Glossary

The data product lexicon

A curated, opinionated glossary, not a dictionary. Every term here is one I have a real take on, usually because the common understanding is subtly wrong in a way that costs teams money. The format is deliberate: what the word is assumed to mean, then what it actually means once you have shipped a few of these. It grows as I write.

The data stack

Semantic layer

Assumed: a BI feature, the place your dashboards keep their numbers.

Actually: the contract that makes "revenue" mean one thing across every tool, query, and agent. It is the layer most teams gesture at and never finish, which is exactly why two dashboards disagree.

Data contract

Assumed: documentation, a schema written down somewhere.

Actually: an enforced agreement with consequences. If no test fails and nobody gets paged when the shape changes, it is a wish, not a contract.

Data product

Assumed: a dashboard, or a dataset someone published.

Actually: something with an owner, a consumer, an SLA, and a lifecycle. If nobody is on the hook when it breaks, it is exhaust, not a product.

Single source of truth

Assumed: a database, usually the warehouse.

Actually: an agreement about which definition wins. No database enforces truth by itself; people and governance do. A second "truth" is one rogue spreadsheet away.

Reverse ETL

Assumed: a niche sync tool for moving rows around.

Actually: the step that operationalizes the warehouse, pushing modeled data back into the tools people actually work in. Analytics nobody acts on is a cost center; this is where it becomes an action.

Data governance

Assumed: compliance, the department that says no.

Actually: the layer that lets you move fast without breaking trust. Done right it is an accelerator, the thing that makes self-serve safe, not a brake.

North Star metric

Assumed: the one number the company should grow.

Actually: the metric that best predicts durable value for the customer. Pick the wrong one and you optimize the company straight into a wall, efficiently.

Markets and buyers

TAM (total addressable market)

Assumed: the big number at the top of the deck.

Actually: a boundary you choose to draw, and drawing it is the strategy. Uber's TAM was never the taxi fleet. The number follows the boundary, not the other way around.

SOM (serviceable obtainable market)

Assumed: TAM times some confident-looking percentage.

Actually: what you can actually operate to, built from reps you can hire, deals they can close, and contract value that pays for the motion. If the math needs 400 deals and you can fund eight reps, it is fan fiction.

Profit pool

Assumed: where the revenue is.

Actually: where the margin is, which is where the power is. Revenue shows you the activity; margin shows you who captures it. The PC industry ran on hundreds of billions in revenue while Intel and Microsoft kept nearly all the profit and everyone else fought for scraps.

Buyer journey

Assumed: a tidy funnel: awareness, consideration, purchase.

Actually: a trigger makes the status quo suddenly unacceptable, one worried person spends months turning that into a funded priority, and by the time an RFP exists the winner has usually already been chosen by whoever framed the problem. Nobody buys on a calm Tuesday.

Economic buyer

Assumed: the person who uses the product.

Actually: the smallest coalition whose incentives you can align to get a purchase that survives deployment. Often it is not the user. Employees hated Concur for two decades; finance held the budget, so it won.

Jobs to be done

Assumed: a fancier word for personas.

Actually: the progress a customer is trying to make, and the trigger that starts them looking. People do not buy products, they hire them for a job and fire them when something does it better.

Product-market fit

Assumed: a good score on a "how would you feel if you could no longer use this" survey.

Actually: retention and pull, measured by what customers do rather than what they say. People promising they would use it is the cheapest, weakest evidence there is.

Strategy and moats

Moat

Assumed: a feature your competitors do not have yet.

Actually: a benefit paired with a barrier, a reason rivals cannot or will not copy you. Great UX is not a moat; anyone with more engineers copies it by Friday.

Counter-positioning

Assumed: simply being different from the incumbent.

Actually: a move the incumbent could copy but will not, because copying you torches their own P&L. The barrier lives inside their income statement, which also means it expires.

Reverse positioning

Assumed: a synonym for counter-positioning, or just differentiating by adding more.

Actually: the opposite of adding more. You strip features the category trained people to expect, then add a few they never asked for. IKEA drops delivery and assembly and adds a showroom and a restaurant. It resets what the product even is. (Not counter-positioning, which is about a rival who cannot afford to copy you.)

Switching costs

Assumed: lock-in, the contract that traps a customer.

Actually: the cost a customer would eat to leave, built up over time through data, workflow, and integrations. It pays off late, so it is a power you invest in before you need it.

Flywheel

Assumed: a growth-loop buzzword for a slide.

Actually: a loop where each turn lowers the cost of the next one. If turn ten costs as much as turn one, you have a treadmill, not a flywheel.

Right to win

Assumed: the same thing as an attractive market.

Actually: a separate axis from market attractiveness: whether you specifically can win here. Google had unlimited capital and distribution and still could not will Google+ into existence. A boring market you can dominate beats a glamorous one where you are a tourist.

Blitzscaling

Assumed: grow as fast as humanly possible, always.

Actually: rational only inside the narrow window where network or scale effects are still up for grabs. Outside that window it is arson with a pitch deck.

AI and agents

Context layer

Assumed: prompt engineering, the words you type at the model.

Actually: the schema, semantic, and business context an agent needs to be right. The model is rented; the context is owned.

Read: The context layer is the product →