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Uploading Employee Database

How to upload your employee database to Crosschq Analytics/Pulse

There are two ways to upload your employee data to Crosschq. You can manually import employee data using a CSV file, or you can set up an automatic sync via an API between Crosschq and your Workday HRIS. 

In this article, you will learn how to manually import your employee database. Click HERE to learn how to sync your data via an API.

When employees are imported or synced with Crosschq Analytics or Crosschq Pulse, Crosschq automatically pulls pre-hire data from Crosschq 360 reports and from your ATS (contact support@crosschq.com to see if your ATS supports this feature). 


Uploading an employee database via .csv file into your analytics instance is an important step in assessing your Quality of Hire. Each row in the employee database .csv is an employee record, and each employee record will have multiple fields (columns) of data to segment, filter, and analyze your hiring outcomes. However, there are rules that must be adhered to for certain fields, which is what we'll be covering in this article.

Read the sections below to learn more about the steps necessary to manually upload your employee database:


Uploading your employee data manually via CSV file

Employee .csv files can be uploaded using the Import Employee CSV section. Your employee CSV must strictly follow the formatting guidelines we provide in this template to upload correctly, including column titles, which are used to match against expected values.

We have created a separate template with instructions regarding data formatting and required information, click here to download it.

Failure to follow the formatting will result in errors that must be corrected to successfully upload your data. Columns/fields that do not follow this guideline will be ignored. Read the Mandatory Fields section to learn more about upload requirements.

To upload your database, drop the .csv into the window within Crosschq or upload via the browse function and select the appropriate file. 

Mandatory Fields

Mandatory fields are required in order to create an employee record in your analytics instance.  If there is no data in the mandatory fields for an employee, that employee will not be uploaded into the database and thus will not be included in any analysis.  If there are too many empty values or invalid values in a mandatory field, the entire upload will fail.  

The following fields are mandatory, and must be included in each employee record: 

  • Employee Email Address
  • First Name
  • Last Name
  • Hire Date
  • Manager Email Address

You will be able to upload your CSV based on the percentage of missing data.

Error Thresholds

1) For the Employee Email Address, First Name, and Last Name, if more than 10% of these values are missing, the CSV file is not uploaded and the following error will prompt:

"File Error: The following required fields are missing or have incorrect formats: Employee Email Adress (20% in error), First Name (15% in error), Last Name (15% in error).

Admins will need to review the .csv file, locate the rows with the missing data, populate the missing fields, and reupload the file.

If less than 10% of the data is missing, the file is uploaded, but the employees that are missing this data will be excluded.

2) For the Hire Date and Manager Email Address, if more than 30% of these values are missing, the CSV file is not uploaded and the following error will prompt:

"File Error: The following required fields are missing or have incorrect formats: Hire Date (36,7% in error)"

Admins will need to review the .csv file, locate the rows with the missing data, populate the missing fields, and reupload the file.

If less than 30% of the Hire Date data is missing, the file is uploaded, but the employees that are missing this data will be excluded.

If less than 30% of the Manager's Email Address data is missing, the file is uploaded, and the employee record is created but a yellow warning will prompt.

Optional Fields

Optional fields should be used whenever possible, as this data enriches the analytics and reports that are provided by Crosschq Analytics. 

The rest of the fields are optional and will not conclude an upload due to errors or invalid values:

Job Title
  • Org Level
  • Department
  • Manager Email Address
  • Employment Type
  • Pay Type
  • Termination Date
  • Termination Type
  • Termination Disposition
  • Termination Reason
  • Work Address
  • Gender
  • Date of Birth
  • Marital Status
  • Ethnicity
  • Education Level
  • Under-graduate Institution
  • Graduate Institution
  • Recruiter
  • Candidate Source
  • Interview Scores
  • is_sales
  • Corporate Division

CSV Rules

In addition, please follow these rules:

  1. .csv must be the file’s format and extension and it cannot have a password.
  2. Field names for personal details must match the template field names.
  3. The file header row (first row) can only contain text characters (do not use invisible control characters like Shift-In).
  4. Column names are used to identify the information - make sure that the titles of your columns exactly match those in the .csv template. For example, Employee Email Address should be formatted “Employee Email Address”.
  5. Dates entries (for example, the Hire Date column) should match one of these formats:
    1. Mmm dd, yyyy;
    2. MMM DD YYYY;
    3. YYYY-MM-DD;
  6. The emails provided in the Employee Email Address column must be unique. Emails in the Manager Email Address column must be the valid email of an employee.
  7. Termination Type should be filled with one of the following: voluntary, involuntary.
  8. Employees will be considered terminated when their records have an End Date or when the record is no longer available/not included in subsequent uploads. 

Fields with restricted and invalid values

  • Org Level, Employment Type, Pay Type, Termination Disposition, and Marital Status, have fixed values in our database mapping table. However, if a user tries to upload a value not included in the mapping table, it will not cause an error as the new value will be dynamically added to the mapping table. For example, if a user enters "I don't know" in the Marital Status field, that employee's record will show "I don't know".
  • Gender and Ethnicity, on the contrary, also have fixed values in our database mapping table, but if a user tries to upload a value not included in the mapping table, that unique value will map to "other" in the database. For example, if a user enters "I don't know" in the Ethnicity field, that employee's record will show "other".
  • If there is invalid data on the file, the CSV file will be uploaded but the record of the employee whose data is invalid will not be created. For example, when you upload a file with text that does not include an '@' symbol or your company domain name in the Employee Email Address field or when you fill in the Hire Date field with plain text, the file is uploaded, but that employee record is not created. The following warnings will appear: