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Upload a table from Excel or CSV

For: The person in charge, The employeeTakes 4 minLast updated

Short answer

Open "Upload data" and pick an Excel (.xlsx) or CSV file with "Choose files" (or drag it in). GoalCube reads the file on its server in the EU, suggests how to protect each column and shows the first rows as the AI sees them. Below the table you can see whether anyone could be recognised from the columns, and which words in the free text could point to a person (yellow). Choose what to do with each yellow word, give the dataset a name and press "Save the dataset". The file itself is not stored.

Try it

Walkthrough: Upload a table (Excel or CSV)Step 1 of 5Fictitious data

Drag the file here, or press "Choose files". GoalCube reads it on its own server in the EU; the file itself is not stored.

All steps (5)
  1. Drag the file here, or press "Choose files". GoalCube reads it on its own server in the EU; the file itself is not stored.
  2. GoalCube also checks whether anyone can be recognised from the columns, and marks words in the free text that could point to a person. Here, nothing was found.
  3. GoalCube suggests how each column is protected and shows what the AI sees. Give the dataset a name.
  4. Press "Save the dataset".
  5. The dataset is now under Personal data. The AI can use it once the use has been registered.

What GoalCube does for you

What it looks like: Upload dataFictitious data
The Upload data page, before a file has been chosen.
  • Finds the language of the text, so names and gender are handled correctly.
  • Suggests the protection for each column from its heading and content, e.g. "Person's name → pseudonym" for a column of names.
  • Picks the ID column – the one that tells the rows apart – automatically.
  • Shows the first rows as the AI will see them.
  • Turns cities into regions – in free text too, e.g. "Aarhus" → "Region Midtjylland".
  • Marks words that can point to a person, and checks whether anyone can be recognised from the columns (see below).

If a suggestion is wrong, open Adjust the protection. See Adjust the protection per column.

Yellow words: what can point to a person

Walkthrough: Choose what the AI sees of words that could point to a personStep 1 of 7Fictitious data

Upload a table with free text, for example notes about employees.

All steps (7)
  1. Upload a table with free text, for example notes about employees.
  2. At the top you see who stands out on the columns. Here, every start date is unique. Press the suggestion to turn it into a range.
  3. Below that are the yellow words: names and numbers are hidden, but these words could still point to one person. Health, trade union, religion, politics and criminal offences are made broader by default.
  4. Choose for each word: "Keep", "Make broader" or "Hide". Here, "CFO" is hidden.
  5. Give the dataset a name.
  6. Press "Save the dataset". The AI now sees [personal matter], [removed] and a region instead of the words.
  7. The choices apply to the whole dataset. The yellow words are found using lists and patterns for text in Danish, English, German, French, Dutch, Italian, Spanish, Swedish, Finnish and Polish and can miss something – read the text through.

Names and numbers are hidden, but a text can still give away who it is about, for example "off sick with stress", "member of the trade union", "our only female engineer" or "CFO". GoalCube marks such words in yellow under "Words that can point to a person". For each word you can choose:

Choice The AI sees
Keep The word as it stands.
Make broader A broader word, e.g. [personal matter], [one of few], [senior position] or [distinction].
Hide [removed]

Words about health (pregnancy too), trade union membership, religion, politics, ethnic origin, sexuality and criminal convictions are made broader by default – they become [personal matter]. Other words, such as those about family circumstances, "only" and top positions, stay as they are by default. With Choose for all … you set the same choice for a whole group. The choices apply to the whole dataset, and you see the result in the preview before you save.

The marking works from lists and patterns and covers text in Danish, English, German, French, Dutch, Italian, Spanish, Swedish, Finnish and Polish. The placeholder is in the dataset's language, e.g. [personal matter] in English. It can miss things, so read the text through. If the text is in another language, the box says so.

Can anyone be recognised from the columns?

If the table is about people, GoalCube checks whether anyone is the only one with a particular combination, e.g. age group + region + job title. The box shows how many records stand out, and on which columns. Below the box are up to three suggestions, e.g. Turn "Length of service" into a range, Turn "City" into a region or Do not send "Job title" to the AI. Press a suggestion and the column changes, and the check runs again. "No records stand out" means a lower risk – not no risk.

The AI only gets the risk level for each record (low, medium or high), never the explanation. See also Datasets.

Tips

  • Include only the columns the AI needs.
  • Name the dataset without personal names and secret words – the AI sees the name.
  • Datasets with decisions about people are flagged, so a person always decides.

Limits

See Limits. If you get an error, see Error messages.

Screens recorded from GoalCube 0.26.0 on 22 October 2026 with fictitious data.

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