What happens when we automate out all the truck drivers?

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A lot of people seem to be worried about future employment. In the U.S., you could argue that’s one reason Trump just won the Presidency. Ultimately, the states that put him over the top were Upper Midwestern states where jobs are automated out (or go abroad), and people get nervous. In an advanced capitalism, a lot of success and self-worth (and ability to do the things you want) are tied to money, and if future employment is zip zero zilch, that terrifies people. Trump channeled the fear — “bad hombres, raping our women” — and he won an election that he probably never should have won on “conventional” metrics alone. That, plus the “high achiever myth.”

What does future employment look like, though? I mean, do any of us really know? Probably not. But I can trace out a little bouncing ball for you. Automation is a real thing. That’s happening. It might be 50 percent. It may be higher. Speaking of Trump, those Carrier jobs he saved? They’re all getting automated anyway. See that super-cool new Amazon grocery store people were sharing on social media? That’s a huge automation play. Trucking — one of the best ways for a less-educated man to make a good salary — is getting automated out too.

So there are definitely some concerns about future employment here. That’s for sure.

What if we could take the supposed strategic revolution of the moment in business and use that to better our stock in future jobs? Let’s try.

Future employment and the role of data

Every company and their mom is trying to compete on data right now. Of course, because a lot of this is one company chasing what another company seems to be doing, most are doing it wrong. (The concept is called “loss aversion,” by the way.) Here’s a recent headline from Harvard Business Review: “Most Industries Are Nowhere Close To Realizing The Power Of Analytics.” You don’t even need to read the article. Just feast on the headline. Back in 2014, CIO Magazine was saying the same thing: execs love to discuss data, but often have no idea what to do with it.

Companies “compete on data.” Most do it wrong. Maybe there’s an employment solution here.

The second problem is that most businesses are rooted in the idea of more, more, more. “I need more revenue,” or “I need more growth.” It doesn’t work that way with data. More data just means more analysis paralysis and slower decision-making. That’s not how you get ahead — it’s actually how your ass gets disrupted.

And there’s a third problem.

Why we need data translators

It’s great if you can collect all this data. Good for you! It’s probably too much, but let’s gloss that over for a second. Let’s assume you collected the right, targeted data. Now what?

Most companies have a suite of decision-makers. There’s usually a tremendous amount of variability in their decisions, largely because they run silos and make decisions relative to those silos (and/or their own bonus). This leads to skewed decision-making processes on relatively simple ideas — and data is not often simple.

Then there’s the issue of time. These guys probably do have the time to learn and embrace this stuff, but if they admit that, somehow they feel less important. So they’ll never admit it. They’ll bark at everyone in sight that they don’t have time, it’s too much, too many rows, etc. They want someone to condense it into a presentation for them. This is how they feel comfortable.

Data translators are born.

MIT on data translators

Good article here called “Why Your Company Needs Data Translators.” (Pretty straight-forward title.) This is near the top:

Our work has included a series of research workshops to discuss trans-Atlantic and cross-sector issues around performance management in professional sports. A key issue that emerged from these meetings was the recognition of this consistent disconnect within performance management practice between “big data” analysts and the decision makers they support. This is evidenced by the predominantly dismissive attitude of many executive decision makers (general managers, head coaches, CEOs, COOs, etc.) to both the data itself and those responsible for delivering it — an attitude often born largely out of ignorance or fear. The research group believed that bridging this cultural gap would provide considerable competitive advantage to any organization concerned with high performance.

“Born out of ignorance or fear” explains about 82 percent of work, as an aside. (Cymbal noise.) But we’ve doing this with big data — sorry, “Big Data” — for years. We miss the crucial step: you need to be able to explain what the heck is going on to the person who can write the check.

But we can’t teach a truck driver to be a data scientist, right?

Who says we can’t? Data science is complicated, but if future employment hinges on it, I feel like a guy could go take a few classes. Maybe we could create apprenticeship or mentor programs at some companies. We have established data people tutoring new people to be “junior” versions of themselves. Now companies can develop little armies of data nerds, help future employment in the process, and maybe get closer to actually competing on data.

I try to think about work in different ways, and I also try to call out some managerial BS we’ve all experienced. If that kinda sorta interests you, I do a newsletter every Thursday. Feel free to join up.

This is largely a pipe dream in some respects because there’s about 174 steps that need to happen first. Some:

  • Determine what data needs to be tracked
  • Hire the right people
  • Know how to scrub the data
  • Reduce executive-level defensiveness that their “gut” isn’t driving the business anymore
  • Explain to data scientists that by mentoring blue-collar guys, that doesn’t mean their high-salary job will be replaced
  • Do all this as you run your business day-to-day anyway

Complicated tableau here, right? But we need to think like this for future employment.

Future employment: “We compete on data now!”

Had a job recently where the CEO simply announced in a meeting, to 225 people, “We compete on data now.” The big move that went with it? The CFO’s job title became “Chief Data Officer.” OK. So that meeting ended at about 10:55am on a Wednesday. At 11:02am, was everyone neck-deep in data projects? Of course not. They were doing the exact same thing they had been doing at 9:51am that day. You don’t lob strategy down from the tower. It needs to be aligned with daily tasks. When it’s not, there is subsequently no priority in that business. The executives become concerned with revenue and stakeholders, allowing middle managers to prioritize work in their own way. This creates many “sense of urgency” projects, all of which burn out employees and lead to turnover. Meanwhile, those urgent projects likely had not even a penny worth of ROI.

This is the cycle at most companies. And you see it everywhere with data. Executives don’t trust the data-gatherers. If the data doesn’t resonate with their gut, they bellow about how it was analyzed. They see rivals being successful with something, assume it’s data, and demand more more more from their people. Smart data people try to stop the train. “We don’t need more, we need the right targets…” They’re cut off. “No time for this, chasing Q2 CAGR!” People burn out. Job roles remain the same. No one is really competing on data.

There’s a better way, and that better way might be one of our best bets for future employment. If companies are serious about mining analytics, why not develop a way to find the right metrics and then coach people to translate those metrics to stakeholders?

What else would you add on future employment and data?

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Blogging, largely about work and how to improve it. How I make (some) money: http://thecontextofthings.com/hire-freelance-writer-ted-bauer/

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