Here’s a simple way to think about automation using four words — List, Stratum, Object, Verb — so anyone on the team can point to a thing and say what it does.
Why Another Framing?
Automation is everywhere: CI jobs, data pipelines, deploy scripts, cloud tasks, growth loops. Most grow organically — scattered retries, hidden state, and brittle glue. LSOV gives us a tiny vocabulary to make automation safe, observable, and evolvable without heavy ceremony.
LSOV, In Plain Words
- List — the whole thing you’re running (the store; the full app).
- Stratum — the sections that keep it tidy (Security, Men’s, Women’s, Checkout).
- Object — the things inside a section (CashRegister, Rack, Product, Staff).
- Verb — what those things do (scan, pay, restock, return).
How This Helps
This framing makes technical systems sound less technical. Store‑style names keep conversations plain: anyone can point to the List, name the section (Stratum), and say what an object does with a verb. Subcomponents hold their own objects, which keeps the global scope clean and reduces cross‑talk. You don’t have to choose a side on stateless vs. stateful — pick what fits the job; LSOV works either way. It makes naming conventions easier as well.
Strata As Compartments, Named For Stats
A Stratum is a compartment — a subcomponent with its own objects and verbs. Name each stratum by the metric it owns so analytics come first, not last. Instead of generic labels like “Module A”, choose names that read like a dashboard dimension: “Auth: Session Success”, “Catalog: Product Views”, “Checkout: Conversion & Refunds”. When sections are titled by their stats, you can run analytics across the architecture without extra mapping.
From SOV Logs to LSOV Architecture
In the learning ecosystem, the ADL Initiative (adlnet.gov) introduced standards like the Experience API (xAPI, originally Tin Can API). xAPI expresses activity as actor–verb–object statements and stores them in a Learning Record Store (LRS), making cross‑system analytics possible. That simple structure — who did what to which thing — proved powerful for measurement and interoperability.
Conclusion
Put simply: name the whole, split it into clear sections, place things where they belong, and give every action a verb. Title sections by their stats so your dashboards read like your docs. LSOV keeps the conversation human and the system measurable. Next, we’ll share naming patterns and small, working examples you can drop into real projects.