Human resources (HR) analytics emerged as a field 20 years ago, but it has only recently gained traction and importance due to the dramatic shift to remote and hybrid work environments, digitization, and the proliferation of people data catalyzed by the pandemic. Whereas HR departments years ago had smaller and fewer datasets and could be characterized as “people-oriented,” people teams today have bigger and more data at their disposal, such as cloud-based HR, payroll, and time and attendance solutions (HRISes &andHRMs), applicant tracking systems (ATSes), performance management systems (PMSes), learning management systems (LMSes), feeds from collaboration tools, and more.
With an uptick in layoffs, reorganizations, and remote/hybrid teams—as well as the changing nature of work with the rise of AI—it’s critical that chief HR officers (CHROs) and HR teams augment their people-oriented approach with new data-driven ones to make the best people and operations decisions to help their organizations succeed.
In this post, we’ll dive into the benefits of HR analytics, the challenges of traditional approaches, and how AI and augmented analytics are revolutionizing every part of people management— planning, recruiting, onboarding, management, upskilling, and beyond.
In This Post
What is people analytics?
People analytics—also known as HR analytics, talent analytics, or workforce analytics—is a data-driven approach to attracting, retaining, and growing talent.
It brings visibility, insights, and accessible advanced analytics to the human capital management lifecycle.
Here are some of the key benefits of people analytics:
- Cost reduction/control. HR analytics can identify cost-effective recruitment channels (reducing the time-to-fill and lowering cost-per-hire), pinpoint employee turnover drivers (reducing the cost of recruitment and onboarding), align staffing levels with demand (reducing overtime costs and optimizing labor expenses), and design competitive compensation (controlling compensation expenses).
- Increased productivity. Data-driven insights can help HR teams identify performance gaps and training needs, as well as interventions and compensation approaches, to boost employee performance and productivity.
- Risk mitigation. People analytics can identify engagement drivers to reduce the risk of turnover, as well as ensure compliance with labor laws and regulations to avoid costly penalties.
- Policy effectiveness. HR analytics provides data-driven insights that enable HR professionals to assess the impact of existing policies, identify areas for improvement, and make informed decisions to optimize workforce management strategies.
- Faster, better talent acquisition. HR analytics can spot favorable attributes of successful candidates, as well as process bottlenecks, for faster time-to-hire and improved candidate quality.
- Onboarding effectiveness. Analytics can streamline the onboarding and orientation process by creating data-driven tailored onboarding plans and personalized training recommendations.
How does HR analytics work?
At a high level, people analytics turns HR data into business-impacting insights.
Unpacking this, here are the key components of HR analytics:
- Data: Structured and semi-structured data is gathered from HRISes / HRMs, ATSes, PMSes, LMSes, payroll, time and attendance systems, employee engagement and exit surveys, benchmarks, and third-party data regarding economic indicators, industry-specific trends, and the job market.
- Storage: Data is stored in a database or data lake in the cloud, on-premises, or both, with data pipelines refreshing everything as necessary.
- Analytics: Relationships are inferred or defined on these datasets, and then natural language search, visualizations and dashboards, automated insights, embedded analytics, alerts, machine learning modeling, and more enable descriptive (“what happened?”), diagnostic (“why did it happen?”), predictive (“what might happen?”), prescriptive (“what should we do next?”), and proactive (“what am I not thinking about?”) analytics.
- Decisions and outcomes: Insights drive data-driven decisions related to workforce analytics that impact the firm’s bottom line.
Challenges with current people analytics approaches
Current approaches to workforce analysis involve toggling between systems, reports, dashboards, and hacked-together spreadsheets—which poses several challenges:
- Incomplete views: Siloed data. Lack of interoperability between systems. Backwards-facing BI. HR teams face these and other challenges, leading to incomplete views of the business’ current and upcoming needs, driving inefficiencies and higher costs.
- Lack of efficient ad hoc analysis systems. Analyzing data in HR analytics tools is hard. Piecing together data from various systems in a spreadsheet is time-consuming and risky if the file isn’t secured or is out of date.
- Slow time to insights and root causes. HR issues are highly multidimensional (read: complicated people). Getting to the root cause of issues is time-consuming due to the number of variables—and groupings of variables—to consider. CHROs and executive leadership often need answers and insights in hours, not days.
- Inaccessible advanced analytics. Predictive analytics for use cases like workforce forecasting is daunting or out of reach for the typical HR analyst due to limited advanced analytics expertise and lack of access to data scientists.
AI-powered people analytics
AI-powered analytics platforms greatly expedite data analysis, insights generation, and advanced analytics through intelligent automation and intuitive UX. AI is ideal for HR analytics because it overcomes previously mentioned challenges and unlocks analysis for all users.
Here’s what leading AI analytics platforms offer:
- Easily bringing together a variety of data sources. Technical and non-technical users alike can bring together a variety of data sources—historical, operational, and external data—in a point-and-click manner to inform rich analysis.
- Lightweight data prep and semantic layer. Data is never clean, nor is it 100% straightforward how various datasets relate to each other. Modern analytics platforms allow AI-suggested joins and cleaning steps, no-code prep steps, and full-code flexibility to get the data into analysis-ready form.
- Rapid free-form analysis. Natural language search and automated best-fit visualization enable anyone—not just those who understand SQL—to explore vast datasets easily.
- Intuitive dashboarding, visualization, data stories, and PPT export. Rather than wait on BI developers to create custom dashboards, leading AI analytics solutions auto-suggest visualizations, which can be easily added to dashboards and data stories. These can then be customized in a point-and-click manner and then shared or exported in native PowerPoint presentations (with the underlying data) for executives to consume.
- Automated root cause and key driver analysis. Automated insights help organizations make better-informed decisions about a variety of HR areas, such as attrition, by identifying patterns of employee behavior, performance, and more to determine key factors influencing employee retention and satisfaction using key driver and trend analysis.
- Real-time visibility. Real-time anomaly detection and alerting allow HR teams to respond swiftly to talent issues that arise to minimize disruption to the business.
- Accessible advanced analytics. HR subject matter experts can easily leverage no-code solutions to apply domain knowledge for predictive and prescriptive analytics.
Use cases of AI analytics for human resources
Talent acquisition and planning
Talent planning. Accurate talent and workforce planning is critical to cost-effective people operations. Workforce planning is challenging with current tools due to the complexity of workforce data, the lack of integration with other business systems, the need for real-time insights, and the difficulty in identifying emerging talent needs and trends accurately.
AI-powered people analytics can be used to swiftly analyze vast datasets to provide HR teams with predictive, prescriptive, and proactive insights to identify talent gaps, workforce trends, skill development needs, and much more—for smarter talent sourcing and recruiting strategies.
Talent acquisition. Attracting and retaining top talent is vital for sustained business success and growth. However, high applicant volumes and inefficiencies in identifying the best candidates through traditional talent acquisition approaches lead to prolonged time-to-hire and talent shortages.
AI analytics can be used to review recruitment data to identify effective recruiting channels and unique characteristics of candidates who are more likely to succeed in the organization based on successful hires’ attributes, skills, and experience.
AI-based intelligent alerting can further identify bottlenecks in the recruitment process based on time-to-completion of each stage benchmarked against previous hires. The result is faster time-to-hire and improved candidate quality for a healthier talent pipeline.
Tellius Success Story: A multinational CPG firm used Tellius’ automated root cause analysis and proactive anomaly detection capabilities to identify recruitment bottlenecks, which helped streamline processes and reduce delays in the recruitment process.
The result was reducing cost-per-hire by 28% (a $1.7M savings) by automating decision-making processes and simplifying recruitment workflows; cutting onboarding time by 72% by identifying inefficiencies in recruitment stages; and performing talent analytics 10X faster than previous manual efforts.
Employee integration and development
Onboarding and orientation. Traditional approaches to onboarding new candidates are time-consuming, prone to errors, and challenging to personalize for individual employee needs, leading to longer ramp-up times and lower engagement.
AI-powered analytics streamlines onboarding and orientation by creating tailored, data-driven onboarding plans and personalized training recommendations. This not only reduces time-to-productivity—so candidates can add value quickly—but also enhances employee engagement and satisfaction, driving higher employee success rates.
Employee development and training. Nurturing talent and developing new employee skill sets is important to remaining competitive in today’s rapidly evolving work environment.
However, traditional training methods struggle to pinpoint individual learning needs, track progress effectively, and align training programs with evolving skill demands, leading to skill gaps and ineffective employee development programs (and thus, wasted money).
AI-powered analytics transforms employee development and training by analyzing employee performance data, identifying skill gaps, and recommending personalized training paths. This accelerates skill acquisition, improving employee performance to bolster an organization’s competitiveness.
Mentoring analytics. Mentoring helps foster knowledge transfer, skill development, and employee engagement. But traditional mentoring programs typically lack visibility into their effectiveness, making it challenging to assess the impact of mentorship on employee growth and satisfaction.
AI analytics can help HR teams track mentor-mentee interactions, measure skill development, and assess the outcomes of mentoring relationships over time to enhance mentor/mentee satisfaction, drive employee retention, accelerate career growth, foster a culture of learning and development, and ultimately benefit individuals and the organization as a whole.
Performance and rewards management
People and org analytics. HR teams lack the ability to self-serve answers and insights from vast amounts of data. Instead, they face endless dashboards, reports, and queues for analytics support.
AI-powered people analytics allows anyone—regardless of technical/analytical experience—to search their organization’s people data using natural language through an intuitive, Google-like search experience and dynamically generated visualizations and summaries.
This means that answers to ad hoc HR questions are a search away, analysis and insights can be easily shared in drag-and-drop dashboards, and powerful ML-based automated insights parse millions of variables to identify true root causes, key drivers, and cohort comparisons—doing the work that might have taken HR and analytics teams days performed in minutes.
Performance management. Effective HR performance management is critical to workforce productivity, aligning individual goals with strategic objectives, and driving continuous improvement.
The key challenge of traditional performance management tools is that HR teams lack real-time insights, hindering their ability to make informed decisions about employee development, compensation, and other areas.
AI-powered analytics can be used to leverage data to provide objective evaluations, track progress against goals, and provide managers with the ability to provide timely feedback to their employees.
Compensation and benefits administration. Compensation and benefits are important to attracting, retaining, and motivating top talent, but it’s challenging to align compensation and rewards with performance.
Historic compensation analysis is labor-intensive, and missing the mark in either direction can lead to talent retention issues and/or budget issues. AI analytics can be used to quickly analyze market trends, benchmark salaries, and correlate compensation with performance metrics for fairer and more competitive compensation packages to drive better employee retention, improve budget allocation, and strengthen an organization’s talent retention strategy.
Tellius Success Story: A 28,000-employee hospital system used Tellius to perform compensation standardization across their entire network, which involved modeling what-if scenarios and cost implications of changes to shift pay, differential paid time off, and more. The impact was a reduction in labor costs as a percentage of total expenses by 5%, equating to millions of dollars.
Employee engagement and relations
Employee listening / employee experience. Creating an engaging and productive work environment is a central function of HR. Yet traditional employee experience monitoring methods often rely on sporadic surveys or self-selecting populations (think: the loudest voices are heard).
A lack of real-time insights makes it challenging to address emerging issues quickly before they escalate into problems. AI analytics can aid HR teams in employee listening by harnessing real-time data from numerous sources, including surveys, feedback platforms, and communication channels.
This provides a holistic view of employee sentiment for faster responses to employee concerns, enhanced engagement, and ultimately, improved employee retention, morale, and productivity.
Tellius Success Story: A leading hospitality firm’s HR team leverages Tellius to gain real-time insights from diverse data sources, including daily hotel operations such as occupancy rates, guest feedback, and revenue per available room (RevPAR) combined with surveys and performance reviews to monitor workforce morale and engagement. Tellius’ analytics empowered them to identify trends, predict issues, and take timely interventions.
Employee relations and conflict resolution. Preserving a positive work environment is important for high-performing teams. However, traditional approaches to employee relations often rely on manual processes, lack visibility into ongoing issues, and typically do not proactively identify potential conflicts—which can lead to unresolved disputes and an increased employee turnover rate.
AI-powered HR analytics can use automated insights to identify patterns of conflict, assess root causes, and predict potential issues, enabling HR teams to reduce grievance caseloads and foster a more productive and engaging workplace.
Talent management and transition
Talent retention and succession planning. Talent retention and succession planning are essential for organizations to retain top performers, develop future leaders, and ensure a seamless transition of critical roles.
However, current planning processes often rely on subjective assessments, lack visibility into emerging talent gaps, and rarely adapt quickly to changing business needs, leading to talent and leadership shortages.
AI analytics can be used in talent retention and succession planning to perform data-driven identification of high-potential employees, track their development, and predict succession needs, which enhances talent retention, minimizes leadership gaps while accelerating leadership development, and offers a more agile approach to responding to organizational talent needs.
Tellius Success Story: A leading software development company harnessed the power of Tellius’ AI analytics solution to dramatically improve talent retention and succession planning. By leveraging data-driven insights, they identified and nurtured top talent with remarkable success, resulting in a 20% improvement in talent retention, reducing leadership gaps by 15%, positioning the firm as a forward-thinking organization ready to meet future talent needs effectively.
Separation and offboarding. Smooth exits protect company assets and employer brands. However, traditional separation and offboarding procedures can be time-consuming and manual, and they may overlook crucial steps, leading to potential security and reputational risks.
AI analytics can swiftly analyze employee data, identifying patterns that indicate potential risks or areas that require attention during offboarding. This includes flagging accounts with access to sensitive data that need to be deactivated promptly or ensuring that all necessary documentation, exit interviews, and legal compliance requirements are met to reduce the risk of legal disputes.
Read more: Learn why HR leaders are choosing Tellius for AI-powered HR and people analytics.
What does the future of people analytics look like?
The future of people analytics will be:
- Real-time and continuous feedback: Traditional annual performance reviews will evolve into continuous feedback loops. Real-time feedback—and resulting analysis and insights powered by HR analytics—will enable agile performance management and development.
- Proactive (vs. reactive): Moving away from a reactive approach, the future of people analytics will emphasize proactive strategies. By harnessing predictive analytics and real-time data insights, CHROs and other business leaders can anticipate issues and make informed decisions preemptively, reducing employee churn and responding to market opportunities faster.
- Contextual and human-oriented: Augmented analytics and generative AI will play pivotal roles in helping HR teams make more informed decisions that are not just data-driven but also sensitive to the unique needs and aspirations of employees.
The role of the HR team is evolving, and the challenges they face—including the new skills and ways of working as AI impacts an organization’s workforce—require innovative solutions. But by providing actionable insights, streamlining processes, and enhancing decision-making, augmented analytics empowers HR leaders to navigate the complexities of the modern workforce with agility and foresight, ultimately driving efficiency and success in the long term.