Importing data

Most registers don’t start empty. They start as a spreadsheet, or inside another tool. The importer is built to move that data in safely, around one promise: nothing changes until you’ve seen a preview of exactly what will happen. Open it from Assets → Import.

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Step 1: Where is your data from?

Pick your source system and the importer pre-maps its export format for you. Snipe-IT, Lansweeper, ServiceNow, Freshservice, Jamf Pro, Microsoft Intune and Spiceworks all have ready-made profiles, each with notes on how to export from that system. For anything else, including your own spreadsheets, choose Custom CSV / Excel and map the columns yourself in step 3.

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Step 2: Upload

Drop in a CSV or Excel (.xlsx) file. The importer reads the headers and sample rows and carries them through the rest of the wizard, so you’re always mapping against your real data.

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Step 3: Map categories and fields

If your file has its own “type” column (Laptop, Vehicle, Licence, and so on), the wizard asks which column that is and lets you map each of its values onto your own category tree, once per value, not per row. Then Map Fields: each register field gets a dropdown of your file’s columns, with sample values shown so you can see what you’re mapping. Source profiles arrive pre-mapped, so you only adjust what’s different. Your mappings are remembered, so next month’s export from the same system imports in seconds.

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Step 4: Review (the dry run)

Nothing has been written yet. The review screen shows precisely what would happen:

• How many asset records will be created, and how many will be updated instead when rows match existing assets. Matching uses identifying fields like serial number, which is what makes re-imports safe. Importing the same file twice updates assets rather than duplicating them, so a monthly export from another system can keep the register current.
• Row-level problems, called out per row: an unknown location, a lifecycle stage that doesn’t exist, a duplicate serial within the file. The review grid is editable. Click any cell to fix a value on the spot.
• Fix once, apply everywhere. When a value like a misspelled location appears in fifty rows, remap it once and every row updates. AI Resolve can do this pass for you, matching unresolved values to your existing locations, departments, people, lifecycle stages and depreciation methods.

The importer is deliberately forgiving about formats. $ 2,499.50 becomes 2499.50, 30/06/2026 is understood as a date, lifecycle stages match regardless of case (“in life” becomes In Use), and common abbreviations like “SL” or “DDB” resolve to the right depreciation method. Whitespace is trimmed. What it can’t confidently interpret, it flags. It never guesses silently. Rows with unresolved errors are skipped, never half-imported, and the wizard asks you to confirm before committing an import that skips anything.

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Step 5: Commit

Click commit and the import runs. Large files process in the background with a progress bar, so you can leave the page and carry on working. When it finishes you’re back at the register, looking at your imported assets.

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After the import

• Spot-check a handful of assets: names, categories, serials, stages.
• If something went wrong at scale, don’t fix it row by row. Use bulk edit, or ask an administrator to roll the import back entirely.
• Set up policies. An import is the moment data quality rules start earning their keep.