Map and match source metadata

This page is about the catalog, which the free edition does not include. Connections, imports, and the physical tables and columns they create are part of the catalog surface, so a free organization has nothing here to work against. Everything below is correct for an organization that has the catalog. See Pricing for what each edition includes.

Mapping is what makes governance flow. The Map & Match workbench, opened from Business > Map & Match in the left navigation (/business/map-match), proposes which business entity each source table represents, and which attribute each column maps to, so the classifications and definitions on your model reach the catalog. Matching runs in both directions: start from your tables to find their entities, or stand on a business entity and ask what in the catalog should map to it. You review every proposal before it sticks.

Starting a match

From tables

Pick a scope at the top (a connection, project, or workspace) to choose which source tables are in play. The grid lists them; tick the tables you want to match (the header checkbox selects everything in view), then run Suggest matches. Until at least one row is ticked the button stays disabled and says “Select the tables you want to match.”, so a run always covers exactly the tables you chose, never a whole scope by accident. The matching AI skill reads your tables, columns, entities, attributes, and any glossary documents you’ve uploaded, then produces a candidate match per table with a confidence score and a short rationale.

The skill behind this workbench is called Match source tables: it maps whole source tables onto your business entities. Matching individual columns runs as a separate step (the column matching described below); you do not launch it as its own skill from the Ask AI list.

From a single table’s catalog detail, Suggest match opens a matching panel on the right side of the table you are viewing. The panel suggests a match for that table automatically and walks you through the same guided review, including the column matching step, without leaving the table. Choose Open full workbench in the panel when you want the full page instead.

From a business entity

Standing on a business entity, ask the reverse question: what in the catalog should map to this? Choose Find source tables in the entity’s detail header, or right-click the entity in the Business entities tree or on a model canvas and choose Map and match. A panel opens, finds up to 12 unmapped tables that resemble the entity by name and by meaning, and runs one match over exactly those candidates. The cost stays fixed no matter how large your catalog grows, because the run is bounded to the candidates it found.

An entity-seeded run never proposes a new entity: you already named the target, so a table with nothing to offer comes back as a skip with the reason. When no unmapped table resembles the entity at all, the panel says so plainly and no run starts.

Where you can start a match

You do not have to leave a canvas to map the table you are looking at. Right-click a table and choose Map and match. The table’s detail opens in the docked pane beside the canvas with the matching panel already on it, and the guided review runs there exactly as it does from the catalog.

The action is on every canvas that draws source tables: a project or workspace canvas, the catalog’s relationship canvas, and the Lineage tab of a table’s detail. On a lineage graph it works on the neighbors too, so you can map an upstream table without navigating to it first. The catalog tree offers the same action: right-click a table or view row and choose Map and match to open its detail with the matching panel already running.

The entity side mirrors it. Right-click an entity row in the Business entities tree, or an entity node on a model canvas, and choose Map and match; or use Find source tables in the entity detail header. All of them open the same entity panel described above.

Pick one table: with several nodes selected the action is not offered, because the panel handles a single table and batch matching belongs in the workbench with its scope picker. When your organization does not have AI matching enabled yet, or you are on the main branch, every one of these actions stays in its menu and tells you why it is unavailable instead of disappearing. If you have an unsaved edit open in the docked pane, the usual unsaved-changes prompt comes first: cancel it and nothing moves.

What the matcher considers

Which entities the matcher compares your tables against follows the scope you launch from:

A workspace-scoped run compares against the entities in that workspace’s own business models.
A project-scoped run compares against the entities in that project’s own business models.
A connection-scoped run compares against the entities in every business model in your organization.
An entity-seeded run (started from a business entity) compares only against that entity and its group members.

On a workspace-scoped run, an Include project models toggle (off by default) widens the pool to the models of every project inside the workspace too. Next to the scope picker, a summary line states the pool in plain terms (for example, “Matching against 6 models in this workspace” or “Matching against all models in the organization”).

If the chosen scope has no candidate entities, Suggest disables. The workbench offers a one-click way to widen the pool: Include project models (on a workspace scope that has not already turned it on) and Match against all models, which drops the pool to every model in your organization. From a model’s own Map & Match tab there is no wider pool to fall back on: if that model has no member entities yet, Suggest stays disabled and asks you to add entities to the model first.

Widen the pool when nothing matches

The matching panel that opens from a single table, whether from Suggest match or from a canvas, follows the page you launched it from: inside a workspace it compares against that workspace’s models, inside a project against that project’s models. When that pool turns up nothing, the panel offers the next scope out rather than leaving you to work out what went wrong. Two findings, because the remedy differs:

No business models are in scope here. There was nothing to match against, so no run starts. Widen the pool first.
No match found among this workspace’s models. The run finished and found no existing entity in the pool it searched.

Either finding comes with the rungs you have left: Include project models, on a workspace that has not already turned it on, and Search the whole organization. Choosing one re-runs matching immediately at the wider scope. Once you reach the whole organization the panel states the result plainly, “No match found anywhere in the organization”, with nothing further to try.

Widening never discards what the matcher already proposed. A new-entity suggestion is often the right answer, so it stays on screen beside the widening options rather than being replaced by them.

Match again re-runs matching at the scope you are on, from a project page as well as a workspace one.

Watching a run

While a run is in progress, a step timeline shows exactly where it is:

Preparing candidates. The matcher gathers the entities in scope and the evidence it will compare your tables against.
Table batches. Tables are matched in batches, one timeline step per batch, each labeled with the tables it covers (for example, “Tables 1-20 of 45”).
Recording results. The suggestions from every batch are saved and the run’s summary comes together.

Each step shows its own status: a spinner while it runs, a checkmark once it succeeds, an X if it fails. A failed batch does not stop the run: the remaining batches still run, and the completion summary at the end reports how many batches failed. Only when every single batch fails does the whole run fail.

Once a run finishes, a one-line summary reports the outcome: how many tables were scanned, how many table suggestions and column suggestions came out of it, and, if any batches failed, how many (for example, “18 tables scanned, 15 table suggestions, 42 column suggestions, 2 of 6 batches failed; some tables were not scanned”). The suggestions that did come back are ready to review; the tables that were not scanned are worth another Suggest run once you are ready.

Reading the suggestions

Each table row shows its suggested entity, a confidence percentage, and the reasoning behind it. Every suggestion in this workbench is grounded in evidence rather than naming similarity alone, so you can see why a match was proposed; what counts as evidence differs by tier. Table suggestions weigh name and business-term similarity, the table’s own column names and declared data types, its row count, and whether sibling tables in the same schema are already mapped, together with the Business Modeling and Glossary & Naming Standards foundations the skill cites. Column suggestions start from the same name and data-type signals, then add the evidence that lives at column level: the profiling statistics stored on the column (null ratio, distinct count, minimum and maximum values, value lengths, and most frequent values), the classifications the candidate attribute already carries, and the columns already matched on the same table; that skill cites Data Quality & Profiling alongside the same two foundations. The rationale names the signals that supported each match, so you can judge a suggestion on its evidence instead of taking the percentage on faith; when only names were available, the rationale says so, and the confidence reflects the thinner evidence.

A suggestion is one of two kinds:

An existing entity: the matcher recognized the table as your Customer, Order, or Shipment entity.
A new entity: nothing fit, so it proposes a skeleton. The row shows a non-editable new: label beside an editable name field. Rename the skeleton inline before you accept it.

A run started from a business entity only ever proposes the first kind: the target is already named, so a table with nothing to offer comes back as a skip instead of a new entity.

Three filter chips control the grid: Unmapped (the default, so you only see work left to do), Suggested, and Mapped. Remap re-runs matching for a table whose suggestion you want to redo.

Matching across a table family

Source data usually arrives in layers: a raw landing copy, a staging copy, and a persistent-staging copy, all derived from one source table. Map & Match recognizes that chain. Rather than marking every derived copy as a skip, it looks across the family for a business entity already mapped to any member and reuses it; when nothing in the family is mapped yet, the name signal uses the clean source name instead of the decorated layer name, so a staging table named stg_customer is recognized as your Customer entity.

When you accept an entity for a table that has siblings in its family, Guided Review offers an apply to family step: a pre-ticked checklist of the other layers in the chain. Confirm to map the whole family to the same entity in one move, or untick any layer you want to leave alone. A source table and its landing, staging, and persistent-staging copies end up pointing at one entity without re-deciding the match for each.

Accepting matches

Select the rows you trust and accept them in bulk. Accepting an existing-entity match records the mapping. Accepting a new-entity match creates the skeleton entity and maps the table to it in one step.

Skeleton entities are governance without modeling. You get a real entity you can classify and define right away, without stopping to draw it on the board. Accepted skeletons wear a Derived chip on the modeling board, indicating they are derived from source mapping and not yet adopted into the model. When you’re ready to make one a first-class part of your model, open its entity sheet and click Adopt into model.

Accept all above the floor

Above the grid, Accept all at or above N% accepts every pending suggestion in the run that meets your organization’s configured confidence floor, in one click and without selecting rows. The percentage in the label is that floor. The control is governed: it is unavailable under the Review each acceptance setting, where every suggestion must pass through Guided Review, and it follows the administrator lock on the AI recommendation workflow. Anything below the floor stays pending for you to judge.

Guided Review

Guided Review walks you through pending suggestions one at a time, so you can give each table your full attention without hunting through the grid. For each suggestion you see a decision card with the proposed entity, the confidence score, and the AI rationale. A progress indicator shows how many decisions remain.

Keyboard shortcuts in Guided Review: press A to accept, R to reject, E to edit the suggestion, and the left and right arrow keys to move between suggestions without committing.

How you reach Guided Review depends on your organization’s acceptance setting (see Acceptance settings below):

Under Review each, the workbench opens directly into Guided Review and hides the bulk grid. Every suggestion goes through the decision card.
Under Allow bulk or Auto-accept, the grid stays visible. A Guided review toggle switches the view into Guided Review when you want to work through remaining suggestions one at a time.

Add to business model

When you reach a suggestion where no existing entity fits, Guided Review gives you two options rather than forcing a reject:

Map to an existing entity: search your model and pick the entity manually. The table is mapped immediately.
Create a new entity: name and create a skeleton entity on the spot. After you confirm, the workbench offers to run column matching for that table so you can continue enriching it without switching context.

Create an entity from a source table

A new-entity suggestion creates a bare skeleton you name and enrich later. When you would rather start from the source table’s shape, this option on the decision card builds the entity from the table’s own columns instead. Choosing Create entity from source opens the column picker. Confirm or edit the Business name the entity will get (it is required), then choose which source columns become attributes: business columns start ticked, dv_ system columns start unticked. If the source table has a designated business key column, it is pre-selected, locked, and becomes the new entity’s business key automatically. Without a designation, the picker embeds the define-business-key step: promote one of the selected columns to business key, or derive a pattern-named key attribute from the business name. Creation stays blocked until the key is resolved, so every entity created from a table leaves the flow with its business key in place. If the table has siblings, the apply-to-family checklist follows.

Model-scoped Map & Match

Opening a model from Business > Models and switching to its own Map & Match tab gives you the same workbench, scoped to that model. The source picker starts narrow: it offers only the projects and workspaces that carry this model, with Any scope… at the end restoring the full list when you want it. A model that is not in any project or workspace yet falls back to the full picker with a note saying so. What the tab proposes and shows is narrower too: existing-entity suggestions are drawn only from the model’s direct member entities, and a table already mapped to an entity is badged In this model or Outside this model, depending on whether the mapped entity belongs to this model.

The grid is honest about what is worth a run. Tables related to this model sort first and start ticked: a table counts as related when its schema already contains a table mapped into the model, or when its name resembles a member entity. Unrelated tables stay in the grid below a divider, unticked. Nothing is hidden; adjust the selection freely before you choose Suggest matches.

Create entity from source works exactly as described above, with one difference: the new entity becomes a member of this model in the same step, so there’s nothing further to do to add it.

Column matching

Drill into a table to match its columns. The column panel has its own Suggest column matches button and shows a confidence percentage per column. A threshold control accepts every suggestion at or above a confidence you set (for example, “Accept all >= 80%”), while anything below stays for you to judge. Clear the threshold and the bulk button switches off rather than accepting everything, and skip-suggestions never ride along with a bulk accept.

Accepting a column opens a dialog that shows the attribute’s classification chips for you to confirm, plus a Seed expectations checkbox. Leave it ticked to copy the attribute’s quality expectations onto the column as a starting point. Until there are suggestions, the panel reads “No column suggestions yet.”

When the table’s mapped entity belongs to an entity group (see Sub-entity grouping), a column suggestion is not limited to that one entity: it can point to any entity in the group. When a suggestion’s target is a different entity than the one the table itself is mapped to, the suggestion carries an owner chip naming that entity, for example on Order Line, so you can see at a glance which entity will actually receive the match. Accepting the suggestion creates or maps the attribute on the entity the chip names; when there is no chip, it lands on the table’s own mapped entity as usual.

Column enrichment

When you accept a column match you can enrich it at the same time: provide a business name for the column, add a comment, and confirm the attribute’s classifications that carry over. This enrichment step adapts to your acceptance setting:

Under Allow bulk, accepted columns display an enrichment table below the grid where you can fill in business name and comment for each accepted column before saving.
Under Review each, each column is presented as a card. You fill in the business name, comment, and classifications before moving to the next column.

The threshold and Seed expectations controls described above are available in both modes. Refer to the column matching section above for how those controls work.

Column quick actions

The Columns tab carries two quick actions for a single column, reached from the row’s actions menu without opening the full Suggest column matches panel. Both need the table to already be mapped to a business entity: a pending suggestion does not count, so until the table’s mapping is accepted, both actions stay disabled with a reminder to map the table first.

Suggest attribute match runs matching for just that one column:

Open the column’s row menu and choose Suggest attribute match.
The column shows a pending spinner while the single-column run is live.
When it finishes, the column carries the same suggestion chip as any other column match, ready to review and accept from there.

Add to entity skips suggestion and review, and creates the attribute right away:

Open the column’s row menu and choose Add to entity.
When the table’s mapped entity belongs to an entity group, pick which entity in the group should own the new attribute; otherwise the dialog names the table’s mapped entity outright.
Choose Add attribute. The attribute is created and named using your organization’s naming convention, with nothing to type.

Check source readiness

Before you build loads on a newly landed source, run Check source readiness to answer one question per table: is this source ready to load? In the Ask AI panel, while you have the Map & Match workbench open, choose Check source readiness and pick the tables under Tables to assess. Readiness evidence is gathered for exactly the tables you pick, together with the Business Modeling and Data Quality & Profiling foundations the skill cites, and the skill returns a report: it proposes nothing, applies nothing, and leaves your mappings untouched.

For each table, the report covers:

Business keys and their mappings. Every declared business key, whether it is mapped to a business attribute, and whether the key column is nullable. These decide the verdict: a table with no declared key, an unmapped key, or a nullable key column is not ready, because a key that can be null cannot identify a row reliably.
Unmapped columns. How many of the table’s columns have no attribute mapping yet.
Classification coverage. How many columns carry a classification from your organization’s schemes; see Data governance overview for the schemes it reports against.

Unmapped columns and thin classification coverage never block a load on their own: the report counts them as readiness debt, with the exact numbers per table. Each table gets a verdict, ready or not ready, plus the evidence that decided it and what would have to change to reach ready. The report closes with an overall summary that orders your tables from closest to ready to furthest away.

Acceptance settings

Your organization’s AI recommendation workflow setting, found at Settings → Artificial Intelligence, governs how the workbench handles suggestions across your workspace. There are three levels:

Review each: every suggestion is reviewed one at a time through Guided Review before it can be accepted. Bulk accept is not available. This setting suits teams that want a human decision on every mapping.
Allow bulk (the default): the bulk grid, the confidence threshold control, and the Guided review toggle are all available. You decide when to accept in bulk and when to step through suggestions individually.
Auto-accept: suggestions at or above a confidence floor you configure are applied automatically as soon as a match run finishes. Anything below the floor stays pending for you to review. The confidence floor control is only shown when this level is selected.

When Auto-accept runs, the workbench shows a calm “Auto-accepted N suggestions” banner. Click Review applied on that banner to jump directly to the Mapped filter and inspect what was applied.

An organization administrator lock on the Conventions page controls which members can change the AI recommendation workflow setting. If you cannot see or edit the setting, ask an organization administrator.

Everything is branch-aware

Column matching runs on a feature branch, like any catalog change. Suggestions land as drafts, accepting them is an edit you review, and nothing reaches main until you commit. Viewers see the workbench read-only: the Suggest and accept controls need the editor role. On the main branch the controls explain why column drafts can’t be accepted there.

See Data governance overview for what the mappings feed, and Table details for the derived badges that result.