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Data checklist: Payroll analysis

  • Updated


Learn which fields are required to leverage the existing configuration and control points for payroll analytics.

Data checklist

Columns Description
Employee ID Each employee's unique identifier.
Department ID Each department's unique identifier (or equivalent category, such as cost center).
Pay Code Payment type (regular salary, bonus, contract, overtime etc.).
Check Date Date associated with the payment amount.

The monetary value associated with the entry.

Note: All amounts in an analysis use the same currency.


General data expectations

Data must be/contain...

  • In .xlsx, .csv, or another supported format (.pdfs are not supported)
  • Entry based
  • Densely populated (limited blank cells)
  • At least 200,000 entries (rows)
    • Larger data sets are preferred (1+ million rows preferred)
    • MindBridge can analyze smaller datasets (20,000 rows), but some algorithms will not be as effective. Leveraging multiple years of data could add to the data volume.
  • In a consistent format across columns 
  • A healthy variety of values, including:
    • Numerous values for any categorical columns to allow richer categorization and pivoting of visuals
    • Limited synthetic, fake or columnar data


  • There are no missing values within the required fields
  • Each row represents one unique monetary flow (i.e., the details about one unique payment)
Note: If a row is missing a required field, that row of data will be excluded from the analysis.

Additional columns relevant to your business could be mapped in the analysis for filtering. These values will not be used in risk scoring. Examples of data that would be useful for analysis:

  • Cost Center
  • Deductions
  • Role/Position

Anything else on your mind? Chat with us or submit a request for further assistance.

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