Before You Start: What Data Do You Need?
Getting value from Evenpay starts with bringing the right data into the system. You don't need everything to be perfect on day one — but the more complete your data, the more accurate and actionable your pay equity analysis will be.
At a minimum, you need three things:
Employee records — who works in your organization
Compensation data — what each person earns
Job structure — how roles and levels are organized
Optional data — like performance ratings, education, certifications, and skills — will make your adjusted pay gap analysis more precise. But you can run a meaningful analysis without them.
Step 1: Prepare Your Data File
Evenpay accepts data via file import (CSV) or through a direct integration with your HR or payroll system. If you're starting with a file import, prepare two spreadsheets.
Step A: Position import
In Job structure import, prepare the file with 1 row per job/role. This import is used to create the structure, and place different jobs/roles to their respective job families, grades and career tracks.
Field | Required | Example | Notes |
Job/Role | Yes | Marketing Manager 2 | Employees are grouped by Job/Role, and Job/Role can be linked to a specific Grade and Career track |
Job Family | Recommended | Marketing | A group of positions that involve work in the same functional occupation and have related core knowledge and background requirements. |
Grade | Yes, if grade exists | 3 | The Grade that the Job belongs to. If no Grade has been assigned, leave empty. |
Career track | Recommended | Individual Contributor (IC) | The Career Track that this Job belongs to. |
Step B: Employee Import
In Employee import, prepare the file with 1 row per employee. This import is used to create employees and enrigh their information, and link them to a specific job/role.
Field | Required | Example | Notes |
First Name | No | Alice | Not mandatory, but improves understanding of which employee is in question |
Last Name | No | Wonderland | Not mandatory, but improves understanding of which employee is in question |
No | Used in order to automate employee right for knowledge | ||
Employee ID | Yes | 1234 | A unique identifier for each person (from your HR system) |
Gender | Yes | Female | Required for pay gap calculations |
Date of birth | Recommended | 20.10.1990 | Used for age-related analytics |
Education | Optional | Master's Degree | Not mandatory, but can be used in Pay Equity analysis |
Organization Unit | No | Services | Used in analytics and setting of user rights |
Position title | No | Account Executive | Employees personal Position title. A single title can belong to multiple Jobs/Roles, or Grades, and doesn't have to be unique.
|
Job/Role | Yes | Sales Specialist 1 | Employees are grouped by Job/Role, and Job/Role can be linked to a specific Grade and Career track. A single Job can belong to a single Grade. Primary grouping variable to define "same" jobs/roles.
|
Job/Role start date | Yes | 15.2.2023 | Used to calculate tenure |
Employment type | Yes | Full time | Full-time, part-time, etc. |
Weekly working hours | Yes | 37.5 | Amount of hours worked in a week |
Manager ID | No | 2345 | The employee ID of this individuals Manager / Supervisor. Used for user rights. |
Country | No | France | The country of employment. Used in Pay Equity analysis. |
City | No | Paris | The city of employment. Used in Pay Equity analysis. |
Performance rating | Optional | 3 | Most recent or average of last 1–3 cycles |
Currency | Yes | Euro | The salary currency |
Base salary | Yes | 3000 | Guaranteed base pay is the sum of basic salary including guaranteed allowances. Included fixed payments, that the employee is guaranteed to receive. Does not include variable pay (commission, bonuses, fringe benefits) |
Commission (on target) | Recommended | 1000 | If the employee reaches the set targets, what is the monetary compensation / commission of short-term incentives? Short term incentives refer to a period of one year or less. |
Bonus (on target) | Recommended | 1500 | If the employee reaches the set targets, what is the monetary compensation / bonus of short term incentives? Short term incentives refer to a period of one year or less. |
Phone benefit | Recommended | 20 | Value of monthly phone benefit on top of Base Salary |
Lunch benefit | Recommended | 50 | Value of monthly lunch benefit on top of Base Salary |
Car benefit | Recommended | 500 | Value of monthly car benefit on top of Base Salary |
Housing benefit | Recommended | 1000 | Value of monthly housing benefit on top of Base Salary |
Other fringe benefit | Recommended | 100 | Value of other Fringe benefits on a monthly basis on top of Base Salary |
Additional components | Optional | eg. Company, Function, Team, etc. | Bonuses, allowances, fringe benefits, grouping variables — include if relevant to your analysis |
Tip: Don't worry if some columns are missing or incomplete. Evenpay will flag which data is missing after import and tell you how it affects your analysis.
Step 2: Import Your Data
Once your files are ready, navigate to Settings → Data Import in Evenpay. From there, you can upload your file and map your columns to Evenpay's fields.
In first step, import Positions, and when the structure is in place, import Employees.
The import process works like this:
Navigate to Settings and Data & Integrations — From the navigation list on the left.
Choose "Data" — In the top of the screen.
Select the correct import flow — Either "Import Positions" for job structrue, or "Import Employees" for employees.
Upload your file — CSV format. One row per job/role & employee.
Map your columns — Evenpay will suggest matches automatically. Review and confirm each mapping.
Review validation results — Evenpay checks for missing required fields, duplicate IDs, formatting issues, and invalid values. Fix any flagged errors before proceeding.
Confirm and import — Once validation passes, confirm the import. Your employee records, compensation data, and structure will be loaded into the system.
If you're connecting via an HR system integration (e.g. through your HRIS or payroll provider), go to Settings → Integrations to set up the connection. If you don't see your integration in the list of available integrations, or you need assistance in setting up the conenction, reach out via Intercom chat in the app or through our support [email protected].
Once connected, data can be synced automatically on a schedule you define.
Step 3: Set Up Your Organization Structure and validate your imported data
After your data is imported, Evenpay will create organization units and job levels based on the values in your file. You'll want to review and refine these to make sure they reflect how your organization actually works.
Go to Positions in the main navigation. Here you'll find:
Positions — the Jobs/roles that you have imported
Job levels — the levels or grades in your job structure (e.g. IC1–IC5, M1–M4). These are central to pay equity analysis because they define what "equal value work" means in your organization.
Job families — functional groupings like Engineering, Sales, Finance. These help you analyze pay gaps by function.
Organization units — the departments and divisions in your company. Check that the hierarchy is correct and that each employee is assigned to the right unit. If you want to create a hierarchical structure, select the Organization units that you want to place under another Parent unit, click on "Edit Fields", and choose the parent unit.
If your job levels weren't in your import file, or if they don't yet follow a consistent structure, this is the right moment to define them. Evenpay also supports building a full job architecture from scratch — see the Job Architecture guide for more detail.
Next, go to Employees in the main navigation. Here you'll find:
Employees — The full list of employees. Select an individual employee and
Validate Overview — Go through the Personal Information, Employment Information, and any Custom Fields you have imported. Make sure all the necessary information is correct.
Validate Compensation — Navigate within the employee page to "Compensation", and make sure that the Agreed Compensation shows the total compensation correctly. Next make sure that the Agreed Compensation Breakdown shows all the imported compensation elements.
If the employee is missing Overview related elements, double check your import file and retry the import by updating the employees by employee ID.
If the employee is missing Compensation related elements, navigate to Settings, and Compensation.
Go through all the Salary Components, and if missing assign a Type and Group for the Salary Components. Make sure that the Salary Components that you want to include in Total Compensation have "Include in Total" ticked, and depending on your preference include wanted Salary Components to Salary Band by selecting "Include in Salary Band"
Go through all the Benefits, and if missing, assign a Category and a Group for all the Benefits. Make sure that the benefits that you want to include in Total Compensation have "Include in Total" ticked, and depending on your preference include wanted Benefits to Salary Band by selecting "Include in Salary Band"
Step 4: Configure Your Pay Philosophy
The pay philosophy tells Evenpay which factors your organization uses to determine pay — and therefore which factors should be used to explain (and separate from) the pay gap.
Go to Settings → Pay Factors and enable the factors that apply to your organization. The selectable Pay Factors include by default:
Pay Factor | Typical relevance | Description |
Job Grade | High | The global grade that the employees are in. Shows to what proportion the Job Grade affect the Gender Pay Gap. |
Job Family | High | The group of Jobs the employee is in. Shows to what proportion does Job Family affect the Gender Pay Gap. |
Location | High | The Primary location of the employee. Shows to what proportion does the Location affect the Gender Pay Gap. |
Education | Moderate | The highest level or type of education attained by the employee. Shows to what proportion Education affects the Gender Pay Gap. |
Experience | Moderate | The total relevant work experience of the employee, typically measured in years. Shows to what proportion Experience affects the Gender Pay Gap. |
Tenure in position | Moderate | The length of time the employee has been in their current role. Shows to what proportion Tenure in position affects the Gender Pay Gap. |
Performance | Moderate | The employee’s performance rating or evaluation score. Shows to what proportion Performance affects the Gender Pay Gap. |
Organization Unit | Low | The specific department, team, or business unit the employee belongs to. Shows to what proportion the Organization Unit affects the Gender Pay Gap. |
Skills | Low | The set of skills assigned to or demonstrated by the employee. Shows to what proportion Skills affect the Gender Pay Gap. |
Skill fit | Low | The degree to which an employee’s skills match the requirements of their role. Shows to what proportion Skill fit affects the Gender Pay Gap. |
Certifications | Low | The professional certifications held by the employee. Shows to what proportion Certifications affect the Gender Pay Gap. |
Certification fit | Low | The degree to which an employee’s certifications are relevant to their role. Shows to what proportion Certification fit affects the Gender Pay Gap. |
Custom | — | Any additional organization-specific factor defined by the user. Shows to what proportion the Custom factor affects the Gender Pay Gap. |
Only enable factors that genuinely drive pay decisions in your organization. Including factors that aren't actual pay criteria can skew your adjusted gap results.
Note: Gender is never included as a pay factor — it's only used to compare outcomes between groups. For in depth knowledge on the Pay Gap calculation methodology, navigate to How Pay Gap Analysis Works in Evenpay.
Step 5: Verify Your Data Before Running Analysis
Before running your first pay equity analysis, take a few minutes to sanity-check your data. Go to Employees and look for:
Employees without a job level assigned — they won't be included in the analysis
Employees with zero or missing salary — they're automatically excluded
Employees without a gender recorded — they can't be included in gap calculations
Organization units that look incorrect or inconsistently named
The more complete your data, the more accurate and useful your results will be. Small gaps in data coverage are normal — just be aware of them when reading your results.
Step 6: Run Your First Pay Equity Analysis
Once your data is in order, navigate to Pay Equity and Overview to start a new analysis session.
Run first analysis — During the first session, you will see the ability to Run the first analysis, or import data. Proceed to click on the "Run first analysis"
Agreed Compensation analysis — By default, Evenpay will use your the salary parameters that you have imported. You can adjust whether you want to include total compensation, or just certain compensation factors from the top bar under "Compensation".
Unadjusted pay gap — Unadjusted pay gap shows the average salaries of males and females, containing the selected compensation factors. By hovering over the bar graph, you'll see compensation factor specific gaps.
Adjusted pay gap — Adjusted pay gap shows the portion of gender pay gap, which is unexplained after taking into consideration the selected variables.
Show model transparency — By clicking on "Show model transparency", you will see the statistical performance of the regression analysis, and the Factor statistics. You can use these statistics to validate your model's performance, and significance.
For a detailed explanation of how to read the results, see How Pay Gap Analysis Works in Evenpay.
Keeping Your Data Up to Date
Evenpay works best when your data reflects your current organization. We recommend updating your data:
After each salary review cycle — so your analysis always reflects current compensation
When your headcount changes — new hires, departures, promotions
Before generating a pay equity report — for regulatory or internal reporting
If you've set up an integration, data syncs automatically. If you're using file imports, re-import your updated file following the same steps above. Evenpay will update existing records and add new ones.
Frequently Asked Questions
What file formats does Evenpay accept for import?
Evenpay accepts CSV files. Make sure your file has a header row and one employee per row for Employee Import, and a one job/role per row for Position Import.
We don't have a formal job level structure yet. Can we still use Evenpay?
Yes. You can import your data with job titles only, and then build your job level structure inside Evenpay. However, having a clear job level structure significantly improves the quality of your pay equity analysis. See the Job Architecture guide for help setting one up.
What happens if some employees are missing salary data?
Employees with zero or missing salary are automatically excluded from the pay equity analysis. They'll still appear in the employee list and you'll see a note about how many were excluded when you run the analysis.
Can I import data from our HRIS directly?
Yes. Evenpay supports integrations with common HR and payroll systems. Go to Settings → Integrations to connect your system. Once connected, you can sync data automatically without manual exports. If you do not see your preferred integration in the list, or require assistance setting it up, reach out to our support via in-app chat or [email protected].
How often should we update our data?
We recommend updating after each salary review cycle and whenever headcount changes occur. If you're using an integration, this happens automatically. If you're using file imports, plan a re-import after each major people or compensation change.
Our data has some gaps — missing experience values, no education data. Is the analysis still valid?
Yes. Evenpay automatically disables factors that have too much missing data and tells you which factors were excluded. Your results will clearly indicate the data coverage for each factor used. The analysis is still valid — just be aware of which factors were included when interpreting your results.
