25 HR Data Sources for Analytics
“Where there is data smoke, there is business fire,” said Thomas Redman, business author and data expert. Understanding your HR data sources helps you spot issues early, make smarter decisions, and clearly show HR’s impact on the business.
Solid HR data enables organizations to better understand their people, processes, and potential. But what data sources can be used for data analytics in Human Resources?
This article lists common HR data sources you can use as a starting point for your people analytics efforts, and also provides somes suggestions on how to work with data sources in HR.
Contents
Types of HR data sources
HR systems data sources
Other HR data sources
Business data sources
External data sources
7 tips for working with HR data sources
FAQ
Types of HR data sources
HR data sources can be categorized into four main groups:
- HR systems data: Data from the company’s Human Resources Information System (HRIS) includes most of the company’s data about its employees. Common examples of HRIS providers, especially at large companies, include Oracle, SAP, and Workday. Other systems include the applicant tracking system (ATS) and learning management system (LMS).
- Other HR data: Some HR data is essential for data-driven decision-making but is not included in the HRIS. This is often data acquired through surveys or other measurement techniques.
- Business data: Although it is impossible to cover all business data when doing people analytics, it plays an increasingly important role in connecting workforce trends to broader company outcomes. This includes data from finance, sales, operations, and customer feedback systems.
- External data: Data from external sources, such as industry reports and trends and even data on the flu and the weather, also inform HR strategies.
HR systems data sources
The company’s HRIS contains data on the most common HR functions, including recruitment, performance management, and talent management. Although the modules in the HRIS differ from company to company, there is often a common group of modules that contain data useful for people analytics.
Recruiting data
Recruiting data gathered from the ATS, which is part of or connected to the HRIS, is a common data source for analysis. It includes the number of candidates who applied, their CVs, other characteristics, and data about the recruitment funnel, recruitment sources, selection, and so on. The ATS is the most common source of input for recruiting metrics.
Demographic data
Another key data source is the employee records in the HRIS. These include employees’ IDs, names, genders, dates of birth, residences, positions, departments, cost center specifications, termination dates, and so on. These demographic data are often included in an analysis as control variables.
Also, when data is combined manually, this often provides a database enriched with data from other systems by matching an employee’s ID as a unique identifier.
Absence data
Recorded absence data is another key source of HR data. Managers or HR usually track sick days and record them in a system. Some organizations also record absence reasons. Similarly, other types of leave, like parental and FMLA leave, and tardiness data are also captured.
Performance management data
The performance management system (PMS) is often part of the HRIS and contains information about performance management, including employee reviews and performance ratings.
Learning management data
The learning management system (LMS) is another source of HR information. It contains a course offering and registers employees’ progress through different programs.
However, not all learning data is stored in the LMS. For example, the finance department often holds information on expenditures from external sources, while learning impact and effectiveness are typically measured using surveys. We’ll discuss this further below.
Job architecture
Job architecture is a framework that serves as a foundation for compensation. Other related terms are job grading and job leveling. Different roles are organized into salary scales with bands and grades with maximum reward levels.
For example, a company might group software engineers into levels from Junior to Senior to Lead, each tied to a salary band and clear expectations.
When used in analytics, it helps answer questions like:
- Are pay levels consistent across similar roles or departments?
- How many employees are in roles with growth potential vs. capped roles?
- Are certain levels seeing higher turnover or promotion rates?
- How well is the organization developing talent across job grades?
Compensation & benefits
An essential part of keeping employees engaged is making sure they receive fair compensation for their work. Compensation and benefits data is also stored in the company’s HR system and/or the payroll system. It includes compensation package details and secondary employee benefits.
Succession planning
Succession plans are also often found in the HR system. The amount of data regarding this depends on the organization’s succession planning practices. Example data includes leadership development data and data about which employees are next in line for certain positions.
Talent development
This includes data on programs aimed at growing internal talent—like leadership development tracks, high-potential employee lists, or mentorship participation. While some elements may live in the LMS, broader talent development data often comes from multiple sources, including the HRIS, talent reviews, and program tracking tools. It provides insight into how the organization invests in future leaders and internal mobility.
Exit interviews
Depending on the organization, exit interview data may also be stored in the HRIS. The data provides information on, among other things, why employees left the organization. It can be valuable for gaining insights into reducing employee turnover and improving employee experience.
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Other HR data sources
These are sources that typically fall outside the HRIS, often because they’re harder to collect through standardized processes. While not always structured or centralized, this type of data adds important context to how employees work, collaborate, and engage with the organization.
Learning programs data
Data on learning effectiveness and learning program evaluation is often stored separately from the LMS and managed by the learning department. This data may live in Excel spreadsheets and survey collection tools.
Integrating this data into a broader HR reporting and insights database is an early priority for organizations that are starting to work on learning analytics or trying to advance their reporting.
Travel data
Travel data provides useful context about employee roles and work patterns, especially for global or client-facing teams. Since it’s typically managed through finance or travel booking systems, it usually sits outside the HRIS and needs to be pulled in separately for analysis.
Mentoring data
Mentoring programs can be a rich data source when tracked properly. Information such as who participates, how long mentorships last, match types (peer, cross-functional, senior-junior), and feedback from participants can help HR assess program reach, effectiveness, and its role in career growth.
This data is useful for understanding development trends, supporting DEIB goals, and identifying future leaders. It may come from dedicated mentoring platforms, program coordinators, or survey tools.
Employee survey data
A large part of HR data is collected through surveys. This can range from a poll on the quality of food in the cafeteria, a survey by the CEO about their popularity, to the traditional employee engagement survey.
Most companies send out surveys in a decentralized way, which can lead to scattered survey data throughout the organization and survey fatigue. Collecting all this data in one place helps provide better insight into employee survey data.
Engagement surveys
The engagement survey is sometimes part of the employee survey data bank we mentioned. However, engagement surveys are often collected by a third party to guarantee anonymity. This means engagement surveys function as a separate data source with their own structure and reporting.
Wellbeing and wellness
Depending on the organization, records may be available around (participation in) employee wellness programs. This is another data source that is not typically captured in the HRIS.
Organizational social network data
Data on organizational social networks—also referred to as organizational network analysis (ONA)—look at how employees connect and collaborate across the organization. It can reveal informal influencers, communication bottlenecks, and cross-team dynamics.
Data for ONA can come from various sources, including collaboration tools (like email or chat platforms), calendar data, phone logs, or dedicated network surveys. While it requires careful handling due to privacy concerns, ONA can provide valuable insight into how work really gets done beyond org charts.
Business data sources
The scope of business data is almost endless. Many business data sources can be used for people analytics. Here are some of the most important ones.
CRM data
The company’s Customer Relationship Management system holds a wealth of data on customers. This includes customer contact moments, NPS scores for those touchpoints, lead scoring, etc. This data can be crucial outcome data used to measure the impact of people policies on customer-facing employees.
Financial data
Financial data is another key business data source. It can be used for simple analyses of L&D spending or more complicated analyses of labor costs, ROI calculations for different interventions, and other financial analyses.
Production management data
Production management systems track operational metrics like scheduling, service calls, delivery rates, and turnaround times. While primarily used by operations teams, this data can serve as outcome data in people analytics—helping HR assess how workforce policies affect productivity, efficiency, or service quality in production and delivery roles.
Sales data
Sales data is another outcome measurement. Examples include sales per store, which can be used as outcome data to measure the impact of different HR policies, like learning program effectiveness.
External data sources
In addition to internal systems, external data can play a key role in shaping people strategies and understanding workforce dynamics. Here are a few notable categories:
Job market and salary benchmarking databases
Sources like Glassdoor, Payscale, and the Bureau of Labor Statistics (BLS) provide compensation and labor market data. These benchmarks help HR stay competitive in pay, understand talent availability, and spot shifts in demand for certain skills.
Competitor and industry reports
Research from organizations like McKinsey, SHRM, and Deloitte offers insights into hiring trends, workforce benchmarks, and evolving HR practices. These reports help contextualize internal data and guide strategic planning.
Government and compliance databases
Agencies such as the EEOC, OSHA, and IRS publish data on employment law, safety, benefits, and workforce trends. This information supports compliance efforts and can inform risk management and policy development.
Job boards and recruiting platforms
Job boards and recruiting platforms like LinkedIn, Indeed, and ZipRecruiter can serve as external data sources by offering insights into job posting performance, candidate availability, and market competitiveness. Employers using these tools can access analytics to refine sourcing strategies and adapt to labor market trends. Even without full access, these platforms can still provide useful signals about hiring demand and industry standards.
7 tips for working with HR data sources
Working with a multitude of different HR data sources requires structure and effective HR data management. Here are seven best practices to consider implementing:
- Centralize data: Use a Human Resources Information System (HRIS) as a single source of truth for employee data. Integrate other HR systems, such as your ATS, LMS, payroll, and performance management systems, to eliminate data silos. Use HR analytics platforms or data warehouses to consolidate information from multiple sources.
- Maintain compliance and data security: Follow guidelines and regulations to protect employee privacy and maintain HR compliance. Restrict access to sensitive HR data based on role-based permissions and encrypt and back up HR data.
- Train HR professionals on data literacy: Data-literate HR practitioners can deduce relevant information from the data, think critically about what the data shows, and apply it suitably for specific purposes.
- Combine HR data with business data: You can combine HR data with business data (e.g., sales and customer service metrics) to assess how your HR practices contribute to your organization’s business goals. The HR value chain can help you with this analysis.
- Establish clear data ownership and governance: Assign HR data stewards to oversee data integrity and compliance, define data ownership for each HR function (e.g., the payroll team manages salary data, the recruitment team handles ATS data, etc.), and create HR data governance policies covering data access, modification rights, and reporting structures.
- Continuously improve data strategies: Review your HR data collection methods regularly to eliminate redundant data sources and optimize your processes.
- Ensure data quality: You want to make sure the data you collect and work with is accurate, meaning complete, free from errors, and up to date.
On a final note
The short answer to the question of which data sources can be used for data analytics in HR is that there are many different data sources.
The slightly longer answer is that every organization has structured its HR and business data differently. Some of the data and sources mentioned in this article may be available, but other data may not (yet) be. That’s why it’s important to map out what’s already accessible, identify gaps, and build a plan to gradually bring more data into your HR analytics efforts.
FAQ
Common HR data sources for HR analytics are HR systems data, other HR data like employee surveys, business data, and external data.
External data sources of HR data include job market and salary benchmarking databases, competitor and industry reports, government and compliance databases, and job boards.
Typical internal sources of HR data include the HRIS, the ATS, the LMS, and data collected from employee surveys.
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