How HR analytics helps Human Resource Management

 What is HR analytics?

 HR analytics is defined as the process of measuring the impact of HR metrics, such as time to hire and retention rate, on business performance. This is a methodology for creating insights into how investments in human capital assets contribute to the success of four principal outcomes:

(a) generating revenue,

(b) minimizing expenses,

(c) mitigating risks,

(d) executing strategic plans.

 HR is a people-oriented function and is perceived by most people. But for those who think that the HR team’s contributions are limited to extending offer letters and onboarding new hires, human resource analytics (HR analytics) can prove them wrong. When used strategically, analytics can transform how HR operates, giving the team insights and allowing it to actively and meaningfully contribute to the organization’s bottom line.

 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.  (yennhi, 2023)

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, the investment needed to increase productivity, employee likelihood to leave, and the 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.

HR Analytics, People Analytics, and Workforce Analytics: What is the Difference?

HR analytics, people analytics, and workforce analytics are often used interchangeably. But there are slight differences between each of these terms. It would help you to know the difference to assess the most relevant data to their function.

The difference between HR Analytics, People Analytics, and Workforce Analytics

HR analytics:

 HR analytics deals explicitly with the metrics of the HR function, such as time to hire, training expense per employee, and time until promotion. All these metrics are managed exclusively by HR for HR. 

People analytics:

 Although often used as a synonym for HR analytics, “people analytics” technically applies to “people” in general. It can encompass any group of individuals, even outside the organization. For instance, the term “people analytics” may be applied to analytics about an organization’s customers and not necessarily only employees. 

Workforce analytics: 

Workforce analytics is an all-encompassing term referring specifically to employees of an organization. It includes on-site employees, remote employees, gig workers, freelancers, consultants, and others working in various capacities in an organization. In the HR context, some workforce and HR analytics metrics may overlap, so the two terms are often used as synonyms. The goal of the two may also be the same. For instance, data on employee productivity and performance inform both HR and workforce analytics, and the goal is to improve retention rates and enhance the employee experience.

What Metrics Does HR Analytics Measure?

Several HR metrics contribute to business value. Based on the key performance indicators (KPI) of the organization, HR can then propose the metrics that can influence these KPIs.

Here are some common metrics tracked by HR analytics

1. Revenue per employee: Obtained by dividing a company’s revenue by the total number of employees in the company. This indicates the average revenue each employee generates. It is a measure of how efficient an organization is at enabling revenue generation through employees.

2. Training expenses per employee: Obtained by dividing the total training expense by the total number of employees who received training. The value of this expense can be determined by measuring the training efficiency. Poor efficiency may lead you to re-evaluate the training expense per employee.

3. Voluntary turnover rate: Voluntary turnover occurs when employees voluntarily choose to leave their jobs. It is calculated by dividing the number of employees who left voluntarily by the total number of employees in the organization. This metric can lead to the identification of gaps in the employee experience that are leading to voluntary attrition.

4. Absenteeism: Absenteeism is a productivity metric, which is measured by dividing the number of days missed by the total number of scheduled workdays. Absenteeism can offer insights into overall employee health and can also serve as an indicator of employee happiness.

5. Human capital risk: This may include employee-related risks, such as the absence of a specific skill to fill a new type of job, the lack of qualified employees to fill leadership positions, the potential of an employee to leave the job based on several factors, such as relationship with managers, compensation, and absence of a clear succession plan. HR analytics can be used to measure all these metrics.

The complete HR analytics cycle

I. Create a collective mindset

Before the operational and mathematical aspects can kick in, HR leaders must prepare their teams and organizations for a workflow-driven by analytics.

II. Bring in data scientists

“Data scientists will play an invaluable role in creating a culture of analytics across HR. As the role of HR business partners and generalists evolves to include skills such as data strategy, analysis, and communication (articulating ‘the story behind the science’), the data scientist will serve as the coach, mentoring their colleagues across HR in how to understand, and apply, the insights.”

III. Start small

IV. Get clearance from the legal team

The sort of data collection that HR analytics uses is governed heavily by compliance laws. Some legal considerations to keep in mind when implementing an HR analytics solution are:

o   Employee privacy and anonymity

o   Consent from employees about the amount and type of data being collected

   Establishing the goal of data collection and informing employees accordingly

o   IT security when using third-party software to run HR analytics

o   Location of the HR analytics vendor – with whom the data will be stored – and their compliance with local laws

Collaborate with the legal team of your organization to ensure ethics and compliance norms are followed. 

V. Choose an HR analytics solution

Any HR analytics solution that will be used at scale must have certain components.   (Lalwani, 2023)

Types of data analysis.

01. Descriptive analytics - What happened?

Tables and bar charts the easiest and quickest way to look into your data is by using (frequency) tables and bar charts. This only goes for nominal and sometimes ordinal data

Histograms A more advanced way of looking at your ratio or interval variables can be achieved by making a histogram. A histogram visualizes the distribution of data over a continuous interval and can be used to see how your data is deviated.

02. Diagnostic analytics - Why did this happen?

03. Predictive analytics - What is likely to happen?

04. Prescriptive analytics - What should be done?

(Wolvius, 2020),(Stevens, 2023)

What are common data sources for HR analytics?

Common data sources include internal data like demographic employee data, payroll data, social network data, performance data, and engagement data. External data sources can include labor market data, population data, LinkedIn data, and much more. Any data that’s relevant to the specific project can be used. (Singh, 2023)

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.

·        A 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.

Cons:

·       Lack of statistical and analytical skills in HR departments can hinder the 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.

How HR Data Analytics Improves Decision-Making

To ensure you effectively use HR data analytics to get the most value from it, it is essential to consider the many ways analytics can help you manage your workforce. You don’t necessarily need a team of analytics professionals to get started. You can immediately begin using HR analytics to identify solutions to your most pressing workforce management challenges.

Here are some examples of the ways you can use HR data analytics to improve decision-making

Streamline recruitment and onboarding processes.

Given the need to compete for talent, your organization can use HR data analytics to discover the best methods for sourcing, evaluating, and selecting hires. Even after hiring, you'll want to make sure you are onboarding employees properly. For example, you can use applicant tracking data to understand:

• Which sources yield the best candidates.
• How long it takes to hire a candidate.
• What each hire costs to attract, select, and onboard.

  Examine benefits for cost-effectiveness.

HR data analytics helps you determine if the benefits you offer are effectively meeting employee needs. For example, you can generate reports that tell you which benefits are most in demand among employees. You can also use data analytics to calculate your total benefits expenses and determine if there are areas in which you can cut costs but continue to offer competitive and comprehensive benefits. (Management, 2023)

Conclusion

HR analytics is a powerful tool that can help organizations improve their HR processes and outcomes. By collecting and analyzing data related to the people in an organization, HR analytics can help make data-driven decisions, measure the impact of HR initiatives, and shape the future of work. These are just some of the key reasons why HR analytics is important. There are many examples of successful HR analytics implementations that have delivered business value in various areas such as hiring, attrition, training, capacity, performance, and anomaly detection. (team, 2023)

References

Lalwani, P., 2023. What Is HR Analytics? Definition, Importance,. [Online]
Available at: https://www.spiceworks.com/hr/hr-analytics/articles/what-is-hr-analytics/
[Accessed 28 Oct 2023].

Management, F. W., 2023. What Is HR Analytics and How Can Data Improve Decision-Making?. [Online]
Available at: https://www.fuseworkforce.com/blog/what-is-hr-analytics-how-data-improve-decision-making
[Accessed 08 Nov 2023].

Singh, D., 2023. Role of Analytics & Data in Human Resource Management. [Online]
Available at: https://www.linkedin.com/pulse/role-analytics-data-human-resource-management-deependra-singh/
[Accessed 08 Nov 2023].

Stevens, E., 2023. The 4 Types of Data Analysis. [Online]
Available at: https://careerfoundry.com/en/blog/data-analytics/different-types-of-data-analysis/
[Accessed 3 Nov 2023].

team, M., 2023. Why HR Analytics Is Important. [Online]
Available at: https://www.manatal.com/blog/why-hr-analytics-is-important

Wolvius, C., 2020. Predict the future, understand the past: the four types of data analysis. [Online]
Available at: https://datajourney.akvo.org/blog/the-four-types-of-data-analysis?
[Accessed 5 11 2023].

yennhi, 2023. HR Data Analytics: Significance, Metrics, and Pros & Cons. [Online]
[Accessed Oct 25, 2023].