Table of Contents
- Introduction
- Employee data
- Activity data
- Organizational Hierarchy data
- Location data
- Job data
- Compensation data
- Disability data
- Ethnicity data
- Veteran data
- Applicant data
- Requisition data
- Employee opinion survey data
- Employee performance data
- Exit interview data
- New hire performance data
- Hiring leader satisfaction survey data
Introduction
HR (Human Resources) Analytics, also known as “People Analytics”
uses the power of data analytics to solve problems in the HR space and helps HR functions become more efficient and productive. There are several teams within the whole HR function, and they have their unique set of problems to solve. Techniques used to solve some of the issues might be simple and basic while some problems may require rigorous data analysis and complex model building. But, it all starts with the right data. Data is a crucial element that creates the foundation for all the data analytics or data science work that comes at a later stage. In today’s article, we will try to understand the types of data and data elements in each of the datasets that are used in People Analytics.
Employee data
Employee data is the data of the workers employed at the organization at any point in time. It consists of data of current as well as past employees. This is one of the crucial datasets used in People Analytics and most of the analyses revolve around this. Employee ID, name, date of birth, gender, hire date, active or inactive employee are the fields included in this data. As this data contains personal identifier information, it needs to be handled cautiously.
Activity data
This data captures all the events related to any changes that happened to an employee's job, location, leader, assignment, and events such as promotion, internal transfer, global transfer, hiring, termination, or retirement. The variables included in this data are employee ID, assignment ID, start date, end date, action code, and reason code. Action code generally specifies what type of event it was and the reason code gives more details about it.
Organizational Hierarchy data
This data maintains the organizational hierarchies such as enterprise segments, legal entity names, business units, departments, and so on. It captures all the data over several years. It does contain current mappings as well as historical ones. Sometimes some departments are moved from one business unit to another. Then these types of events are also captured in this data.
Location data
Employee work location as well as base location details are captured in this data. Any changes to any of the locations are also recorded in the location data. It contains variables like address, city, postal code, and country for both types of locations along with a few other fields such as employee ID, start date, and end date. Whenever there is any change in any of the location variables for an employee, an additional row is added for the employee that captures the changes along with the start date as the date of the change.
Job data
All details related to a job that the employee performs at the organization are captured here. Employee ID, start date, end date, assignment ID, department, job code, job title, full-time or part-time, regular or temporary, job function, and business unit are the main variables that are part of this data. It also captures any changes in any of these fields and when exactly the change happened.
Compensation data
It pertains to employee salary details. It contains fields such as employee ID, start date, end date, salary amount, currency, compa ratio, incentive amount, and a few other fields that vary from org to org.
Disability data
This data is more specifically tracked for US employees. It contains details such as employee ID, start date, end date, disability type, and description of disability. Many times this data also includes if a person was This is one of the extremely confidential datasets and only employees with due permissions should be able to access this data.
Ethnicity data
This is also mainly tracked for US employees. It contains variables such as employee ID, race, and ethnicity.
Veteran data
This is also mainly tracked for US employees. It contains variables such as employee ID, whether an employee is a military veteran or not, and a few more details in case an employee is a military veteran.
Applicant data
This data contains details of the applicants who have applied to various job openings posted by the organization. This data contains an applicant’s details such as applicant ID, name, location, education, years of experience, gender, date of birth, date of application, and other details such as requisition ID, hiring leader, recruiter name, source, and sub-source. It also tracks the stages that an applicant has gone through such as resume screening, technical discussion rounds, hr discussions, and whether the candidate was selected or not, if rejected then the reason for rejection. If a candidate is selected, then the fields such as offer acceptance date, and expected start date are also tracked.
Requisition data
This data includes all the data related to all the job postings. It captures requisition ID, number of openings in the requisition, requisition approval date, requisition start date, requisition close date, number of openings closed, hiring leader name, recruiter details, department, business unit, job function, job code, and job title fields.
Employee opinion survey data
It is very important for every organization to know if the employees are satisfied and engaged. For that, organizations run employee opinion surveys that are completely anonymous. Generally, an external agency is hired to run these surveys and collect the data. Every employee is given a unique respondent ID and all the questions revolve around employee satisfaction with respect to job, leader, strategy, and culture. Some organizations also include questions related to pay fairness and progression opportunities. The questions differ from organization to organization, but the intent is to measure how happy and engaged the employees are and to identify the areas of improvement.
Employee performance data
High-performing employees are an asset to any organization. Employees are assessed every year and given ratings and feedback for their contributions during a particular year. So the data generated as a result of this process is performance data. The data contains employee ID, leader ID, performance year, performance rating, and performance comment.
Exit interview data
After an employee decides to leave the organization voluntarily, the employee as well as the leader are asked to fill out exit surveys. The employee survey comprises questions like a reason/s for separation, where the employee would like to rejoin, the overall experience of the organization, and a few more questions. The leader survey tries to collect the answers to questions like the primary reason for termination, which retention tactics are used to retain the employee, if the leader would recommend the employee to rehire in the future, whether the employee was a critical resource, what was the impact of employee’s departure on work, whether the employee was a high-performer. The survey may also include questions like the name of the organization that an employee is going to join and the percentage of salary hike they received from the next organization.
New hire performance data
Every new hire is assessed for their performance in their first 90 days of joining. In this survey, the organization tries to gather information about the fitment of the new hire, if the new hire can perform the duties the way they are expected, if there are any red flags, etc.
Hiring leader satisfaction survey data
In this survey most of the questions revolve around the overall experience of the hiring process. The leader of a new hire fills out this survey. There are questions like feedback to the recruiter who assisted in the hiring process, quality of resume, quality of candidates, accuracy of the job posting, overall assistance provided by the recruiting department throughout the hiring process, etc.
This is the list of basic datasets that are used for People Analytics work in any organization. There are a few other survey datasets such as Hogan or benchmarking datasets such as Saratoga. A few external datasets are also used for various analyses. If you are curious and want to know more about these datasets, then let me know in the comments section. I will try to cover that in a separate article. There are a variety of problems in the HR space that are solved using one or combining multiple of these datasets. I am planning to write about it in my upcoming article. Stay tuned! Copyright © Priyanka Datar 2025. All rights reserved.
Loading comments...