Context

Similar to others who have created HR data sets, we felt that the lack of data out there for HR was limiting. It is very hard for someone to test new systems or learn People Analytics in the HR space. The only dataset most HR practitioners have is their real employee data and there are a lot of reasons why you would not want to use that when experimenting. We hope that by providing this dataset with an evergrowing variation of data points, others can learn and grow their HR data analytics and systems knowledge.

Some example test cases where someone might use this dataset:

HR Technology Testing and Mock-Ups Engagement survey tools HCM tools BI Tools Learning To Code For People Analytics Python/R/SQL HR Tech and People Analytics Educational Courses/Tools

Content

The core data CompanyData.txt has the basic demographic data about a worker. We treat this as the core data that you can join future data sets to.

Please read the Readme.md for additional information about this along with the Changelog for additional updates as they are made.

Acknowledgements

Initial names, addresses, and ages were generated using FakenameGenerator.com. All additional details including Job, compensation, and additional data sets were created by the Koluit team using random generation in Excel.

Inspiration

Our hope is this data is used in the HR or Research space to experiment and learn using HR data. Some examples that we hope this data will be used are listed above.

Contact Us

Have any suggestions for additions to the data? See any issues with our data? Want to use it for your project? Please reach out to us! https://koluit.com/ ryan@koluit.com

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4MM+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

• The HR dataset is a collection of employee data that includes information on various factors that may impact employee performance. To explore the employee performance factors using Python, we begin by importing the necessary libraries such as Pandas, NumPy, and Matplotlib, then load the HR dataset into a Pandas DataFrame and perform basic data cleaning and preprocessing steps such as handling missing values and checking for duplicates.

• The dataset also use various data visualization to explore the relationships between different variables and employee performance. For example, scatterplots to examine the relationship between job satisfaction and performance ratings, or bar charts to compare the average performance ratings across different gender or positions.

HR analytics, also referred to as people analytics, workforce analytics, or talent analytics, involves gathering together, analyzing, and reporting HR data. It is the collection and application of talent data to improve critical talent and business outcomes. It enables your organization to measure the impact of a range of HR metrics on overall business performance and make decisions based on data. They are primarily responsible for interpreting and analyzing vast datasets.

Download the data CSV files here ; https://drive.google.com/drive/folders/18mQalCEyZypeV8TJeP3SME_R6qsCS2Og

This dataset titled Human Resources.csv contains anonymized employee data collected for internal HR analysis and research purposes. It includes fields such as employee ID, department, gender, age, job role, and employment status. The data can be used for workforce trend analysis, HR benchmarking, diversity studies, and training models in human resource analytics.The file is provided in CSV format (3.05 MB) and adheres to general data privacy standards, with no personally identifiable information (PII).Last updated: April 11, 2025. Uploaded by Anurag Pardiash.

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

➡️ You can select raw or clean and AI-enriched datasets;

➡️ Multiple APIs designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing our APIs.

Coresignal's employee data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including HR and sales technology and investment.

Employee Data use cases:

✅ Source best-fit talent for your recruitment needs

Coresignal's Employee Data can help source the best-fit talent for your recruitment needs by providing the most up-to-date information on qualified candidates globally.

✅ Fuel your lead generation pipeline

Enhance lead generation with 712M+ up-to-date employee records from the largest professional network. Our Employee Data can help you develop a qualified list of potential clients and enrich your own database.

✅ Analyze talent for investment opportunities

Employee Data can help you generate actionable signals and identify new investment opportunities earlier than competitors or perform deeper analysis of companies you're interested in.

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

Abstract

The objectives of the survey were to provide information regarding the following: a. Household use of, and expenditure patterns for, social services; b. Reasons for low levels of household investment in education and health services for children; c. The distribution of the benefits of public spending for social services and how to improve targeting; d. Households' evaluation of the social services available to them; e. The potential for demand-side interventions to increase human capital investment directly (especially for girls and the poor); and f. The feasibility of repeated national monitoring surveys to assess the impact of future Bank and government projects in the social sectors, and to increase Tanzania's capacity to perform household survey work.

Geographic coverage

National coverage

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 5,184 households

The HRDS is national in scope and uses all the 222 clusters of the National Master Sample (NMS) maintained by the Bureau of Statistics as its sampling frame.4 Two NMS clusters were not surveyed because of weather conditions. For example, Nyamburi village in the Mara region was inaccessible. Heavy rains had washed away a bridge 8 kms (14 miles) from the village. All household surveys conducted by the Bureau of Statistics (e.g. Agricultural Sample Survey since 1986/87, Labor Force Survey in 1990/91) have used the framework of the NMS. This permits obtaining estimates at the national level and by area: rural, Dar es Salaam (DSM), and other urban towns. The current NMS covers 222 clusters: 100 rural villages representing the rural areas, and 122 Enumeration Areas (EAs) representing the urban areas. Fifty-two EAs are from the capital city, itself, 40 EAs are from the nine municipalities (Arusha, Dodoma, Moshi, Tanga, Morogoro, Iringa, Mbeya, Tabora, and Mwanza), and 10 EAs are from the remaining regional headquarters.

Selection of households and non-response.

Household selection was done in the field. In each cluster the team supervisor would first obtain the list of ten-cell leaders from the local authorities, and then, from each ten cell-leader, the list of households belonging to his/her cell. Each household was assigned a unique number, and then, using a table of random numbers, randomly selected. In each cluster, a list of about 30 households was then obtained, the last households in the list being alternates. With the collaboration of local authorities, the field workers were able to have an almost 100 percent reponse rate, except for the cases in which no member of the household was present for intervieing, and returning to the household was not feasible. Refusals to cooperate were rare. In those cases--absent households or refusals--, new households were drawn from the list of alternates.

The survey covered a total of 4,953 households in the 20 regions of Mainland Tanzania: 2,135 rural and 2,818 urban (see Table 1). In a second stage, the survey was extended to Zanzibar, where 230 households, in 24 clusters, were interviewed.

Region / Rural / Urban / Total Dodoma / 100 / 80 / 180 Arusha / 118 / 121 / 239 Kilimanjaro / 124 / 154 / 278 Tanga / 132 / 167 / 299 Morogoro / 88 / 120 / 208 Coast / 79 / 88 / 167 Dar es Salaam / 0 / 1127 / 1127 Lindi / 84 / 50 / 134 Mtwara / 114 / 44 / 158 Ruvuma / 69 / 49 / 118 Iringa / 124 / 128 / 252 Mbeya / 174 / 153 / 327 Singida / 82 / 41 / 123 Tabora / 99 / 72 / 171 Rukwa / 59 / 56 / 115 Kigoma / 83 / 35 / 118 Shinyanga / 153 / 54 / 207 Kagera / 193 / 24 / 217 Mwanza / 163 / 192 / 355 Mara / 97 / 63 / 160 Mainland Tanzania / 2135 / 2818 / 4953 Zanzibar / 127 / 104 / 231

Mode of data collection

Face-to-face [f2f]

Research instrument

Development of Survey Instrument.

The first draft of the household survey was developed in English in July, 1993. Training of enumerators, based on this draft, began on August 2, 1993. The month of August was devoted to training the enumerators and pre-testing the questionnaire. The first pre-test of the questionnaire took place in mid-August. The household questionnaire was almost completely precoded to eliminate coding errors and time delays. A category labeled "other: specify" was added to several questions. For those questions for which answers were not mutually exclusive, we precoded them with letters, rather than numbers, to allow for unambiguously coding of multiple answers. To minimize nonsampling errors, the questionnaire was in a form that reduced to a minimum the number of decisions required of interviewers while in the field. In anticipation of pages becoming detached from the questionnaire, every page contained a space for the household number and the last digit of the cluster code. Despite the fact that questions were written exactly as they were supposed to be asked by the interviewer, interviewers were granted some flexibility to give the interview greater semblance to a conversation, rather than an inquisition.

Pre-Test of Questionnaire.

The "pre-pre-test" of the questionnaire (August 16, 1993) was done only to discern whether the questions were understood, how long the administration of the survey required, whether all responses had been anticipated, which sections needed to be stressed during the training, etc. In this pre-pre-test, each questionnaire required an average of 4 hours to complete, far longer than the planned 1.5 hour maximum. The survey was consequently shortened and streamlined.

The true pre-test was conducted in two different types of clusters: Ubungo ward in DSM (urban) and Kibaha in the Coast Region (rural) over a period of two days. We chose these clusters because they are representative of two distinct groups, so a broader spectrum of answers and problems with the instrument could be anticipated. In the pre-test each questionnaire required an average of 2.5 hours. After a couple weeks of interviewing, the enumerators became more familiar with the instrument, resulting in their spending an average of 1.5 to 2 hours per questionnaire.

During the pre-test, each supervisor was asked to comment on each interview. The supervisor was asked to pay special attention to questions that seemed to make the respondent uncomfortable, that the respondent had difficulty understanding, or that the respondent seemed to dislike. The supervisor also evaluated which sections seemed to go slowly, had the most difficult questions, or provided insufficient opportunity for a complete response.

Revision of questionnaire.

Given the results of the two pre-tests, several areas for improvement in the questionnaire were identified. Perhaps most importantly, the willingness-to-pay amounts were adjusted. The sample distributions of the maximum willingness-to-pay questions were analyzed, and, based on that analysis, we decided to change some of the values. For example, in the child spacing question, the "pay Tsh 1,000" responses unexpectedly accounted for a large share of the bids. Thus, we provided the option of paying more by introducing "pay Tsh 50,000" and "pay Tsh 25,000" as answer choices. For the other contigent valuation sections--health and education--the first pre-test determined that there was also a large lumping of responses at the high end of the scale. We adjusted the ranges accordingly, although there remains some lumping at the high end in the final data.

We also changed the order of the sections. Based on the pre-test and judgment of the field workers, we decided to first ask the questions in the individual section, then the contigent valuation questions, then the household questions. Because the respondents enjoyed the contigent valuation questions so much, this decision helped increase interest in the questionnaire and re-energized the respondent before proceeding with the household questions--the last part of the questionnaire. The final survey instrument, incorporating all of the changes dictated by the pre-tests and other expert advice, was completed on September 12, 1993.

Translation.

Translation of the survey instrument was a joint effort of the enumerators and supervisors. Given the specific characteristics of the Kswahili language, this was a much better approach than asking one translator to translate from English to Kswahili, and another one to translate from Kswahili to English. The "group" translation, involving those who would ask the questions, was intended to avoid different interpretations of the same question and achieve uniformity. In this way the enumerators were able to better convey the message/objective of each question.

The majority of the interviews were conducted in swahili. In very few cases, because no one in the selected household could speak swahili, the need arose to use interpreters.

Our initial plan called for the field work to start no later than August 29. However, unforeseen circumstances, including both financial and logistical problems, delayed the first field trip. Both the money and the materials were available by September 6, and five of the six teams left for Tanga region on that day. Initially we had planned to have the sixth team based full-time in Dar es Salaam; however, tighter time constraints imposed by the above and subsequent delays eventually made it necessary to send the sixth team into the field as well, as detailed below.

Description of questionnaires

The main objective of the survey was to obtain data on the use of, and spending on, the social sectors. The primary emphasis was on education and health--the areas in which the major gaps in availability of data were identified. The survey was divided into five major components, each of which was further subdivided, as described below:

I. Individual Questionnaire A. Household Roster; B. Information on

Snapshot img

Human Resource (HR) Technology Market Size 2025-2029

The human resource (HR) technology market size is forecast to increase by USD 18.31 billion, at a CAGR of 8.4% between 2024 and 2029.

Major Market Trends & Insights

North America dominated the market and accounted for a 42% growth during the forecast period.
By the Application - Payroll processing segment was valued at USD 5.47 billion in 2023
By the End-user - Large enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 112.28 billion
Market Future Opportunities: USD 18.31 billion 
CAGR : 8.4%
North America: Largest market in 2023

Market Summary

The market is witnessing significant advancements, with an increasing number of businesses adopting technology-driven solutions to streamline their HR processes. According to recent studies, the global HR technology market is projected to reach a value of USD36.5 billion by 2026, growing at a steady pace. One of the key trends shaping this market is the integration of Artificial Intelligence (AI) in HR solutions. This integration enables automation of repetitive tasks, enhances recruitment processes, and improves employee engagement. However, data privacy and security concerns remain a major challenge, with 63% of organizations citing this as a concern.
Despite this, the benefits of HR technology are compelling, with 74% of businesses reporting increased productivity and 68% experiencing reduced costs. As businesses continue to prioritize digital transformation, the HR technology market is poised for continued growth and innovation.

What will be the Size of the Human Resource (HR) Technology Market during the forecast period?

Explore market size, adoption trends, and growth potential for human resource (hr) technology market Request Free Sample

The human resource technology market continues to expand without fail, with current adoption rates reaching over 40% of businesses worldwide. This figure represents a significant increase from previous years, underscoring the growing importance of digital solutions in HR operations. Looking ahead, future growth is projected to exceed 15%, driven by the continued demand for advanced tools that streamline processes and enhance employee experiences. A comparison of key numerical data reveals the substantial impact of HR technology on various aspects of HR management.
For instance, employee well-being initiatives have seen a 30% increase in engagement through digital platforms, while candidate relationship management tools have reduced time-to-hire by up to 50%. Furthermore, performance review cycles have been shortened by 25%, enabling more frequent and effective feedback. These improvements collectively contribute to increased productivity and efficiency within organizations.

How is this Human Resource (HR) Technology Industry segmented?

The human resource (HR) technology industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Payroll processing
  Applicant management
  Learning and development
  Talent management
  Others


End-user

  Large enterprises
  Small and medium enterprises (SMEs)
  Government organizations
  Non-profit organizations


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The payroll processing segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with payroll processing being a key application area. According to recent reports, payroll processing solutions adoption currently stands at 30%, reflecting their increasing importance in managing employee compensation efficiently. Looking ahead, industry experts anticipate a 25% increase in the adoption of HR technology, including payroll processing tools, over the next five years. Automated HR processes, such as payroll systems, are a significant driver of this market expansion. These systems automate salary calculations, tax deductions, benefits management, and the generation of pay slips. By streamlining these tasks, HR departments can reduce administrative burden and minimize errors.

In fact, automated payroll systems are currently used by 55% of businesses, a figure that is expected to rise to 70% within the next few years. Another key trend in the HR technology market is the integration of HR analytics dashboards, which enable data-driven HR decisions. These tools provide insights into employee performance, turnover rates, and other critical HR metrics. Additionally, HR

This dataset provides a detailed SQL-based employee database, which is ideal for practicing SQL queries and performing database-related operations. The dataset is structured to simulate a real-world organizational database, featuring various tables related to employee information, job roles, departments, and more.

The dataset is sourced from the GitHub repository https://github.com/cmoeser5/Employee-Database-SQL. It is intended for educational purposes, particularly for learning and practicing SQL.

Tables Included - employees: Contains records of employees with fields such as employee ID, name, job title, and department. - departments: Lists departments within the organization with fields including department ID and department name. - jobs: Includes details about job roles with fields such as job ID, job title, and job description. - salaries: Provides salary information for employees, including employee ID, salary amount, and salary date. - titles: Contains historical job title data for employees, including employee ID, job title, and title date.

Abstract The paper examined the extent to which Human Resource Management (HRM) contributes to the performance of small Brazilian hospitals from an efficiency perspective. The literature review addressed efficiency to assess hospital performance by measuring the contribution of Human Resources (HR) in health and industrial organizations. The methodological pathway used Data Envelopment Analysis (DEA) on a sample of 702 hospitals from secondary data from census surveys with 2777 small hospitals. The central hypothesis of the study, that GRH contributes to the efficiency of hospitals, was confirmed by the results considering that this was the variable with the greatest need for improvement in the efficiency of small hospitals. Other variables were also relevant in the context of hospital efficiency, such as the influence of peculiarities regarding size (number of beds), legal nature of institutions and regional distribution.

HR director email list is a database of contact information for human resources directors and other senior HR professionals. This list is a valuable tool for businesses that offer products or services to the HR sector. It includes email addresses, numbers, names, job titles, and company information. This type of data is a powerful resource for businesses that want to connect with decision-makers in human resources. Your message goes straight to the right person. This is the person who makes decisions about HR products. It does not get lost. You can create highly targeted campaigns for people who are most likely to need what you offer. For example, if you sell recruiting software, you can directly contact HR Directors who handle talent acquisition.

HR director email list saves time. You don’t have to search for contact info. This lets your sales team focus on building relationships and closing deals. In short, this email lead helps you connect with top HR professionals. It is accurate and up-to-date. We carefully collect and update our database so that you always get the most current information. It makes your marketing and sales efforts more efficient and effective. Of Course, you need to know about our website List to Data. You can buy this lead from this site. HR director email database is a list that contains the phone numbers of HR. If you want to talk to Executives and grow your business, our library is just what you need. This list has phone numbers and email addresses for individuals, helping you reach a valuable audience. Whether you’re launching a new product, offering high-end services, or looking for executives, our list helps you find the right people.

HR director email database can create marketing messages that fit the interests of executive personnel. This makes your efforts more successful and helps you build trust. Our list is also great for finding partners or investors. Stop using broad marketing methods that don’t work. With our list, you can make important connections and grow your business.

Data for analyzing the mediating role of organizational trust and work satisfaction in the relationship between HRM organizational practices and employees’ organizational citizenship behavior, as well as the moderating role of organizational justice. The study includes 339 employees working in healthcare centers in the Metropolitan Region of Chile (sample average age is 40 years; 64.6% are women; 56.2% of the employees had 6 or more years of professional experience). This study provides data from the Perceived HRM practices scale (Den Hartog et al., 2013), Organizational justice (Niehoff & Moorman, 1993), Organizational trust (Cook & Wall, 1980), Job satisfaction (Hackman & Oldham, 1975), and OCB (Kehoe & Wright, 2013) scales.

Data Collection

Because titles vary widely across local governments our researchers call 22,000 counties, cities, and towns every 180 days to learn who is currently in the role and what their title is. It's common for officials to be responsible for multiple roles especially in smaller local governments. Because the data is phone verified MarketEdge's contact data achieves 97% accuracy.

Overview

The Head of Human Resources makes decisions around the recruitment and hiring of employees along with their subsequent training and development.

Responsibilities To qualify as the Head of Human Resources the person must perform one or more of the following primary responsibilities: - Recruit and help to hire new employees - Arrange for the training and development of employees - Develop and administer the local government’s employment policies - Administer employee recognition programs - Ensure safe working conditions for the local government’s employees

Reporting Structure and Occurrence - In many governments (especially smaller ones), the HR function will be performed by a single person. This person may or may not have other responsibilities in addition to the responsibilities listed above. NOTE: This person could “sit” within groups or departments that don’t seem like HR. For example, the Head of HR could be: - Within the finance function, or even the same person who is the head of finance - Within the clerk function, or even the same person who is the head clerk - Within the office of the top appointed official, or even the same person who is the top appointed official

Titles You Might Expect - Human Resources Director - Human Resources Manager - HR Director - Director of Human Resources - HR Manager - Human Resources Officer - Personnel Director - Human Resources Coordinator - Human Resource Director - Human Resources Administrator

Surprising Titles - City Clerk - City Administrator - City Manager - Town Clerk - City Clerk/Treasurer - Town Manager - Mayor - Town Clerk/Treasurer - Town Administrator - City Secretary

Dataset created as part of the Master Thesis "Business Intelligence – Automation of Data Marts modeling and its data processing".

Lucerne University of Applied Sciences and Arts

Master of Science in Applied Information and Data Science (MScIDS)

Autumn Semester 2022

Change log Version 1.1:

The following SQL scripts were added:

    Index
    Type
    Name


    1
    View
    pg.dictionary_table


    2
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    pg.dictionary_column


    3
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    pg.dictionary_relation


    4
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    pg.accesslayer_table


    5
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    pg.accesslayer_column


    6
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    pg.accesslayer_relation


    7
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    pg.accesslayer_fact_candidate


    8
    Stored Procedure
    pg.get_fact_candidate


    9
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    pg.get_dimension_candidate


    10
    Stored Procedure
    pg.get_columns

Scripts are based on Microsoft SQL Server Version 2017 and compatible with a data warehouse built with Datavault Builder. Data warehouse objects scripts of the sample data warehouse are restricted and cannot be shared.

Snapshot img

HR Software Market Size 2025-2029

The HR software market size is forecast to increase by USD 17.36 billion, at a CAGR of 12% between 2024 and 2029.

The market is experiencing significant growth, fueled by increased budgets for HR technology solutions and the rising adoption of digital HR systems. Companies are recognizing the value of HR software in streamlining processes, improving efficiency, and enhancing the employee experience. However, this market is not without challenges. Organizational development and strategic workforce planning leverage big data analytics to identify trends and make informed decisions.
To capitalize on market opportunities and navigate challenges effectively, companies must prioritize robust data security measures and transparent data handling practices. Additionally, staying informed about the latest HR technology trends and innovations will be crucial for staying competitive and meeting evolving business needs. Data privacy and security concerns are becoming increasingly prominent, as organizations grapple with the risks associated with storing and managing sensitive employee information. These concerns are heightened as HR software becomes more integrated with other business systems and processes.

What will be the Size of the HR Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, with dynamic market activities shaping the industry landscape. Seamlessly integrated solutions now encompass various HR functions, including interview scheduling, HR service delivery, background checks, data privacy, HR analytics, project management, change management, learning management system, absence management, human capital management, integration capabilities, HR business partnering, and global payroll. User experience plays a pivotal role in the market, as organizations prioritize intuitive interfaces and streamlined processes for talent development, employee surveys, leave management, document management, API integrations, and interview scheduling are all integral components of this ever-evolving market.

The market is characterized by continuous innovation, as entities strive to meet the evolving needs of businesses across various sectors. The integration of these HR functions creates a comprehensive HR solution that enables organizations to effectively manage their workforce and optimize their human capital.

How is this HR Software Industry segmented?

The hr software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Core HR
  Talent management
  Employee collaboration and engagement
  Recruiting
  Workforce planning and analytics


End-user

  Large enterprises
  SMEs


Sector

  IT and tech
  Healthcare
  Manufacturing
  Retail
  Others


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    Germany
    The Netherlands
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The core HR segment is estimated to witness significant growth during the forecast period. The human resources (HR) software market is experiencing significant evolution, with a focus on enhancing workforce management capabilities. Compensation management and benchmarking are becoming more sophisticated, allowing for user-friendly experiences and real-time analytics. Talent development is a key priority, with employee surveys and onboarding workflows streamlined to improve engagement and retention. Leave management, document management, and compliance reporting are being integrated with HR systems, ensuring seamless data flow and regulatory adherence. API integrations enable HR solutions to connect with other business applications, improving efficiency and data accuracy. Reference checking, policy management, and workflow automation are essential components of HR information systems, ensuring consistent processes and reducing manual tasks.

Recruitment marketing, applicant tracking systems, interview scheduling, and hr service delivery are essential components of the HR technology landscape, helping organizations attract, engage, and hire top talent. Background checks, data privacy, and hr analytics are also critical, ensuring compliance and informed decision-making. Project management, change management, and learning management systems are increasingly integrated with HR solutions, improving workforce development and organizational effectiveness. Absence management, human capital management, and integrat

Question Paper Solutions of chapter HR Metrics of Human Resource Analytics, 6th Semester , Bachelor in Business Administration 2020 - 2021

Snapshot img

Human Resource Outsourcing (HRO) Market Size 2025-2029

The human resource outsourcing (HRO) market size is valued to increase USD 14.1 billion, at a CAGR of 5.3% from 2024 to 2029. Digitization of human resource outsourcing will drive the human resource outsourcing (HRO) market.

Major Market Trends & Insights

North America dominated the market and accounted for a 44% growth during the forecast period.
By End-user - Large enterprises segment was valued at USD 32.70 billion in 2023
By Service - PO segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 59.84 billion
Market Future Opportunities: USD 14.10 billion
CAGR : 5.3%
North America: Largest market in 2023

Market Summary

The market represents a significant and continually evolving sector, driven by the digitization of HR processes and the increasing adoption of recruitment analytics. Core technologies, such as artificial intelligence and machine learning, are transforming HRO services, enabling more efficient talent acquisition and management. The market's growth is further fueled by the growing dependence on outsourcing agencies to manage HR functions, freeing up in-house resources for strategic initiatives. According to recent studies, the global market share for HRO services is projected to reach 55% by 2025, underscoring the market's robust growth trajectory.
Despite these opportunities, challenges persist, including data security concerns, regulatory complexities, and the need for customized solutions. As businesses navigate these challenges, the HRO market continues to evolve, offering innovative solutions and services to meet the evolving needs of organizations worldwide.

What will be the Size of the Human Resource Outsourcing (HRO) Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Human Resource Outsourcing (HRO) Market Segmented and what are the key trends of market segmentation?

The human resource outsourcing (HRO) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Large enterprises
  SMEs


Service

  PO
  BAO
  MPHRO
  RPO
  LSO


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The large enterprises segment is estimated to witness significant growth during the forecast period.

Human resource outsourcing (HRO) has gained significant traction among businesses, particularly large enterprises with over 5,000 employees. These organizations collaborate with industry leaders from various sizes and geographical locations in the Americas, Europe, and Asia, fostering innovation and expanding market reach. The adoption of HRO solutions has seen a substantial increase, with workplace technology integration driving a 21% uptick in HR service delivery efficiency. Talent management systems have experienced a similar surge, improving strategic workforce management by 18%. HR technology solutions have also seen a 25% rise in implementation, enhancing HR business processes and enabling seamless outsourcing strategies.

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The Large enterprises segment was valued at USD 32.70 billion in 2019 and showed a gradual increase during the forecast period.

Moreover, compliance regulations have become increasingly complex, leading to a 27% increase in outsourcing adoption for benefits administration and payroll processing. Recruitment technology and HR shared services have also witnessed significant growth, with a 30% and 29% rise, respectively. Looking ahead, the future of HRO holds promising prospects. Total rewards and employee experience are expected to see a 24% and 22% increase in outsourcing, respectively, as companies focus on enhancing employee engagement and retention. Risk management, performance management, and learning and development are also anticipated to grow by 26%, 28%, and 29%, respectively. These trends underscore the evolving nature of the HRO market and its applications across various sectors, providing businesses with valuable insights to make informed decisions.

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Regional Analysis

North America is estimated to contribute 44% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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North America's the market demonstrates consistent expansion in the mid-market and tra

Abstract

The survey was one of three components of a World Bank project implemented to provide information on the size and composition of the civil service, improve systems and control mechanisms, institutional capacity, and provide information on policy-formulation and decision-making processes. Other components included a census of Guatemalan civil servants and contractors, and the continuous updating and use of this information to strengthen checks and improve transparency, and a new policy framework aimed at strengthening the institutional capacity of the Guatemalan civil service.

The aim of the survey was to assess the characteristics and quality of human resource management in the public administration, as well as to capture the attitudes, motivations, and experiences of public officials. In particular, the survey focused on the priority areas for reform identified by the Government of Guatemala and the World Bank. The data collected was used to support the World Bank’s diagnostic of key problem areas in the human resource management of the public administration in Guatemala. It was used to inform the design of institution-level interventions, as well as the new public policy framework.

Geographic coverage

The target population were civil servants across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Analysis unit

Public servants (managers and non-managers) across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Kind of data

Aggregate data [agg]

Sampling procedure

The sample frame used comes from the frame used for the Human Resources National Census. It has the list of positions in all the units of the 18 institutions selected for this study. The sample size for the managerial level was calculated with a 95% confidence level and a 5% margin error for each institution. For the non-managers, it was calculated with the same confidence level and margin error. The sample sizes are adjusted so the sample would have an even number for each study domain for the experiment which will assign a different questionnaire to half of the respondents.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire comprises following modules: 1- Pre-interview questions, 2- Demographic and work history information, 3- Management practices, 4- Performance evaluation, 5- perceptions about discrimination, 6- Human resources management practices, 7- Perceptions of the national office of the civil service, 8- Perception of acts of corruption, and 9- Review of surveys.

The questionnaire was prepared in English and Spanish.

Response rate

Response rate was 96%.

The dataset links data from two sources of information: (1) individuals with executive titles in a sample of small to medium- sized enterprises in Alberta were surveyed in 2016 and 2018 about their organization's human resource management practices, and (2) archival organizational-level injury data from Alberta Workers' Compensation Board from 2014 to 2019. Thus, the purpose of this dataset is to connect human resource management practices with injury data at the organizational level in a sample of small- to medium- sized enterprises in Alberta over time. The function of this dataset is to provide greater understanding of potential organizational-level predictors of occupational safety. The variables from the survey were removed prior to posting publicly; contact the researcher for more information.