Creating an External data enrichment
An External data enrichment sends the values of one of your columns to a specialist provider and adds the fields it returns to your table — for example, the city, country and network operator behind an IP address.
Go to Data Enrichment in the left sidebar, click Add, and choose External data. The wizard has six steps, and you can move back and forth between the steps you have already reached.
| Step | What you do |
|---|---|
| 1 — Setup | Name the enrichment and pick the provider |
| 2 — Input mapping | Choose the table and the column the provider reads |
| 3 — Filters & preview | Narrow the records, then see what the provider returns |
| 4 — Attributes | Choose the fields to add to your table |
| 5 — Run logic | Choose how often values are looked up again |
| 6 — Summary | Check everything and activate |
A draft is saved as soon as you leave the input mapping step. From then on the enrichment is in the list as a Draft, and you can leave the wizard and pick it up later with Edit.
Looking for answers a language model works out from your own columns instead? See Creating an AI enrichment.
Step 1 — Setup
Name the enrichment, then pick the provider that will answer it.
Give it a name you will recognise in the list — "Geolocation from IP" is clearer than "Enrichment 1" — and an optional description for your colleagues.
Then choose the provider. Each card shows what it needs as input and previews the fields it returns. Today the catalog offers IP Geolocation, which turns an IP address into location and network details; more are being added.
The provider is fixed once a draft exists: the columns carry its name. Switching provider means creating a new enrichment.
Step 2 — Input mapping
Pick the table to enrich, then point the provider's input at the column that holds it.
Two choices: the source table whose records you want to enrich, and the input mapping that tells the provider which column holds the value to look up — for IP Geolocation, the column containing the IP address.
When you pick a column, BPP shows a sample of its real values and how often each appears, so you can confirm you chose the right one before spending anything.
The column picker previews the values actually in the column — the quickest way to catch the wrong choice.
A record with nothing in the mapped column has nothing to look up. It is not sent, it is not charged for, and its new columns stay empty.
Step 3 — Filters & preview
Filters are optional; the preview is not. It shows what the provider actually returns for your data.
Value filters narrow which records get enriched. Without one, every value in the column is looked up. Filters can also use fields from other tables, for example only users who bought something in the last 90 days.
Preview is required before you can go on. It runs a real lookup on up to 150 input combinations from the records your filters select, and shows the full payload that comes back: how many fields the provider returned, how many values matched, how many had no match, and the values themselves for every sampled record.
The preview samples the population your filters select. Setting filters afterwards would mean what you saw was measured on different records from the ones that actually get enriched. Change the source table or the mapping later and the wizard asks for a new preview.
The sampled values are bought from the provider. It is deliberately limited to five previews per hour.
Step 4 — Attributes
Pick the fields to add to your table. Completeness comes from the preview you just ran.
Every field the preview discovered is listed with its completeness — how often the provider actually returned a value for it on your data — and its type. A field at 95% is reliable; one at 40% will be empty for most of your users. This is how you decide which fields are worth keeping.
Tick the fields you want. For each one you can set a display label — the name shown across the platform — and whether it is visible in the pickers. Grouped fields (objects and lists) are shown for reference but cannot be selected; pick the individual fields inside them instead.
The panel on the right, Reads as, restates your configuration in plain language, so you can sanity-check it without going back through the steps.
Selecting more fields costs nothing extra with this provider: one lookup returns the whole set. Choosing fewer fields keeps your schema tidy, not your bill lower.
Step 5 — Run logic
Choose how often a value you already have is looked up again.
Every option covers values never seen before, on every scheduled run; they differ only in how a value you already have is treated.
| Option | What it does | Use it when |
|---|---|---|
| Enrich new values only | A value is looked up once and never asked again. | The answer cannot change — the country behind an IP range. Cheapest. |
| Enrich new values, and refresh older ones | New values immediately; an existing value is looked up again once it is older than the window you set. | The default, with a one-month window. Set the window in days, weeks or months. |
| Re-enrich everything on every run | Every value in scope is looked up again on every run. | Rarely. Only when the data changes constantly and the answer must always be current. Most expensive. |
External data runs as part of your platform's scheduled pipeline, after reconciliation and before the AI models — so the models work on enriched columns. That is why this step asks how often values are refreshed, not when the job runs.
Step 6 — Summary
The whole configuration on one screen, with an Edit link on each block.
Check everything, then click Create source. A confirmation spells out what you are committing to — how many fields will be written to your records, which input is looked up and under which run logic — and offers two ways to activate:
Both buttons activate the source. The difference is whether the first lookup starts now.
- Create and run — activates the source and starts the first run immediately. The run continues if you leave the page.
- Save without running — activates the source and leaves the first run to the next scheduled pipeline.
Either way, the columns appear on your table immediately and read empty until the first run completes.
Next
- Managing enrichments — running, pausing, editing, and what each status means.
- Data Enrichment overview — how costs work and where the new columns appear.