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Human Capital Management Solutions Market Size 2024-2028

The human capital management solutions market size is forecast to increase by USD 13.92 billion at a CAGR of 9.52% between 2023 and 2028.

The Human Capital Management (HCM) Solutions market is experiencing significant growth, driven by the increasing demand for automated recruitment processes. This trend is fueled by the need for efficiency and cost savings In the hiring process. Another key factor driving market growth is the emergence of cloud-computing services, which enable organizations to access HCM solutions on-demand and reduce the need for costly on-premises infrastructure. However, the market also faces challenges, such as the threat from open-source software, which can offer similar functionality at lower costs. To remain competitive, HCM solution providers must focus on offering differentiated features and services that justify the investment in proprietary solutions. The market is expected to continue growing, as organizations seek to optimize their human resources functions and improve overall workforce management.

What will be the Size of the Human Capital Management Solutions Market During the Forecast Period?

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The human capital management (HCM) solutions market encompasses a range of product offerings designed to enhance business productivity by streamlining HR processes. This market is characterized by continuous evolution, with updates to HR Information Systems (HRIS) software and Human Resource Management Systems (HRMS) providing advanced functionalities. Businesses increasingly prioritize data security In their selection of HCM solutions, ensuring the protection of sensitive employee information. The HCM market comprises both established and emerging companies, offering diverse product lines that extend beyond traditional HR functions. These solutions now include strategic sourcing, talent management, and staff experiences. Deployment models vary, with both cloud-based and on-premises options available.
Manual labor-intensive HR tasks are being replaced with automated solutions, enabling organizations to focus on core business activities. The market's growth is driven by the need for efficient HR processes, increased competition for talent, and the desire to create positive staff experiences.

How is this Human Capital Management Solutions Industry segmented and which is the largest segment?

The human capital management solutions industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Core HR
  Talent
  Workforce


Component

  Solution
  Service


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    India
    Japan


  South America



  Middle East and Africa

By Application Insights

The core hr segment is estimated to witness significant growth during the forecast period.

Core Human Capital Management (HCM) solutions encompass essential HR functions such as recruitment, training, payroll, scheduling, benefits administration, internal relations, and safety and compliance. Digitalization and the need for cost reduction and efficient workforce management are driving the growth of HCM solutions for core HR applications. Oracle Corp., SAP SE, and Workday Inc. Are established companies offering HR functional software solutions, including human resources information systems (HRIS) and human resource management systems (HRMS), which manage employee data for future reference. Advanced technologies like cloud computing, APIs, artificial intelligence, and machine learning are increasingly integrated into HCM solutions to enhance decision-making capabilities, improve employee engagement, and streamline HR processes.HCM software solutions enable businesses to work smarter, increase organizational productivity, and optimize HR procedures while ensuring data security and confidentiality. Cloud-based deployment, scalability, and mobile applications further extend the reach and accessibility of these solutions.

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The Core HR segment was valued at USD 11.23 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% 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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The Human Capital Management (HCM) solutions market in North America is projected to lead with a significant market share due to factors such as increasing job opp

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The large-scale forcing data is derived based on the constrained variational analysis approach (Zhang and Lin, 1997; Zhang et al. 2001), which calculates the large-scale vertical velocity and advective tendencies from sounding measurements of winds, temperature, and water vapor mixing ratio over a network of a small number of stations.

View More Major updates in the VARANAL v2 include: 1) the method used to develop multi-year continuous forcing data, 2) the incorporation of Eddy Correlation Flux Measurement System (ECOR) turbulent fluxes into the analysis, and 3) improvements to the workflow to increase efficiency.

Currently, two major VARANAL data sets are archived by ARM: 1) Radiosonde- or numerical weather prediction (NWP)-based forcing data for short-term Intensive Operational Periods (IOPs) at different ARM fixed or mobile sites. Most of the IOP forcing is at 3-hr and 25-mb resolutions. 2) Multi-year continuous forcing data at the ARM observatories. The continuous forcing is at 1-hr and 25-mb resolutions.

This VAP has been used to drive Single-Column Models (SCMs), Cloud-Resolving Models (CRMs) and Large-Eddy Simulation Models (LESs) for different cloud and convective systems. Results from these model simulations are then used to improve cloud parameterizations in Global Climate Models (GCMs). It can also be applied to evaluate model results, as it includes diagnostic fields such as diabatic heating profiles, cloud fields, surface measurements, and large-scale conditions. Additionally, VARANAL is one of the critical datasets required for the ongoing routine LES ARM Symbiotic Simulation and Observation (LASSO).

The derivations of the VARANAL from field measurements are subject to uncertainties that can directly impact the simulated cloud and radiation fields by SCM/CRM/LES. These uncertainties originate from two sources: 1) the instrument and measurement errors, and 2) the scale aliasing or sampling biases errors. Both error types depend on scales because horizontal derivatives are involved in the calculation of the horizontal fluxes. Ensemble forcing data by perturbing potential uncertainties in the constraints can help address this type of uncertainty in the forcing data. Please refer to the technical report for more information.

The large-scale forcing data is derived based on the constrained variational analysis approach (Zhang and Lin, 1997; Zhang et al. 2001), which calculates the large-scale vertical velocity and advective tendencies from sounding measurements of winds, temperature, and water vapor mixing ratio over a network of a small number of stations.

View More Major updates in the VARANAL v2 include: 1) the method used to develop multi-year continuous forcing data, 2) the incorporation of Eddy Correlation Flux Measurement System (ECOR) turbulent fluxes into the analysis, and 3) improvements to the workflow to increase efficiency.

Currently, two major VARANAL data sets are archived by ARM: 1) Radiosonde- or numerical weather prediction (NWP)-based forcing data for short-term Intensive Operational Periods (IOPs) at different ARM fixed or mobile sites. Most of the IOP forcing is at 3-hr and 25-mb resolutions. 2) Multi-year continuous forcing data at the ARM observatories. The continuous forcing is at 1-hr and 25-mb resolutions.

This VAP has been used to drive Single-Column Models (SCMs), Cloud-Resolving Models (CRMs) and Large-Eddy Simulation Models (LESs) for different cloud and convective systems. Results from these model simulations are then used to improve cloud parameterizations in Global Climate Models (GCMs). It can also be applied to evaluate model results, as it includes diagnostic fields such as diabatic heating profiles, cloud fields, surface measurements, and large-scale conditions. Additionally, VARANAL is one of the critical datasets required for the ongoing routine LES ARM Symbiotic Simulation and Observation (LASSO).

The derivations of the VARANAL from field measurements are subject to uncertainties that can directly impact the simulated cloud and radiation fields by SCM/CRM/LES. These uncertainties originate from two sources: 1) the instrument and measurement errors, and 2) the scale aliasing or sampling biases errors. Both error types depend on scales because horizontal derivatives are involved in the calculation of the horizontal fluxes. Ensemble forcing data by perturbing potential uncertainties in the constraints can help address this type of uncertainty in the forcing data. Please refer to the technical report for more information.

Data Overview WageScape's US job listings dataset offers real-time, forward-looking insights into the American labor market. Covering millions of job postings data from various industries and locations, it supports workforce analytics, economic forecasting, and strategic planning, enabling businesses to make data-driven decisions with salary data, skill taxonomy data, and comprehensive company data.

Data Enrichment The dataset includes enriched recruiting data with industry codes, title normalization, company normalization, geographic parsing, firmographic info, and compensation data, enhancing usability and accuracy with precise skill taxonomy data.

Main Attributes • Job Titles: Across various sectors and industries, supported by detailed job postings data. • Job Descriptions: Detailed roles and requirements based on recruiting data. • Salary Data: Expected pay information, sourced from comprehensive salary data. • Locations: From state-level to city-specific details, offering insights into company data. • Company Information: Name, size, revenue, and industry sectors, providing context to company data. • Posting Dates: Timeline of market activity reflecting trends in job postings data. • Job Requirements: Skills, education, and experience needed, informed by skill taxonomy data.

Coverage • Industries: Technology, healthcare, finance, manufacturing, retail, and more, analyzed through company data. • Geographical Reach: National coverage, including metropolitan areas, regional hubs, and smaller towns, enriched with job postings data.

Scale and Quality • Data Volume: Over 4 million job postings monthly, providing extensive recruiting data. • Hiring Organizations: Data from 6+ million organizations, supported by reliable company data. • High Precision: Rigorous validation for accuracy, ensuring dependable salary data.

Use Cases • Workforce Analytics: Analyze trends and dynamics using job postings data and recruiting data for HR decisions. • Economic Forecasting: Predict economic and labor market shifts based on company data and job postings data. • Talent Acquisition: Improve recruitment strategies with detailed recruiting data and insights from skill taxonomy data. • Market Research: Understand industry trends using enriched company data. • Strategic Planning: Inform long-term business strategies with comprehensive job postings data and salary data.

Data Accessibility • Delivery Channels: Available through Data-as-a-Service (DaaS), with access to recruiting data and job postings data. • Customizable Reports: Tailored to specific business needs, incorporating skill taxonomy data. • Integration: Seamless integration into existing systems, supporting detailed company data analysis.

Key Benefits • Real-Time Insights: Up-to-date job information for timely decisions based on job postings data. • Forward-Looking Data: Predict future labor market trends with enriched company data and salary data. • Comprehensive Coverage: Extensive industry and geographic data, including detailed recruiting data. • High Quality and Scale: Millions of postings monthly for robust analysis, supported by skill taxonomy data. • Actionable Insights: Enhance job modeling and workforce strategies with high-quality company data.

Key Points WageScape's dataset is essential for businesses to understand the labor market deeply. With extensive coverage and high-quality company data, it empowers organizations to optimize workforce strategies and maintain a competitive edge, leveraging the latest job postings data, salary data, and skill taxonomy data.

Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: An intensive, longitudinal dataset

This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (SD=3.8, MIN=1.0, MAX=13.4) from a population of 21 students of which 12 have also completed an exit survey, and 7 started before the California COVID-19 lockdown order. This multimodal dataset included different means of data collection such as Samsung Galaxy Watch, Oura Ring, a Life-logger app named Personicle, a questionnaire mobile app named Personicle Questions, and periodical and personalised surveys. The dataset contains raw data from Photoplethysmogram (PPG), Inertial measurement unit (IMU), and pressure sensors in addition to processed data on heart rate, heart rate variability, sleep (bedtime, sleep stages, quality), and...

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IoT Analytics Market Size and Trends

The IoT analytics market size is forecast to increase by USD 153.66 billion, at a CAGR of 46.4% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for data-driven insights in various industries. One of the key drivers is the need to enhance business efficiency, particularly in sectors such as healthcare with the use of heart rate tracking and other vital sign monitoring. However, challenges persist, including the sluggish connectivity for Internet of Things devices, which can hinder the seamless flow of data within the IoT ecosystem. The connected car market is also expected to contribute significantly to the growth of IoT analytics, as real-time data analysis becomes increasingly important for optimizing vehicle performance and enhancing the user experience. On-premise IoT analytics solutions continue to be popular due to concerns around data security and privacy. Overall, the market is poised for continued expansion as businesses seek to unlock the value of the vast amounts of data generated by IoT devices.

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The market has revolutionized various industries by enabling the generation and collection of vast amounts of data. This data, in turn, is fueling the automation and optimization of business processes across sectors such as manufacturing, healthcare, agriculture, supply chain management, energy-intensive industries, and smart homes. IoT technology is transforming industries by providing real-time data from various sources, including autonomous vehicles, global positioning systems, and wearable devices. This data is essential for process optimization and improving operational efficiency. For instance, in manufacturing, real-time data from machines can help identify potential issues before they escalate, reducing downtime and increasing productivity. In healthcare, IoT analytics is enabling a patient-centric model by providing personalized recommendations based on real-time data from wearable devices, such as heart rate tracking. This data can help healthcare professionals monitor patients' health status and provide timely interventions, leading to better patient outcomes. Similarly, in agriculture, IoT analytics is helping farmers optimize irrigation, monitor crop health, and manage livestock, leading to increased productivity and reduced costs.

In energy-intensive industries, real-time data from sensors can help identify energy wastage and optimize energy usage, leading to cost savings and reduced carbon footprint. IoT analytics is also transforming supply chain management by providing real-time visibility into inventory levels, shipping status, and delivery schedules. This data can help businesses optimize their supply chain operations, reduce lead times, and improve customer satisfaction. Furthermore, IoT analytics is enabling the development of smart homes, where data from various sensors can be used to optimize energy usage, improve safety, and provide personalized recommendations to residents. In conclusion, IoT analytics is transforming various industries by providing real-time data from various sources, enabling process optimization, and improving operational efficiency. The use of descriptive analytics in IoT data can help businesses gain insights into their operations, identify trends, and make data-driven decisions. By leveraging IoT analytics, businesses can gain a competitive edge and improve their bottom line.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Component

  Software
  Services


End-user

  Manufacturing
  Retail
  Healthcare
  Telecommunication and IT
  Others


Geography

  North America

    Canada
    US


  APAC

    China
    India
    Japan


  Europe

    Germany
    UK
    France
    Spain


  Middle East and Africa



  South America

By Component Insights

The software segment is estimated to witness significant growth during the forecast period. In the market, software solutions held the largest share in 2023, according to the global market size. This dominance is driven by the expanding reach of IT and retail companies, leading to an increased need for managing vast amounts of data. SMEs in emerging economies, such as China, India, Brazil, Indonesia, and Mexico, are fueling the demand for IoT analytics software. These businesses require insights to drive growth and remain competitive.

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The software segment was valued at USD 6.94 billion in 2018. Companies cater to various industries, offering software solutions for real-time data collection and analysis. Ensuring data security is crucial

Estimation of Hazard ratio (HR) estimation and their MSE (Binary risk factor) {Censoring variable = ci = U(0,1)*1.5}.

The characteristics of the dataset prior to pre-processing.

BackgroundAlthough clear cell sarcoma of kidney (CCSK) is rare, it is the second most common renal tumor in children after Wilms’ tumor. NWTS and SIOP are two major groups which had made tremendous efforts on renal tumors, but the strategies are different, for NWTS follows the upfront surgery principle providing definite pathology and the SIOP follows the upfront chemotherapy principle, each has its own advantages. Here we aimed to evaluate the outcomes of CCSK in China following NWTS strategies to analyze the prognostic factors.MethodsFor this multicenter retrospective study, a total of 54 patients were enrolled from three children’s hospitals, between April 2003 and December 2021. Treatment comprised upfront radical nephrectomy, followed by radiotherapy and intensive chemotherapy. Clinical records were regularly updated. Prognostic factors and survival rates were evaluated.ResultsThe 54 enrolled patients had a median age of 37 months (range, 4 months to 11.4 years). The stage distribution was 16% stage I (n = 9), 30% stage II (n = 16), 39% stage III (n = 21), and 15% stage IV (n = 8). Among stage IV, metastasis sites included the lung (n = 6), bone (n = 1), and intra-orbital/cervical lymph node (n = 1). After a median follow-up of 5.6 years, the 5-year event-free survival (EFS) was 82.4±5.4%, and overall survival was 88.1±4.6%. The EFS was 100% for stage I, 93.8 ±6.1% for stage II, 71.1±10.0% for stage III, and 68.6±18.6% for stage IV. Univariate analysis revealed that staging (III/IV), tumor rupture, and inferior vena cava tumor thrombus were inferior prognostic factors. Multivariate analysis revealed that tumor rupture was independent poor prognostic factor (P = 0.01, HR 5.9). Among relapsed patients, relapse occurred a median of 11 months after diagnosis (range, 4–41 months), and 50% (4/8) achieved a second complete remission after multiple treatment. None of the six lung metastasis patients received lung RT, only one patient developed a relapse and was salvaged by RT after relapse.ConclusionsTumor rupture was independent poor prognostic factor. Upfront surgery of NWTS strategies can make a definite pathology diagnosis, but how to reduce tumor rupture during surgery is important especially in developing countries. The outcomes of patients with stage I–III CCSK in China were comparable to findings in other developed countries. Better outcomes were achieved in stage IV CCSK by using an intensive chemotherapy regimen including carboplatin, which require further confirmation by AREN0321. Lung RT may be safely omitted in selected patients who achieve a compete radiographic response after 6 weeks of systemic treatment (including surgery). Treatment should be encouraged even in CCSK cases with metastasis and relapse.