Knowledge risk brief
Generated 27 August 2026, 14:13 UTC across 220 people, indexed from codeowners, confluence, git, github, jira, org-csv, pagerduty, slack.
Summary
Where this company's knowledge rests on too few people. Each figure below jumps to the detail behind it.
Concentrated subjects (all 25)
Where an expert is marked not lately, they know the subject but have stopped working on it. That is a sharper finding than concentration alone: measured against a real project, a lead who had already stopped was less than half as likely to still hold the subject six months on.
A subject is concentrated when most of its expertise sits with one or two people. Bus factor is how few people together hold the majority of it, so a bus factor of one means a single departure takes the topic with it.
| Topic ▾ | Level ▾ | Bus factor ▾ | Top expert holds ▾ | Who holds it ▾ | Also covers |
|---|---|---|---|---|---|
| kafka-lag | critical | 1 | 83% | Stanley Lewis (83%), Dinesh White (17%) | |
| billing-retries | critical | 1 | 98% | Stanley Lewis (98%), Dwight Luciano (2%) | |
| sso-login | critical | 1 | 100% | Stanley Lewis (100%) | |
| search-indexing | critical | 1 | 100% | Lalo Goodman (100%) | |
| data-warehouse | critical | 1 | 100% | Walter Schrute (100%) | |
| payroll-taxes | critical | 1 | 100% | Michelle Schrute (100%) | |
| contract-review | critical | 1 | 100% | Carlton Halpert (100%) | |
| certificate-renewal | elevated | 2 | 75% | Stanley Lewis (75%), Hector Wexler (12%), Chalky Rosetti (12%) | |
| vacation | elevated | 2 | 75% | Stephanie McGill (75%), Holly Dunn (25%) | |
| cdn-caching | elevated | 2 | 69% | Stanley Varga (69%), Chalky Gilfoyle (15%), Holly Fring (15%) | |
| api-ratelimits | elevated | 2 | 69% | Lalo Vance (69%), Kevin Lewis (15%), Joey Butler (15%) | |
| health-benefits | elevated | 2 | 47% | Jared Capone (47%), Stephanie McGill (37%), Gale Malone (16%) | |
| laptop-hardware | elevated | 2 | 67% | Monica Hanneman (67%), Andy Malone (15%), Will Lewis (15%), Saul Varga (2%) | |
| kubernetes-deploys | elevated | 2 | 79% | Walter Bream (79%), Jim Schroeder (21%) | |
| oncall-paging | elevated | 2 | 76% | Steve Rosetti (76%), Will Varga (21%), Kevin Salamanca (2%) | |
| hiring-interviews | elevated | 2 | 78% | Laurie Schroeder (78%), Russ McGill (22%) | |
| terraform-state | elevated | 2 | 72% | Erin Hale (72%), Stanley Varga (28%) | |
| image-uploads | elevated | 2 | 69% | Meyer Wexler (69%), Lalo Vance (28%), Oscar Dunn (3%) | |
| onboarding-paperwork | elevated | 2 | 72% | Will Salamanca (72%), Monica Hanneman (28%) | |
| database-migrations | ok | 3 | 45% | Gale Malone (45%), Walter Bream (23%), Michelle Pinkman (15%), Gavin Hamlin (15%), Angela Thompson (2%) | |
| mobile-releases | ok | 3 | 46% | Richard Belson (46%), Steve Rosetti (23%), Erlich Hale (15%), Mike Bighetti (15%) | |
| office-facilities | ok | 3 | 47% | Todd Boetticher (47%), Laurie Schroeder (22%), Meyer Wexler (16%), Jimmy White (15%) | |
| payment-webhooks | ok | 3 | 56% | Meyer Bratton (56%), Lalo Goodman (24%), Kim Capone (20%) | |
| feature-flags | ok | 3 | 55% | Gale Schrader (55%), Walter Schrute (24%), Owen Fring (21%) | |
| expense-reports | ok | 3 | 55% | Gavin Flenderson (55%), Michelle Schrute (24%), Owen Dunn (21%) |
Who is exposed (16)
The same finding read by person rather than by topic. Sole knowledge has no cover behind it at all. Led knowledge has others contributing, so it degrades rather than disappears.
Stanley Lewis — 1 sole, 3 led
Would leave with them
sso-login
Would lose its lead
billing-retries, certificate-renewal, kafka-lag
Carlton Halpert — 1 sole, 0 led
Would leave with them
contract-review
Lalo Goodman — 1 sole, 0 led
Would leave with them
search-indexing
Michelle Schrute — 1 sole, 0 led
Would leave with them
payroll-taxes
Walter Schrute — 1 sole, 0 led
Would leave with them
data-warehouse
Erin Hale — 0 sole, 1 led
Would lose its lead
terraform-state
Jared Capone — 0 sole, 1 led
Would lose its lead
health-benefits
Lalo Vance — 0 sole, 1 led
Would lose its lead
api-ratelimits
Laurie Schroeder — 0 sole, 1 led
Would lose its lead
hiring-interviews
Meyer Wexler — 0 sole, 1 led
Would lose its lead
image-uploads
Monica Hanneman — 0 sole, 1 led
Would lose its lead
laptop-hardware
Stanley Varga — 0 sole, 1 led
Would lose its lead
cdn-caching
Stephanie McGill — 0 sole, 1 led
Would lose its lead
vacation
Steve Rosetti — 0 sole, 1 led
Would lose its lead
oncall-paging
Walter Bream — 0 sole, 1 led
Would lose its lead
kubernetes-deploys
Will Salamanca — 0 sole, 1 led
Would lose its lead
onboarding-paperwork
One-person connections (8)
Subjects that only one person has ever done work across. They have their own experts, so nothing that counts experts per subject shows this: what rests on one person is not any of the subjects but the knowledge that they belong together, and whoever picks one up afterwards has no reason to look at the others. Where the crossings join up they are listed as the one body of work they are, since that is what would leave.
| Subjects | Only | People holding them | Worked on together |
|---|---|---|---|
| billing-retries + kafka-lag | Stanley Lewis | 3 | 40% |
| certificate-renewal + sso-login | Stanley Lewis | 3 | 40% |
| api-ratelimits + image-uploads | Lalo Vance | 5 | 33% |
| cdn-caching + terraform-state | Stanley Varga | 4 | 33% |
| data-warehouse + feature-flags | Walter Schrute | 3 | 33% |
| database-migrations + kubernetes-deploys | Walter Bream | 6 | 33% |
| expense-reports + payroll-taxes | Michelle Schrute | 3 | 33% |
| health-benefits + vacation | Stephanie McGill | 4 | 33% |
Joined work (1)
Subjects that get worked on together, where the same person leads every one of them. This is a heavier finding than a concentrated subject on its own: whoever takes the work over has to learn the whole of it at once, and a list of subjects one at a time never shows that.
Stanley Lewis — 4 joined subjects
billing-retries, certificate-renewal, kafka-lag, sso-login
How this was measured
Every figure here is arithmetic over the indexed graph, with no model involved and no judgement applied. A person's share of a topic is their weight in it against everyone else's. Bus factor counts the fewest people whose combined share passes a majority. A topic is critical when one person holds most of it, and elevated when two do.
The finding only sees what was indexed, which here was codeowners, confluence, git, github, jira, org-csv, pagerduty, slack. Knowledge that lives somewhere whodar was not pointed at does not appear, so this is a floor on concentration rather than a complete picture.