HR Data Analytics: Significance, Metrics, and Pros & Cons
Discover:
- What is HR Analytics?
- Why is HR Analytics needed?
- Examples in HR Analytics
- How does HR Analytics work?
- Examples of HR analytics Metrics
- Pros and Cons of HR Analytics
- Predictive HR Analytics
What is HR Analytics?
HR analytics, also known as talent analytics, people analytics, or workforce analytics, is the process of collecting and analyzing human resource (HR) data to improve an organization’s workforce performance. This method of data analysis takes data collected by HR and correlates it to organizational objectives to provide measured evidence of how HR initiatives are contributing to achieving those goals.
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Why is HR Analytics needed?
Raw data on its own cannot provide useful insights, so HR analytics organizes, compares, and analyzes data to reveal patterns and answer important questions about employee turnover, hiring speed, investment needed to increase productivity, employee likelihood to leave, and impact of learning and development initiatives. With data-backed evidence, organizations can focus on making necessary improvements and plan future initiatives, resulting in improved workforce performance.
Examples in HR Analytics
HR analytics can be used to address common organizational issues such as turnover and recruitment.
- In turnover, HR analytics can help identify trends and patterns, understand employee behavior and engagement, and create predictive models to flag potential turnover risks.
- In recruitment, HR analytics can help gather data from multiple sources, gain insight into candidate attributes, and provide metrics on hiring timelines and historical hiring data.
How does HR Analytics work?
HR analytics comprises several components that feed into each other to provide problem-solving insights. The four stages of HR analytics are collecting data, measurement, analysis, and application. In this article, we will discuss how HR analytics works.
1. Collecting data
The first vital component of HR analytics is collecting and tracking high-quality data. This data can come from HR systems, learning & development systems, cloud-based systems, mobile devices, or wearable technology. The data collected includes employee profiles, performance, salary and promotion history, onboarding, training, engagement, retention, turnover, and absenteeism.
2. Measurement
The second stage is the process of continuous measurement and comparison, also known as HR metrics. HR analytics compares collected data against historical norms and organizational standards. The key metrics monitored are organizational performance, operations, and process optimization.
3. Analysis
The analytical stage reviews the results from metric reporting to identify trends and patterns that may have an organizational impact. Descriptive analytics, prescriptive analytics, and predictive analytics are used to analyze the data.
4. Application
Once metrics are analyzed, the findings are used as actionable insight for organizational decision-making. This involves applying the analysis gained from HR analytics to decision-making.
Here are some examples of how to apply HR analytics insights:
- Time to hire — If findings reveal that the time to hire is too long and the job application itself is causing the delay, organizations can make an informed decision about how to improve the job application process for better accessibility and effectiveness.
- Turnover — Understanding why employees leave the organization can help prevent or reduce turnover from happening. For instance, if a lack of training support was identified as a contributing factor, initiatives to improve ongoing training can be implemented.
- Absenteeism — Understanding the reasons for employee long-term absence can help organizations develop strategies to improve the work environment factors that affect employee engagement.
Pros and Cons of HR Analytics
HR analytics is gaining value in HR practices. It provides insight into employee behavior, recruitment, and retention through data analysis. But, there are pros and cons to consider before implementing it.
Pros:
- Data-driven decision-making can lead to more accurate results and reduce the need for intuition.
- Deeper understanding of employee behavior can lead to strategies to improve retention and engagement.
- Recruitment and hiring can be tailored to the organization’s needs by analyzing current and potential employee data.
- Predictive analytics can help forecast trends and patterns in HR data, allowing for proactive measures.
Cons:
- Lack of statistical and analytical skills in HR departments can hinder analysis of large datasets.
- Different management and reporting systems can make it difficult to compare data.
- Access to quality data and analytical software may be limited.
Monitoring and collecting more data can create ethical issues.
Overall, while HR analytics has its challenges, it offers valuable benefits to organizations looking to improve their HR practices.
Predictive HR analytics
Predictive HR analytics is a way to use historical data to make predictions about the future, including the success of HR initiatives and employee behaviors.
- Predictive analytics can help organizations identify which candidates are best suited for the job, and which employees are at risk of leaving within a year. Statistical techniques are used to create models that can identify trends and forecast future behaviors.
- Predictive HR analytics can be used to tackle employee turnover, poor performance, and boost employee engagement. But it requires a significant investment in technology and skillset, which many organizations do not have.
- Moreover, the unpredictability of human behavior and the ethical questions that arise when slotting people into an algorithm is a significant challenge.
Conclusion
Human Resource (HR) Analytics is a crucial tool that enables HR professionals to make data-driven decisions. It involves gathering, analyzing, and interpreting data related to employee behavior, performance, and engagement to improve organizational performance. By leveraging HR Analytics, organizations can make better-informed decisions that align with their business goals, resulting in better outcomes for employees and the company.
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