How to Connect DaData to Google Sheets (4 Methods Compared)

The Problem with Pulling DaData Enrichment Into Your Sheet

You have a Google Sheet full of Russian contact records, company INNs, bank BIC codes, or raw addresses. DaData has the cleaning and enrichment APIs you need. But getting that data into your sheet cleanly, across hundreds or thousands of rows, is not a one-step operation.

DaData's API covers an unusually wide surface: postal address standardization, company registry lookups, phone and email cleaning, geocoding, bank metadata, passport validation, Kazakhstan BIN lookups, OKVED2 classification, and more. The data is authoritative and Russian-regulatory-grade. The gap is the spreadsheet side. Getting any of it in bulk into a Google Sheet without writing code is where most teams get stuck.

Below are the four ways people typically pull DaData results into a Google Sheet. Only the last one scales without a developer.

Method 1: Call the DaData API by Hand and Paste the Results

The default approach. You open the DaData API docs, find the right endpoint for the data you need, call it manually or through Postman for a handful of rows, copy the result, and paste it into the sheet.

When this works:

• You need to validate a single address or BIC before a payment
• A developer is testing the API and just needs a sanity check
• You have five rows or fewer and a REST client already open

When it breaks:

• You have more than ten rows and a deadline
• The data you need spans multiple DaData endpoints in one pass
• Someone who is not a developer needs to run this without help

The core problem is scale. DaData's API is well-documented and the responses are structured, but calling it row-by-row through Postman and pasting results into a sheet is a job for an intern with nothing else to do. For 300 supplier INNs or 5,000 customer contact records, this approach is measured in days, not minutes.

Method 2: Use Zapier to Trigger DaData Lookups From Row Events

Zapier connects to DaData and can call enrichment endpoints when a new row appears in a sheet. This is useful for event-driven flows where records are added one at a time.

This works for event-driven moments:

• New customer signs up → clean their address and write it back
• New vendor is added to the supplier tab → validate their INN immediately
• New lead is captured in a form → normalize their phone number

This fails for batch or analytical work:

• You already have 5,000 rows and need to process them now
• You need to combine results from multiple DaData endpoints per row
• You need conditional logic like "skip rows where the phone is already in E.164 format"

Zapier fires on new rows. It does not backfill existing ones. It does not iterate over a range and apply different endpoint calls based on what it finds in each cell. For bulk enrichment of existing data, it is not the right tool, and the per-task cost in Zapier adds up fast on large datasets.

Method 3: The Previous Generation — Script and Add-On Integrations

Until recently, teams handling bulk DaData enrichment wrote Google Apps Script functions that called the API, looped over a range, and wrote results back. That was a real step up from copy and paste.

You configured the endpoint, wrote the column mapping, handled pagination and rate limits, and wired up a menu item so a non-developer could trigger a run. The script worked when it worked. When the sheet structure changed, when DaData released a new API version, or when the team needed a different endpoint, someone had to go back into the code.

There was also a category of low-code tools that let you map sheet columns to API parameters through a UI. Useful in principle. But you still had to configure a separate mapping for every DaData endpoint you needed, and the tools rarely handled multi-endpoint rows cleanly — the case where you want address standardization, phone normalization, and company enrichment in the same pass.

This is the category we think of as the previous generation. It worked, but it asked a lot of the operator.

The Easy Way: Using SheetXAI in Google Sheets

There is a different approach entirely. SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands the column structure, and through its built-in DaData integration it can call the right endpoints per row, handle cleanup and conditional logic, and write the results back to the sheet. No script, no column mapping UI, no Zapier flow. You just ask.

Example 1: Your Data Is Already in the Sheet

You have a Google Sheet with 300 supplier INN codes in column A. Finance needs the legal name, registered address, and active/inactive status for counterparty verification before the next payment run.

For each INN in column A, use DaData to look up the company in the Russian tax registry and write the legal name into column B, the registered address into column C, and the active/inactive status into column D. Skip any row where column A is blank.

SheetXAI reads the INN column, calls DaData's party lookup for each row, and writes the results back. You get a fully enriched sheet without writing a single line of code.

Example 2: Your Data Needs Multiple Endpoints in One Pass

If your sheet has mixed record types — names, phones, emails, and addresses all needing different DaData endpoints — SheetXAI handles the multi-endpoint pass in one prompt:

For each row in my sheet, use DaData to standardize the phone in column B into E.164 format and write it to column E, clean the email in column C and write the corrected version to column F, and parse the address in column D into postal code, city, street, and house — write those into columns G, H, I, and J respectively.

SheetXAI coordinates the endpoint calls, maps the response fields to the right output columns, and runs the whole thing in one go. One prompt, three DaData endpoints, one pass.

Which Method Should You Use

For a single record lookup — one address, one INN, one BIC — the manual API call or Postman approach is fine. For event-driven flows where new rows are added one at a time and you want enrichment to happen automatically, Zapier is a reasonable fit if the volume is low.

For anything with existing rows to backfill, multiple endpoints to combine, conditional logic about which rows to skip, or a non-developer who needs to run this on demand, SheetXAI is the only option that handles it without writing code or configuring a pipeline.

If your team runs enrichment more than once a month, the time saved on the second run pays back the first.

Try It

Get the 7-day free trial of SheetXAI and open any sheet with Russian contact or company data, then ask it to run DaData enrichment on the columns you care about. The DaData integration is included in every SheetXAI plan.

For specific workflows, see how to bulk-clean Russian contact records, how to enrich supplier INNs with registry data, or browse the full integrations directory.