By Priya Shankar, Head of Product at Glue
Every engineering team has that one person who "just knows" how the billing system works. Or the one developer who built the authentication layer three years ago and is still the only person who can debug it. This is what knowledge silos look like on an engineering team, and they are quietly destroying your productivity, your retention, and your ability to ship.
I have been that person. Early in my career, I was the sole owner of a critical service. It felt good to be indispensable until I wanted to take a vacation and realized nobody could cover for me. That experience shaped how I think about knowledge distribution, and it is why I believe silos are the most underestimated risk in engineering organizations.
What Are Knowledge Silos
Knowledge silos form when critical information about your codebase, architecture, or processes lives in one person's head instead of being distributed across the team. They are not a planning failure. They are a natural consequence of how software gets built.
When one engineer works on a module for months, they accumulate context that is hard to transfer: the reasoning behind design decisions, the workarounds for vendor bugs, the implicit contracts between services that are not documented anywhere. This context becomes a silo. Not because anyone hoarded it, but because nobody made its distribution a priority.
Knowledge silos in engineering teams typically take three forms:
- Code ownership silos. One person is the only contributor to a critical module. Their departure would leave the team unable to maintain or extend it.
- Architectural silos. The reasoning behind system design decisions lives only in the architect's memory. New team members inherit the structure but not the "why."
- Process silos. Deployment procedures, incident response runbooks, and debugging techniques that only certain team members know how to execute.
The common thread is fragility. Each silo represents a single point of failure that your organization has accepted, usually without realizing it.
The Real Cost of Silos
Knowledge silos are expensive, but the cost is spread across so many areas that it rarely shows up in any single metric.
Slower development. When only one person understands a part of the codebase, every change to that area becomes a bottleneck. Other engineers either wait for that person or attempt changes without sufficient context, leading to bugs. According to research from Jellyfish, engineering teams spend 23-42% of their development time on technical debt and maintenance, a range that widens significantly when knowledge is concentrated rather than shared.
Constant interruptions. The "expert" becomes a human search engine. Every question about their domain pulls them out of their own work. Gloria Mark's research at UC Irvine found that it takes an average of 23 minutes to fully regain focus after an interruption. If your domain expert gets interrupted 4 times per day, that is over 90 minutes of productive time lost to context switching, every single day.
Expensive onboarding. When knowledge lives in people instead of systems, every new hire needs extensive hand-holding. Cortex's engineering research estimates that the fully loaded cost of onboarding a new developer, including lost productivity from both the new hire and their mentor, can reach $240K. Knowledge silos inflate this number because there is no documentation or tooling to accelerate ramp-up.
Attrition risk. Engineers stuck in silos burn out. They cannot grow because they are trapped maintaining their domain. And when they leave, the organization takes a disproportionate hit. I have seen a single departure set a team back by months because the departing engineer was the only person who understood a critical data pipeline.
Bus Factor: The Ultimate Risk
The bus factor is the number of team members who would need to leave before a project stalls. A bus factor of 1 means one departure could cripple a critical system. It is the most honest metric for measuring knowledge concentration.
Most teams have a bus factor of 1 for at least some part of their codebase. They just do not know which parts.
This is not hypothetical risk. People leave, take parental leave, get sick, or simply transfer to a different team. When the only person who understands the payment reconciliation service is unavailable, and a critical bug surfaces at 2 AM, the bus factor stops being an abstract concept and becomes an incident.
Measuring Your Bus Factor
The challenge with bus factor is that it is hard to measure through conversation alone. Managers ask "who knows about X?" and get reassuring but vague answers. The real picture only emerges when you look at the code itself.
Git history reveals ownership patterns that surveys miss. If 90% of commits to a directory come from one author, that is a measurable bus factor of 1, regardless of what the team reports in a meeting.
This is where automated codebase analysis becomes critical. Glue analyzes your repository's commit history, file ownership patterns, and contribution distribution to calculate bus factor scores at the file, module, and service level. Instead of guessing where your knowledge risks are, you can see them on a dashboard.
A Stripe developer survey reported that engineers spend an average of 17 hours per week on maintenance tasks, much of which involves working in areas they do not fully understand because knowledge was never properly distributed. Making bus factor visible is the first step toward fixing this.
How to Break Down Silos
Breaking down knowledge silos is not about writing more documentation, though that helps. It requires structural changes to how your team works with code and shares context.
1. Make Ownership Visible
You cannot fix what you cannot see. The first step is mapping who knows what in your codebase. This does not mean asking people. It means analyzing contribution patterns, review history, and code ownership data to build an objective picture.
Glue's ownership analysis does this automatically. It shows you which files and modules have concentrated ownership, where bus factor risks exist, and how knowledge distribution has changed over time. This visibility turns "we should share knowledge more" from a vague goal into a specific, actionable plan.
2. Implement Deliberate Pairing
Code reviews are not enough. Reading someone else's code is different from building alongside them. Schedule deliberate pairing sessions where the domain expert works through changes with a teammate, explaining the implicit context that would never make it into a pull request description.
Target the highest-risk areas first. If Glue shows that one engineer owns 85% of your API gateway, schedule weekly pairing sessions on that module until at least two other engineers can confidently make changes.
3. Rotate Ownership Intentionally
Some teams rotate on-call duties but never rotate code ownership. Intentional rotation means assigning tickets in siloed areas to non-experts, with the expert available for support. This is slower in the short term. It is essential for long-term resilience.
Track the impact over time. As knowledge spreads, you should see bus factor scores improving across your codebase. Without measurement, rotation feels like it is slowing the team down. With measurement, you can see the risk reduction it produces.
4. Build Context Into Your Tools
Tribal knowledge persists because the cost of sharing it is high and the tools for capturing it are inadequate. The best way to break silos is to embed context directly into the workflows your team already uses.
When an engineer opens a file they have never touched, they should immediately see who owns it, what it connects to, and what has changed recently. When a PM creates a ticket, they should see which parts of the codebase it will affect and whether the team has sufficient coverage in those areas.
Glue provides this ambient context across your development workflow. Instead of requiring engineers to proactively seek out knowledge, it surfaces relevant codebase intelligence where decisions are being made.
5. Measure and Track Progress
Silo reduction is not a one-time project. It is an ongoing practice. Set quarterly goals for bus factor improvement. Track ownership concentration metrics over time. Celebrate when a previously siloed module gains its second or third confident contributor.
The teams that break silos successfully treat knowledge distribution as a first-class engineering metric, not a nice-to-have that gets deprioritized when deadlines approach.
See where knowledge silos hide in your codebase. Try Glue free and get a bus factor score for every module in your repository.
Frequently Asked Questions
What are knowledge silos in engineering?
Knowledge silos in engineering occur when critical understanding of code, architecture, or processes is concentrated in one or a few team members rather than being distributed across the team. They form naturally as engineers specialize in specific areas over time. The result is that certain parts of the codebase become fragile single points of failure that are difficult to maintain or extend without the knowledge holder's direct involvement.
How do knowledge silos affect productivity?
Knowledge silos reduce productivity in multiple ways. They create bottlenecks where work queues up behind a single expert. They cause frequent interruptions that cost an average of 23 minutes of focus recovery per occurrence. They inflate onboarding costs because new hires cannot ramp up independently. And they increase the risk of bugs when non-experts are forced to make changes in siloed areas without sufficient context.
How do you break down knowledge silos?
Breaking down knowledge silos requires a combination of visibility, structural changes, and ongoing measurement. Start by using tools like Glue to map ownership concentration and bus factor scores across your codebase. Then implement deliberate pairing sessions in high-risk areas, rotate ticket assignments to spread context, and build codebase intelligence into your daily workflows. Track bus factor improvements quarterly to ensure sustained progress.