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This video walks through user data, sales data, integrations, and the joins (connections) that link them together — using a test user, Carol, so you can see exactly what each screen looks like.

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Steps

1

Understand user data

User data is your HR data — the fields that uniquely identify each user (like partner code) and the email IDs used to log in to Compass. Under the Partners template, each user has entries such as Carol01 (partner code) and their work email.
2

Open Data and integrations

Go to Data and integrations → Data sources to see every table available in Compass. Tables are grouped into three sections:
  • CSV — tables you defined by manually uploading a CSV
  • Integrated data — tables populated via native or custom integrations
  • Derived data — tables derived from an existing Compass table using a group-by function
3

Create a new data template

When you upload data for the first time, Compass asks you to define the table template:
  • Field label — the column name
  • Field type — string, float, integer, or date (for dates, pick the format)
  • Mark one field as the mandatory unique identifier for each row
4

Review the uploaded data

Once the template is applied, your data appears with each field in its defined format. In Carol’s row, you can see her partner code alongside her performance fields.
5

Create a connection

A connection is a join between two tables. Click New connection, then:
  • Pick a data source (e.g. Partners)
  • Pick a second table (e.g. Additive invoice data)
  • Set the join key — for example, Partners.partner_code = Additive invoice data.partner_code
6

Choose a join type

Compass supports three join types, covering most use cases:
  • Left join — all rows from the left table + matching rows from the right
  • Inner join — only rows that match in both tables
  • Full outer join — all rows from both tables
7

Use the connection as a view

Once saved, the joined output becomes a named view (for example, Active data). This view combines the user’s identifying fields (first name, email, phone, partner code, role) with their performance data — giving you the base needed to compute KPIs and metrics in the next step.