This article delves into what tribal knowledge is, its impact on organizations, and a CTA (call to action) for leadership to affectively manage it.

The basic LMGTFY (let me google that for you) meaning for tribal knowledge would be: ”unwritten, unspoken, and undocumented institutional knowledge within an organization held by specific individuals and groups“.

🦉 Building knowledge

One of the many things organizations do is build a lot of tribal knowledge base, on a variety of topics from choosing technological decisions to executing leadership strategies.

One may ask, how does an organization build this so-called tribal knowledge?

Historically, based on my observations, much of this aggregates when teams (and organizations) move at unusually faster speeds to achieve goals necessary to meet crucial timelines, sacrificing aspects necessary for long-term growth. Much of the decisions are closed-loop, to execute faster.

Why would an organization operate in such a manner, building short-term wins while sacrificing long-term growth?

🏦 Taxes

Everyone should have heard about taxes, which based on different countries can vary, but generally sit around 22 to 37% for STCG (short-term capital gain) and 0 to 20% range for LTCG (long-term capital gain) depending on the income range.

If we apply this analogy to taxes, strategically converting STCG to LTCG by holding and spreading the LTCG gains over multiple years is the simplest tax-minimizing strategy.

After all, the opposite doesn’t make sense because STCG is taxed fairly higher than LTCG - so gravitating towards long-term would seem more logical.

So why sacrifice long-term engineering growth, and miss out on the force-multipler?

💬 Anecdote

Before proceeding, I want to give an anecdote here, and I’ll need to set some background for this one.

My team would organize a small pizza party on Fridays (when WFH wasn’t the new norm) just as a team-building exercise for the organization. Being naive and young, I went around the office open-plan space, trying to take note of everyone’s pizza preference, to maximize gratitude.

Little did I realize I was slowly falling into the sandpit of indecision.

My manager came up to me and whispered in my ear, “Don’t try to achieve consensus execute a popular choice -immediately!”

I wasn’t convinced back then, but eventually executed the “popular decision”. After all, it was the exact opposite of what I was trying to do.

But turns out that it’s pretty hard to navigate options at times, and people can have a variety of preferences - but I’d suggest folks try this one. The “popular decision” back then was just Garden veggies, Pepperoni, and cheese - easy!

🧮 The 37% rule

There’s a mathematical concept that suggests one can optimally stop at 37% to choose a good option - I feel humans pick this intuitively at times, but I do suggest looking up this 37% concept if it seems interesting.

This might also help explain why people often don’t wait for everyone to agree before making a decision. It suggests that there’s a point where it’s better to stop gathering opinions and just choose.

There’s also a subtle difference in optimal stopping vs achieving consensus, but these are related concepts and optimal stopping can be used for reaching consensus.

🦥 Moving on from taxes, anecdotes, and the 37% rule

Organizations at times bring in weighted choices from their “trusted inner circle” of members, which most possibly includes management & select ICs, and execute decisions by achieving consensus within a group.

This restricted flow of information which happened behind “off-the-record”, often taints organizations as “closed organizations”, but helps them make faster decisions and brings in bias for action.

Information flow that happened behind closed doors must seem like high entropy to the outside universe observing it.

Organically breaking this is a choice leadership involved in this exchange should share and perhaps document - otherwise, the seeds of tribal knowledge start to be sown!

🌊 Call to action

Some organizations choose to let this information flow down like a crystal lattice using mentorship and mentor-mentee relationships. XFn (cross-functional) collaboration is something that when intertwined with a mentorship mechanism can help spread engineering knowledge across various verticals in an organization as well.

Knowledge also starts contributing to “becoming tribal” when people change teams or when people leave companies. This is where encouraging structured documentation and formal knowledge transfer sessions are helpful, but not perfect.

As you’d expect, the lack of documentation within such areas also causes a higher onboarding time & increasing defect rate. Surely having SMEs (Subject matter experts) in your organizations helps alleviate this, but this just increases reliance on individuals & longer resolution times.

As innocuous as it may seem, this obstructed information flow starts hampering the productivity and culture of the organization.

🔚 Ending

When engineering seeks to solve problems, this knowledge may be a crucial aspect in determining solutions.

So help engineering help you.