Business glossary

What is a business glossary?

A business glossary is the agreed list of what your business words actually mean. Revenue, active customer, gross margin, churn, order and lead each get a written definition in plain language that finance, sales, operations and the data team can all read.

It is the business layer that sits on top of your technical metadata. Where a table or column tells you where a number lives, the glossary tells you what the number means and which definition counts when people disagree.

It exists because the same word rarely means one thing. Sales and marketing can both say revenue and steer by different figures. Microsoft Purview even sorts glossary terms into separate governance domains for this reason, so the same term can carry a different approved meaning in different parts of the business. A glossary makes those differences visible instead of letting them surface halfway through a board meeting.

What goes into a glossary entry

A definition on its own is not enough. A working entry carries a few fields, and the one that matters most is not the definition.

• Term and definition. The word and what it means, written for a business reader rather than a database. "Active customer: a customer with at least one paid order in the last twelve months."

• Owner. The person or team allowed to decide when the definition changes. For active customer that is often sales operations or a revenue owner.

• Status. Draft or approved. A reader needs to know whether a definition is settled or still being argued over.

• Scope and exclusions. What the term does and does not cover. Active customer usually excludes test accounts and internal companies.

• Where it lives. A link to the table, report or semantic model where the term is implemented, and for a KPI the formula behind it.

Leave out the owner and the glossary quietly rots. With nobody accountable, the definition never gets updated when the business changes, and within a year the list is an archive instead of a working reference. This is the same reason the data steward role exists across data governance.

Business glossary, data catalog, and data dictionary

These three get mixed up constantly, and keeping them apart is most of what makes a glossary useful.

A business glossary defines business terms in business language and answers what a term means and who owns it. Its readers are finance, sales, product and leadership, and it runs across the whole organisation.

A data catalog is an inventory of your data assets: tables, reports and datasets, plus the technical metadata about them such as owner, schema, freshness and lineage. It answers what data you have and where it is.

A data dictionary works one level lower. It describes individual fields the way a database sees them: column names, data types, allowed values and constraints. It answers what a field is and how to work with it, and its readers are engineers and analysts, usually for one system rather than the whole company.

Put simply: the glossary is for meaning, the catalog is for finding the data, and the dictionary is for the technical shape of each field. Tools such as Collibra and Alation bundle all three in one product, which is part of why people confuse them.

Settling a contested definition

Take revenue. In sales, revenue often means the value of signed deals: the contract is won, so the number counts. In finance, revenue means invoiced amounts excluding VAT, with credit notes and refunds removed. Same word, two figures, and every cross-team report that mentions revenue inherits the argument.

A glossary settles this without pretending one side is wrong. The owner, usually finance, picks the official meaning of revenue and marks it approved. The sales figure does not disappear; it becomes its own term, bookings or signed contract value, with its own definition and owner. Both teams keep the number they steer by, and the shared word now points to one agreed thing.

The hard part is never writing the sentence. It is getting two departments to agree which definition earns the plain word, and recording who made that call.

Glossary versus metrics layer

A glossary defines meaning in words. A metrics layer turns that meaning into logic a computer runs. The glossary says net revenue is invoiced amount minus VAT, refunds and discounts; the metrics layer holds the actual calculation that every dashboard, spreadsheet and AI assistant queries, so they all return the same figure.

You want both, pointing at each other. The glossary is where people agree what a term means; the metrics layer, often built on a Power BI semantic model, is where that agreement gets executed. A definition nobody implements stays theoretical, and calculation logic nobody defined in business terms is almost impossible to audit.

What to watch out for with a business glossary

Start with the words that cause fights. Do not try to document every term in the company on day one. Begin with the handful that trigger a monthly argument: revenue, margin, order, active customer, stock.

Keep the status honest. Mark each definition as draft or approved and let readers see which is which. A glossary full of half-agreed terms presented as final is worse than none, because people stop trusting all of it.