In grad school we had a forced module on information systems in HR in which one case was about RBC and their new formerly-Bain Director of HR Data and the 3 or 4 year effort to “quantify” labor. Cases should be taken with some skepticism but let’s assume the case was correct.

I wanted to tear my hair out. Not only was the output or ultimate deliverable of this gargantuan activity an infographic of largely non falsifiable random words (trustworthy, reliable, predictable, etc etc), I did a quick back of the envelope that the approach to repeatedly solicit feedback on every employee from every business line and every customer they intersect could generate 5 trillion outbound survey “friction” onto their customers and stakeholders in a given year give or take 2 orders of magnitude. What does that cost in terms of non-enjoyment? Does it even work, and at what response rate at that scale? And what does it cost to maintain, since surely the Canadian treasury at least sort of requires or at worst encourages banks to employ trustworthy employees. Worse, how implementable are the outcome and/insights? For a real open requisition is evaluating “trustworthiness” at time of hire possible? Is it possible to compare two candidates on a relative or absolute basis on this metric? And if they are hired, going forward how is that tracked? More surveys?

Perhaps the case wasn’t as bad, and I’m sure the OP HB post of this paper has subtleties in survey design or methods I don’t appreciate, but just to pick on them by cherry picking the first few attributes that stick out: passionate, systemic, data driven, focused, hardworking, persevering, etc?!?!?

Worse, where’s the actual data? Like when Google apparently decided to get rid of middle management it felt like a reversible scientific experiment with measurable outcomes and metrics. This doesn’t. For example, from personal experience, in investment banking being hard working especially at the very lowest and newest ranks is similarly lauded and required and emphasized. But with a fairly simple app that tracks second level activity of which app is open and when and can assign a rule a rule based score, when reviewing a few years of data, the data contradicted the folsky saying.

The data showed above XX hours per week for me was non-restorative, and further above XX hours would create some incremental inefficiency increase, and above even that was a “tech debt” like factor which later required recuperation (which could also be calculated but maybe that was an accident). In English: if (simple numbers) working 10 hours leads to 8 hours of work and 2 hours of goof off for some productivity, maybe working 14 hours is 10/4, and maybe working 17 is 11/6 and also requires 2 hours of rest at a subsequent trailing period. Depending on what’s going on, maybe it makes sense to do that, maybe it doesn’t. The software engineer view would be much more interesting.

It would be fascinating to compare even arbitrary metrics of consenting individuals across a large pool. Who gets the biggest bonus relative to hours worked? What time do peak performers come in and leave at and are there any wfh days? Even trivial but theoretically transferable insights would help: “what percent of top performers drink no caffeine or tea” or “pair programming and velocity”. Heck, buzzfeed baiting analysis like “do top engineers type faster? A 10 part slideshow on why you should switch to a mechanical Dvorak keyboard” or “Can you type XYZ words per minute? Find out if you are dragging your team down”.

Anything but more lengthy repetitions of “trust” and “respect” and whatever the word of the day is.