Tutorial
Start with clean data
A messy import haunts you for months.
Map your columns, preview the result, and fix issues before you commit, so your data comes in clean, without duplicates, broken fields or surprises.
How to import without regret
Map fields carefully
Line up columns so nothing lands in the wrong place.
Catch duplicates
Spot and merge repeats before they enter your data.
Validate as you go
Verify each batch so errors never make it in.
Start right
A messy import haunts you for months
Bad data doesn't stay quiet, it resurfaces in every report.
Rushing an import is how you inherit duplicates, split records and broken fields that quietly poison everything downstream. The time spent getting it right up front is repaid many times over.
This guide walks through mapping fields, catching duplicates before they land, and validating as you go, so what comes in is data you can trust from the first day.
Highlights
The process
Map columns
Match your fields to ours.
Preview
See the result before importing.
Fix first
Catch issues before they land.
Before you start
The import is a negotiation with your past data
Every legacy dataset lies a little: three spellings of the same customer, dates in two formats, a 'notes' column doing the work of five real fields. Budget for a cleanup pass before the import, not a heroic untangling after it.
Always run the dry-run and actually read it. The preview that says '312 rows will merge, 9 will be skipped' is the whole safety story, investigate the 9 before committing, because skipped rows are where the CFO's favorite account is hiding.
Import in that order: users, then structures, then records, then relationships. And keep the source file untouched until a week of real usage confirms the mapping, the cheapest rollback is the import you can simply repeat.
We build software for teams who want their tools to fit the way they actually work — web, mobile, AI and the systems that tie them together. We write here about what we learn shipping it.