Rendering Knowledge [Snowden]

I’ve been thinking a lot about knowledge and context recently. Specifically, when it comes to job interviews. We’re trying to create an experience that enables candidates to demonstrate their knowledge and therefore their fit for a certain role. And yet, it is easier said than done.

I first came across this issue, watching a talk by Jabe Bloom. In the five years since it was given, I must have watched it close to a dozen times, which is extremely unusual in my case. It’s probably one of the most knowledge-packed talks that I’ve ever watched and I’m still unpacking bits and pieces of it.

In his talk, Jabe references Dave Snowden’s work around knowledge management, which I was able to trace back to this short post that’s now a decade old:

Rendering Knowledge

Based on a more detailed paper, which I wasn’t able to find (yet), Dave lays out his 7 principles of knowledge management:

  1. Knowledge can only be volunteered it cannot be conscripted
  2. We only know what we know when we need to know it
  3. In the context of real need few people will withhold their knowledge
  4. Everything is fragmented
  5. Tolerated failure imprints learning better than success 
  6. The way we know things is not the way we report we know things
  7. We always know more than we can say, and we will always say more than we can write down

See more detailed descriptions in the original piece, but I find these to be quite profound. #2 and #7 are particularly interesting in the context of interviews…

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Rendering Knowledge [Snowden]

Deciding how to decide


Decision-making was and will likely continue to be a major challenge in every collaborative effort.

A big complication that often gets in the way of making good decisions is deciding how to decide. Meaning, what decision-making process one should follow. Deciding how to decide is difficult because there is no one-size-fits-all decision-making process. Picking the right one depends on the situation.

It’s a topic that I’ve been grappling with for quite a while and shared some interim insights and structures here:

It’s always fun to be able to document how my thinking is evolving and the trigger to actually putting pen to paper on this one was a good post by the folks at Coinbase:

However, the real breakthrough in my thinking was due to a cool micro-site that was put together by the folks at NOBL called “How Do We Decide”. In it, they’ve identified 8 different types of decision-making processes and about a dozen situational factors that will lead you to favor one type over the other.

Source: howdowedecide.com

Overwhelmed by the number of different processes and potential situational permutations, I tried to come up with a simpler heuristic to match a certain situation to its optimal decision-making process.

As part of my search, I decided to re-read a McKinsey whitepaper I came across a while back called “Untangling Your Organization’s Decision-Making”. While their suggested set of decision-making processes didn’t quite land with me, the taxonomy they’ve used to classify the various types of situations rang true:

Source: McKinsey

Which led me to my big a-ha moment:

Decision-making processes consist of two core stages (and a few additional ones at the beginning and end): 

  1. Identifying and exploring various options
  2. Making the decision (choosing between the options) 

The optimal process for each of the core stages depends on different attributes of the situation at hand

At the end of the day, decision-making processes differ from one another in how collaborative they are. Other attributes of the process, such as the speed in which the decision gets made or the amount of buy-in that’s achieved are a byproduct of that.

Finding the right decision-making process seems tricky when we force ourselves to couple the level of collaboration in both of the core stages. Since the optimal process is driven by different attributes, a certain level of collaboration may be a good fit for one stage but not the other. We can be more collaborative in identifying and exploring options, and less collaborative in making the decision (the “consult” option in my original post), do exactly the opposite, or be just as collaborative in both, depending on the situation.

The less familiar we are with the situation, the more collaborative we should be in identifying and exploring options

To assess our level of familiarity we should ask ourselves:

  1. Is this a decision that we’re making frequently? (more frequent = more familiar)
  2. How clear are the options? (clearer = more familiar)
  3. How available is the information required for identifying/exploring the options? (more available = more familiar)
  4. How distributed is the expertise required for identifying/exploring the options? (less distributed = more familiar)

The higher the impact of the outcome, the more collaborative we should be in making the decision

I’m hoping to improve this part of the framework, but for the time being, to assess the level of impact we should ask ourselves:

  1. What would be the breadth of the outcome? (more people impact = more impact)
  2. What would be the depth of the outcome? (more profound impact = more impact)
  3. How reversible would the outcome be? (less reversible = more impact)

As the impact increases, we should opt for a more collaborative decision-making process: from a single decision-maker, through consent and democratic, to consensus.


I found applying different levels of collaboration to the two stages extremely liberating. It provides me with a more nuanced way to tailor the decision-making process to the situation and a stronger sense of certainty that I’m using a process that fits the situation. However, it’s by no means a silver bullet. The challenge is and will continue to be in assessing the levels of familiarity and impact and picking the appropriate transition points from one process to the other.

Deciding how to decide

The XY Problem [Chen]

Distinctions are a powerful concept. Labeling a pattern makes it easier to identify it and respond to it. And that’s exactly what Lily Chen did for me with

The most common problem I’ve seen in product/engineering process

In this short piece, Lily gave a label to a common pattern that I’ve seen time and time again, in much broader scope than the product development one that Lily’s piece is focused on.

The XY Problem — is asking about your attempted solution rather than the actual problem. You are trying to solve problem X, and you think solution Y would work, but instead of asking about X when you run into trouble, you ask aboutY

The best way to avoid the XY Problem, other than simply being aware of it, is to get into a habit of asking “why”. As Lily suggests, “behind every what there’s a why”. In any problem-solving collaboration, don’t start looking for solutions before you’ve moved from the default starting spot in the What Stack an everyone understands at least a couple of “whys” up the stack.

The XY Problem [Chen]