretty much working in a cost center.
The tricky thing, of course, is that if you work in data, the odds are good that you are considered a cost center. You aren't, after all, critical to anything that the business does.
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So what do you do in this situation? Dealing with the Cost Center Problem A few weeks ago, data scientist Randy Au wrote a piece about his experiences with exactly this problem. So what do you do to deal with this dynamic?
The answer, Randy says, is that you should keep a brag document. The best article I know on this practice comes from software engineer Julia Evans.
You can find her guide to writing brag docs here. Just make it sound exactly as good as it is! You should take the opportunity to explain the big picture at the top of the document e.
Brag documents also allow you to notice patterns in your work over a period of time. Evans prefers to write hers once every 6 months, instead of the recommended once every two weeks — the longer period allows her to see the bigger trends in her work. But Au takes this basic idea and adapts it for the specifics of a data role: In addition to maintaining a brag doc, Au has dates prefixed into the name of every file he creates in ISO format, because of course he would.
This includes every analysis, every query, every spreadsheet, dealing centers profit every document he works on as part of his job. This dealing centers profit a lot harder than it sounds — he notices that often, product teams will take months before they launch anything that produces a measurable impact.
But at the end, Au closes with a sobering note: Will these methods save you if large staffing cuts are coming? Probably not. Being let go just meant the organization was willing to fly blind without detailed analytics insights for a period of time.
And the reality of data-oriented careers is that they are — in the vast majority of cases — a career in a cost center. The implication here is that you have to be aware of the cost center problem if you want to embark on a career in data.
Dealing with the Cost Center Problem
This means all sorts of things: it means things like evaluating your employer at the interviewpicking healthy companies that are unlikely to do layoffs hard!
As Francois Chollet said in a Twitter thread recently: I think it's clear that for many smaller companies that invested in deep learning, it turned out not to be essential and got cut post-Covid as part of downsizings.
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