O-Rings and The Myth of The 10x Engineer

Another wonderful post by Adrian Colyer (of The Universal Scalability Law for Organizations) covering another topic that is near and dear to my heart:

The O-Ring Theory of DevOps

In this post, Adrian applies Michael Kramer’s O-Ring Theory of Economic Development to the realm of DevOps. but in my mind, the most interesting applicability of this theory is at a context somewhere between the massive scale of economic development and the targeted scale of DevOps: the context of organizational operations and talent management in particular.

In a nutshell the O-ring theory, inspired by the 1986 Challenger disaster where a single failure of an O-ring lead to catastrophic results, shows how small changes in quality lead to massive changes in output/productivity.

The theory applies in scenarios that meet the following three conditions:

  1. Production depends on completing a series of tasks
  2. Failure or quality reduction of any one task reduces the value of the entire product
  3. You can’t substitute quantity for quality (Adrian’s example: two mediocre chefs can’t replace a great chef in making an amazing meal)

In Adrian’s example, quality of each step/person is measured by a coefficient (q) between 0 and  1, and the process has 10 steps. Improving the quality in each step from 0.5 to 0.75, a 50% improvement, yields a 600% improvement in output.

I’d argue that the entire process of software development and delivery meets the three conditions for the O-ring theory to apply. You can probably generalize this more broadly to the entire organization/company, but satisfying the 2nd condition may be more challenging.

Adrian’s implications, applied to talent will look something like the following:

  1. A single “weak link” overall output dramatically. It’s not enough to hire top talent for some roles. You need to be good across the board.
  2. Small differences in quality quickly compound to make very large differences between the performance of the best-in-class and the rest
  3. The better you already are, the more value you get from improving your weaknesses. Conversely, if you’re fairly poor across the board, you won’t get as high a return on investment on an improvement in one specific area as a company with a higher overall level would. These forces tend to lead to ‘skill-matching’ – fairly uniform levels of performance across the talent pool of a given organisation

A different way to articulate #3 is that the more great workers a company already has, the more incremental value another great worker adds. This, to some degree, rationalizes some of the behaviors we’re seeing in the market where top performing companies are willing to pay more for top talent – it’s not just because they can, it’s because they should…

But it also calls into question another practice that’s becoming more common in these top performing companies. Some of them, like Google, use the growing proof that talent is not distributed normally (which I covered here) to justify paying their employees “unfairly” and legitimizing substantial (3x-5x and sometime even more)  pay differences for employees at the same level. But if the 10x difference in output can be explained, not by the existence of a single 10x engineer, but by the existence of 10 engineers that are only 10% better than average, does that still make sense?


O-Rings and The Myth of The 10x Engineer

Why Strategy Execution Unravels

I don’t reference a lot of HBR articles in this blog, but I came across this one, by Donald Sull (et. el) recently and it really struck a cord with me, since it talks about strategy execution first and foremost from an organizational perspective:

Why Strategy Execution Unravels – And What to Do About It

Sull and his co-authors make their case by debunking 5 strategy execution myths:

Myth 1: Execution equals alignment – alignment down the chain-of-command is a solved problem and most corporate processes to manage it (MBOs, tying bonuses to goals, etc.) are working well. The true, unsolved problem is around cross-departmental coordination:

“Only 9% of managers say they can rely on colleagues in other functions and units all the time, and just half say they can rely on them most of the time… When managers cannot rely on colleagues in other functions and units, they compensate with a host of dysfunctional behaviors that undermine execution”

Myth 2: Execution means sticking to the plan – strategy execution fails when companies “stick to the plan” too much, rather than seize fleeting opportunities  that support the strategy. Perhaps the most extreme case of this behavior, is disinvestment from opportunities that did not pan out as expected:

“Companies also struggle to disinvest… Top executives devote a disproportionate amount of time and attention to businesses with limited upside and send in talented managers who often burn themselves out, trying to save businesses that should have been shut down or sold years earlier”
Myth 3: Communication equals understanding – there’s a growing appreciation for the criticality of organizational clarity in driving strategy execution and the important role that internal communication plays in creating that clarity. However, internal communication initiatives often center around optimizing the wrong metric:
“Not only are strategic objectives poorly-understood, but they seem unrelated to one another and disconnected from the overall strategy… Part of the problem is that executives measure communication in terms of inputs, rather than by the only metric that actually counts – how well key leaders understand what’s communicated”
Myth 4: Performance culture drives execution – this issue is tied directly to the first myth. If the culture emphasized individual performance over collaboration and coordination, it gets in the way of removing the true roadblocks for execution:
“The most pressing problem with many corporate cultures, however, is that they fail to foster the coordination that, as we’ve discussed,  is essential to execution. Companies consistently get this wrong. When it comes to hires, promotions and non-financial recognition, past performance is two or three times more likely than a track record of collaboration to be rewarded
Myth 5: Execution should be driven from the top – in complex organizations, effective execution requires dealing with a constant stream of tough trade-offs: should we invest the time in coordinating with another department at the cost of losing a fleeting opportunity? should we say “no” to a client request that’s misaligned with the strategy at the cost of losing revenue? The leaders closest to the situation are the ones who can respond the quickest and are best positioned to make the tough call:
“Frequent and direct intervention from on high encourages middle managers to escalate conflicts rather than resolve them, and over time they lose their ability to work things out with colleagues in other departments. Moreover, if top executives insist on making the important calls themselves, the diminish middle-managers’ decision making skills, initiative, and ownership of results”
At the end of the day, Sull and team paint a rather grim picture of a commonly used myth-driven strategy execution approach. But if we choose to look at the glass half full, they also outline the path for a more effective strategy execution approach:
  1. Build businesses processes that strengthen cross-departmental coordination and collaboration
  2. Orient execution effort around agility, and seizing strategy-aligned opportunities
  3. Measure the effectiveness of internal communications in driving the understanding of strategic objectives
  4. Create an organizational culture that balances individual performance and collaboration
  5. Provide middle managers with the necessary technical competency and organizational clarity to make the tough decisions themselves



Why Strategy Execution Unravels

The Bell Curve Is A Myth

This following post by Josh Bersin (one of Reid Hoffman’s co-authors of “The Alliance” which I covered in the previous post) made the rounds a few months back and is still top-of-mind for me:

The Bell Curve Is A Myth

The gist: most common HR practices (performance reviews, promotions, etc.) pre-suppose a normal-distribution of performance across the employee base. However, careful studies have shown that real performance looks more like a power-distribution than a normal distribution. Many of the common HR practices “break” under those assumptions.

Bersin provides references to the academic papers that drew this conclusion, yet none of them hypothesizes why performance looks the way it does. Here’s my thesis: in the general/broader population, performance/mastery of a given skill does distribute normally. However, in almost any professional setting, we apply some sort of screening process – we intentionally try to hire the people who are “above average” (if not the top 5-10%) who are most qualified for the position. Since we don’t just pick people at random, there’s no reason to expect a normal performance distribution post-screening. We would expect to see something that resembles the right-most portion of a normal distribution – which also looks a lot like a power distribution.

These articles make a compelling case for why the current HR practices don’t make a lot of sense under these assumptions. But they fail to propose a prescriptive way to change them in the right direction.

What do you think? If we assume hat performance (at least in professional settings) follow a power-distribution, how should we change some of the key HR practices?


The Bell Curve Is A Myth