Here's the thing you came for: a free, ready-to-use Salesforce data dictionary template, built to be filled in rather than admired. It's an Excel file with four working sheets, dropdowns where you'd want them, and a few rows already filled in so you can see what "done" looks like. Download it, and the rest of this post is how to fill it without it turning into another abandoned spreadsheet.
A quick honest note up front, because it shapes everything below: the hard part of a data dictionary was never the spreadsheet. It's deciding what goes in it and keeping it true. This template is opinionated about both, which is what separates it from the one-click metadata exports floating around.
What actually belongs in a data dictionary
Open any object in Object Manager and you can read its fields' types, lengths, and picklist values in a few minutes. A browser extension can dump all of it to a spreadsheet in one click. That information is real and worth having — but it is not a data dictionary. It's an inventory.
The difference is context. Knowing that Account.Customer_Tier__c is a picklist tells you nothing about why it exists, that a flow sets it on contract renewal, that nobody should edit it by hand, or that the CS team owns the logic. That second layer — meaning, source, ownership, sensitivity, whether the field is safe to touch — is the entire reason a dictionary earns its keep, and it's the layer no export can produce, because it lives in people's heads, not in the metadata.
So the template is built in two halves, and the core sheet shows the seam on purpose. The left columns are Structure — the facts you can pull from your org. The right columns are Context — the part you supply. When you fill it in, do the right half first. It's the harder, more valuable work, and the half people quietly skip.
The four sheets, and how to fill them
Objects. Start here, and resist documenting everything. List the five to ten objects people actually use day to day. For each, capture what it's for, who owns it, and whether it's active or on its way out. This sheet scopes the whole effort — without it, "document the org" is an infinite task and you'll stall on day two.
Field Dictionary. The core. One row per field, split into the Structure and Context halves.
The Structure columns — object, field label, API Name, data type, required, unique, picklist, default — are the ones you can lift from an export or Object Manager. Paste them in; don't hand-type them.
The Context columns are the dictionary. Fill these with care:
- Business Definition — what the field means and why it exists, in plain language a new hire would understand. Not "the industry field," but what it drives downstream.
- System of Record / Source — where the value comes from. Manual entry? An integration? A formula? A flow? This single column prevents more confusion than any other, because "who sets this" is the question that starts most field-related arguments.
- Referenced By — the flows, validation rules, and reports that depend on the field. This is the column that saves you the day someone asks "can we delete this," and it's the one most likely to drift, so date it.
- Who Can Edit / Access Notes — a short read on who sees and changes the field. Not a full security audit, just enough to know if it's locked down or wide open.
- Data Sensitivity — None, Internal, PII, or Sensitive. The dropdown keeps it consistent, and it's the column your compliance reviewer will go straight to.
- Owner / SME — the human to ask. Tribal knowledge written down is just knowledge.
- Status — Active, Deprecated, or Do Not Use, color-coded so a dead field is visible at a glance. The sample rows include a
Old_Score__cfield flagged Do Not Use so you can see how a graveyard field should read. - Last Reviewed — a date. This is the column that tells you, six months from now, how much of this document to still trust.
Picklist Values. Picklists are where definitions quietly rot, because values get added and retired without anyone updating the docs. List the values per field, mark which are active, and note where each applies. The template includes a worked Customer_Tier__c example with a retired value, because showing a deactivated value is part of an honest dictionary.
Change Log. The least glamorous sheet and the one that decides whether any of this survives. Every time you change a field or its documentation, add a line: date, who, what, why. A dictionary with no change history is a snapshot of one afternoon; a dictionary with a change log is a living record people can trust.
An export gives you the Structure. The Context — meaning, source, ownership, sensitivity — is the whole point, and it only lives in people's heads.
The honest catch — and it's the important part
This template is accurate the hour you finish it. It starts drifting the moment someone adds a field, renames a flow, tweaks a picklist, or stands up an integration — none of which sends you a notification. In a quiet org you might re-true it once a quarter and stay roughly honest. In an active one, the gap between what the spreadsheet says and what the org actually is opens faster than any person can hand-close it, and a data dictionary that's quietly wrong is worse than none at all, because people stop double-checking it right when they shouldn't.
That's not a flaw in this template; it's the nature of documenting a living system by hand. The Last Reviewed and Change Log columns exist precisely to keep the drift visible, so you notice the rot instead of trusting through it. Use them honestly and you'll get real value out of this spreadsheet — for one object, one onboarding, one cleanup project, it's exactly the right tool.
But pay attention to the moment keeping it current stops feeling realistic. That moment isn't a discipline failure. It's the signal that the work has outgrown a spreadsheet and wants to be generated from the org itself — where the Structure half is pulled and kept current automatically, change is detected as it happens, and you spend your effort only on the Context half that genuinely needs a human. That's the problem SchemaForce exists to solve, and it's worth being precise about the boundary: a tool can keep the structure true and flag what changed, but it can't invent your business definitions or name your system of record for you. The Context columns still need you. What automation removes is the transcription and the decay — which is to say, the two reasons this spreadsheet eventually ends up abandoned in a Drive folder.
Start here
Don't try to document the whole org this week. Download the template, list your top objects, and fully document just those — Context columns first. Set a review date on each field, log your changes, and see how it holds up over a month. You'll get a genuinely useful artifact out of it, and you'll learn firsthand exactly where hand-maintenance starts to strain. Both of those are worth having before you decide what to do next.
