Glossary
By the Glue Team
A knowledge silo is a situation where critical information, expertise, or understanding about a system, process, or business function exists in isolation—typically trapped within one person, team, or department—and is not shared across the organization. Knowledge silos create communication barriers, reduce organizational agility, and increase risk when the knowledge holder becomes unavailable.
Knowledge silos form when valuable information is not documented, accessible, or communicated across organizational boundaries. Unlike information that's intentionally restricted for security reasons, knowledge silos are typically unintentional barriers created by organizational structure, lack of documentation, or communication gaps.
In software engineering, knowledge silos commonly appear as:
Knowledge silos create tangible business problems:
Operational Risk: When critical knowledge is siloed, the organization becomes dependent on that person. Turnover, illness, or vacations create crises. Projects stall when the knowledge holder is unavailable. Some teams report 2-3 week delays when their expert is on vacation.
Slow Decision-Making: Decisions get delayed because only one person has the context needed. Meetings need to be scheduled. Slack messages go unanswered. Cross-functional collaboration becomes difficult when team members don't understand each other's domains.
Poor Quality Outcomes: When knowledge exists only in one person's head, decisions made independently of that knowledge often duplicate effort, miss nuances, or create conflicts with existing approaches. Feature teams build things that should have integrated with existing systems but didn't because they didn't know those systems existed.
Reduced Innovation: Silos prevent cross-pollination of ideas. One team's solution to a problem might have solved another team's challenge, but they never connected because knowledge didn't flow between teams.
Higher Onboarding Cost: New team members take longer to become productive in siloed organizations. New hires need to arrange numerous 1-on-1 meetings with knowledge holders instead of finding answers in documentation or code.
Employee Burnout: People become bottlenecks. They're constantly interrupted with questions. They feel irreplaceable but trapped. This is a leading cause of burnout and departure, ironically creating turnover in roles that were supposed to be stable.
Knowledge silos develop gradually, often through no one's fault:
Rapid Growth: When teams scale quickly, communication structures don't keep pace. The first three engineers know everything about the product. By the tenth engineer, knowledge gaps emerge. By fifty, silos are entrenched.
Lack of Documentation: When systems are documented in code but not explained in prose, only people who read that code deeply understand the "why." Documentation is harder than coding, so it often gets skipped.
Organizational Structure: Teams organized by function (backend, frontend, infrastructure) naturally silo knowledge. Information that should be shared across teams stays within teams. Reorganizations often shuffle which knowledge is siloed without eliminating silos.
High Context Work: Complex domains (machine learning, distributed systems, security) are hard to document and understand without deep expertise. Knowledge holders become gatekeepers because only they truly understand the domain.
Turnover: When someone leaves, their undocumented knowledge leaves with them. New hires recreate that knowledge slowly. Sometimes recreated knowledge is worse—missing edge cases or optimizations the original person knew about.
Fragmented Tools: When knowledge lives in different places (Slack, Notion, GitHub, Confluence, someone's laptop), it's effectively inaccessible. No one knows where to look, so they give up and ask the expert.
Reducing silos requires intentional effort:
Documentation: Systematic documentation of systems, decisions, and processes. This is ongoing work, not a one-time project. New documentation should be part of the definition of done for features.
Knowledge Sharing Sessions: Regular presentations where knowledge holders teach others. Pair programming is high-leverage but doesn't scale—recorded sessions or documentation supplements scaling.
Rotation: Rotating people through different roles exposes them to different knowledge domains and prevents any single person from being the only expert.
Clear Decision Records: When architectural or product decisions are made, document the decision, the options considered, and why the chosen option was selected. This is invaluable context for future decisions.
Cross-Functional Collaboration: Deliberately bring teams together. Have backend engineers sit in on frontend planning. Have product managers attend technical design reviews. Knowledge flows when people are in the same rooms.
Accessible Tools: Consolidate where knowledge lives. One wiki, one Q&A tool, one source of truth. Make it searchable and accessible.
Mentoring and Pair Programming: Direct knowledge transfer works. Schedule it intentionally, don't hope it happens organically.
Glue helps break down knowledge silos by making codebase knowledge transparent and accessible. Instead of knowledge existing only in the heads of senior engineers, Glue makes it queryable. Product managers can ask questions about the codebase directly. New developers can understand feature implementations quickly. The "why" becomes visible through code intelligence.
"Silos only happen in large companies." False. Small teams with single-engineer features develop silos quickly. A 10-person startup with one expert on payments has a dangerous silo.
"Good documentation solves silos." Partly true. Documentation is necessary but not sufficient. Documentation that no one reads doesn't break silos. You need accessibility, searchability, and a culture where people check docs first.
"Silos are fine as long as we have backups." False. Even if two people know something, that's still a silo—just with lower risk. True knowledge distribution means three or more people understand critical systems.
"We can eliminate silos by hiring more experts." Hiring helps but won't solve it alone. Unless you're transferring existing knowledge to those new people, you're just adding more people to the silo.
Bus Factor: How many people would need to leave before critical knowledge is lost. Bus factor is the quantification of silo risk.
Technical Debt: Undocumented code is a form of technical debt. It increases silos because knowledge is trapped in code.
Developer Onboarding: Organizations with silos have slower onboarding because new people need to learn from knowledge holders rather than reading documentation.
Code Complexity: Complex code with poor documentation creates silos because only the original author understands it.
Q: Is some level of specialization the same as a knowledge silo? A: No. Specialization is healthy—having someone deep in machine learning or distributed systems is valuable. A silo is when that knowledge is inaccessible to others. The specialist should be able to document their approach, teach others, and help the team understand key concepts.
Q: How do you know if your organization has knowledge silos? A: Ask: "For each critical system, could someone other than the owner maintain it tomorrow?" If the answer is no, you have a silo. Also watch for repeated questions—if the same question keeps getting asked, that's knowledge that should be documented but isn't.
Q: Can you have too much knowledge sharing? A: In theory, no—though in practice, excessive meetings or documentation requirements can slow teams down. The goal is efficient knowledge sharing: right information, right people, right time.
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