> ## Documentation Index
> Fetch the complete documentation index at: https://docs.outkept.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Mapping data

> Turn the columns and values in your file into the tags and placeholders we use.

Your data source uses your organisation's own naming. Our platform uses a predetermined set of
columns, and a fixed set of tags and placeholders. Mapping is where the two are connected.

This matters because those tags and placeholders are what make a simulation credible.
Firstname becomes a placeholder inside the email, and Function title becomes a targeting tag
our ethical phishers can aim at. Data we cannot map is data they cannot use.

<Info>
  All tags are anonymised. They let phishers target a group without ever linking back to an
  individual.
</Info>

## The two steps

They happen in order, and the difference between them is easy to miss.

<Steps>
  <Step title="Mapping column names">
    Match the headings in your file to our columns. Your `Dept` column becomes our
    **Department Name**.

    See [Mapping column names](/mapping-column-names).
  </Step>

  <Step title="Mapping column values">
    Match what is inside those columns to our tags. The value `Finance` in that column becomes
    our **Finance** tag.

    See [Mapping column values](/mapping-column-values).
  </Step>
</Steps>

<Note>
  The import wizard does much of this for you. It recognises common column names, and applies
  values it has seen before automatically. What is left is what it could not work out on its
  own.
</Note>

## How long it takes

Column names go quickly. Column values are the most time-consuming part of onboarding, at
roughly fifteen to thirty minutes depending on how varied your data is.

<Tip>
  You only do it thoroughly once. Mappings are remembered, so later imports and new joiners
  reuse what you set up here.
</Tip>

## Next

Start with [Mapping column names](/mapping-column-names).


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