How to Connect Datagma to Excel (4 Methods Compared)

The Problem With Getting Workbook Data In and Out of Datagma

You have an Excel workbook full of data — prospect names, company domains, inbound phone numbers, customer email addresses. You need Datagma to enrich it, fill in missing fields, or flag job changes. And you need the results written back into the workbook without disrupting your existing structure.

Datagma is good at returning accurate contact intelligence on demand. But the path from a column of raw inputs to a column of enriched outputs runs through an API. The typical Excel flow involves exporting to CSV, calling the API separately, and re-importing — which means formatting mismatches, formula breaks, and at least one manual reconciliation pass.

Below are the four approaches. The last one is the one that actually fits your workflow.

Method 1: Manual Copy-Paste

The most common Excel approach is to export a column of emails or names to a CSV, paste them into Datagma's web interface, download the enriched results, and import them back into the workbook.

For 20 contacts, this is manageable. For 200, it's half a day. For 500, the import always seems to land in the wrong columns, the CSV headers don't match what you expected, and you're reformatting before you've even started on the actual work.

The deeper problem with repeated export-enrich-import cycles is what they do to your workbook's integrity. Named ranges drift. Formulas that reference column D break when the import lands in column E. Someone opens the workbook between runs and the data looks stale. Eventually the whole process is held together by whoever remembers how it works.

Method 2: Power Automate

Power Automate can connect to Datagma via HTTP requests. You build a flow that reads rows from your Excel table, calls Datagma's enrichment endpoint, and writes the response fields back.

Worth asking yourself before you commit: are you comfortable with HTTP request steps? Have you configured custom connectors before? Do you know how to parse a JSON response and map nested fields to table columns? If any of those are new territory, this path will take longer than the outcome is worth. Skip to Method 3 or 4.

If you've done this kind of work: the flow is buildable. You authenticate, configure the HTTP step with the right Datagma endpoint, parse the response, and use Update Row to write back.

The ceiling appears fast.

A flow that fires per-row means one trigger per contact. Five hundred phone numbers means five hundred runs, five hundred entries in your run history. When row 203 fails because the number format didn't match what Datagma expected, you're scanning through a run log to find it.

You probably just need carrier and location added next to each number. You probably have no idea how to configure an HTTP connector in Power Automate — and that's not a gap in your skill set, it's just not what you were hired to do. So you paste the Datagma docs into a Teams message and ask whoever handles your Power Automate flows. And then you wait.

Chaining steps — filter first to skip already-enriched rows, then call Datagma, then handle nulls — means more steps, more triggers, more places for the flow to silently break when Datagma's response schema changes.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, teams that needed repeatable Excel ↔ Datagma enrichment used a generation of add-ins that let you configure column mappings and save them as reusable templates. Define the input columns, define the output columns, save the config, run it.

It was a real step up from CSV-export-and-reimport. Consistent column placement. Reusable configs. No manual reformatting.

What you still owned: building the template, mapping every field, writing conditional rules for missing data, updating the config every time a column was renamed. The add-in moved the data. The decisions were still yours every time. And when your table structure changed, the config didn't update itself.

That's the previous generation. Functional, but it never stopped requiring operator attention.

The Easy Way: Using SheetXAI in Excel

There is a different approach. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands what you're looking at, and through its built-in Datagma integration it can enrich contacts, look up emails, detect job moves, or reverse-lookup numbers — using whatever columns your data already sits in. No template, no connector config, no export step. You describe the task.

Example 1: Bulk enrich a prospect table

For each row in my Prospects worksheet (name in column A, company in column B), call Datagma to enrich the contact and write LinkedIn URL, job title, and company size into columns C, D, and E

SheetXAI reads the table, sends each row to Datagma, and writes the enriched fields back — surfacing errors inline where a lookup returned nothing.

Example 2: Fall back gracefully when input data is incomplete

Enrich all 300 leads in my workbook using Datagma — use the LinkedIn URL in column C where available, otherwise fall back to the email in column D

Instead of pre-filtering the workbook or building conditional logic into a flow, you describe the condition in the prompt. SheetXAI handles the fallback on each row.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with a contact list or lead table, then ask it to enrich using Datagma. The Datagma integration is included in every SheetXAI plan.