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What is HR Analytics

In today’s marketplace, many people ask, “What is HR analytics?” While digital tools generate a lot of data, HR analytics turns this raw information into valuable information that can help address business and workforce challenges.

In this article, we will discuss:

  • the meaning of HR analytics
  • types of HR analytics
  • benefits of HR analytics for organizations
  • how to implement HR data analytics in your company

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What is HR analytics?

HR analytics, or people or workforce analytics, uses data to help companies make informed decisions and improve performance. Over the last century, human resource management has evolved from operational to strategic, with HR analytics helping this transformation. HR professionals can better evaluate their policies and strategies by relying on data rather than instincts.

Methods for analyzing HR data

There are different ways HR data can help your decision making and planning processes. Here’s a look at the main types of HR analytics:

Descriptive

Descriptive HR analytics looks at past data to understand what happened over time—for instance, checking previous years’ attendance records to see patterns in employee absences.

Diagnostic

Diagnostic HR analytics explores data to figure out why something occurred—for example, studying employee feedback to determine why there is high turnover in a particular department.

Predictive

Predictive HR analytics uses current and past data to forecast future outcomes—for instance, analyzing past recruitment trends to estimate which candidates might succeed in upcoming roles.

Prescriptive

Prescriptive HR analytics provides recommendations based on possible future scenarios, such as developing strategies to address potential skill gaps based on projected company growth and current employee skill sets.

How HR analytics can benefit your organization

HR analytics allows you to draw conclusions, gain insights and forecast your workforce. Here’s how it can enhance various HR functions:

  • Assessing recruitment efforts: reviewing candidate and process data to improve hiring strategies
  • Evaluating talent management: measuring effectiveness with indicators like employee engagement and absenteeism rates
  • Identifying turnover patterns: analyzing data to understand trends in both voluntary and involuntary employee departures
  • Determining training needs: using skills inventory data to identify areas requiring additional training
  • Optimizing compensation and benefits: analyzing market trends and internal data to refine compensation packages and keep them competitive
  • Predicting future workforce requirements: examining current workforce demographics, skills and retirement trends to plan for future needs

The importance of HR analytics

Data can transform HR from a routine function into an organization’s strategic partner. By understanding the impact of HR policies, your HR department can align its strategies with business goals and measure its contributions. This process can benefit employees and enhance business performance.

HR analytics can help HR teams:

  • find areas where improvements can boost productivity and cut costs
  • use data to guide choices that affect employees and the organization
  • measure how well HR policies and interventions are working
  • provide solid evidence to support HR programs and changes
  • assess and improve diversity, equity, inclusion and belonging initiatives
  • proactively manage challenges and uncertainties by using data-driven insights

What are HR metrics?

HR metrics can help you monitor employee performance and evaluate the effectiveness of HR initiatives. Here’s a look at some standard metrics and how to understand them:

Employee turnover

Tracking turnover can help identify which departments or positions have the most departures. You can calculate it by dividing the number of terminations by the number of employees at the start of the period and multiplying it by 100.

Absenteeism

Frequent absences can show dissatisfaction and highlight areas that may need attention. To calculate the number of absent days, take the number of missed days, divide it by the total working days and multiply by 100.

Revenue per employee

This metric gives each employee’s average revenue, showing overall efficiency. You can calculate it by dividing the total revenue by the total number of employees.

Employee net promoter score (eNPS)

This score predicts employee loyalty and satisfaction based on survey responses. Employees rate their willingness to recommend your organization from 0 to 10. You can find the eNPS by subtracting the percentage of detractors (those who rate 0-6) from the percentage of promoters (those who rate 9-10).

Cost per hire

The cost-per-hire metric measures the total expense of recruiting a new employee, including advertising, background checks, bonuses and administrative costs. You can calculate it by adding the internal and external costs and dividing it by the total number of hires.

How to get started with HR data analytics

Analyzing HR data can involve several stages. Understanding each step can help you use HR analytics effectively. Below is a breakdown:

Asking the right business question

When using HR analytics, focus on how HR can positively influence business results. Start by identifying your end goal. Determine the specific area you want to improve and what insights you need from the data, then frame it as a question. For example, if you’re looking to boost employee engagement, you might ask: What factors most influence job satisfaction among our team members?

Choosing the right data

The next step is to figure out what information you need to answer your question and where to find it. Most of the necessary data may be available in your HR systems or other internal sources, but you might sometimes need to include external benchmarking data. Organizing and sorting the data can be challenging without the right tools, so having a system that integrates with your reporting set-up is ideal. Here are some of the various data sources to choose from:

Cleaning up your data

Cleaning your data before using it for HR analytics can help promote accuracy and reliability in your analysis. If your data contains duplicates, errors or missing information, your conclusions can be misleading, resulting in poor decision making. Clean data can provide trustworthy insights to help you make knowledgeable decisions that positively impact your organization.

Analyzing your data

Review and analyze your data to uncover trends, relationships and patterns that help you reach conclusions. You can use tools like Excel, R, Python or ChatGPT to help with this analysis. Your insights can help answer your initial questions.

Turning data into action

You can use your discoveries to assess the effectiveness of your HR processes and policies and make decisions or recommendations for improvements.

Advancing from basic to advanced HR analytics

HR data analytics can greatly benefit an organization, especially when moving beyond basic analysis to more advanced techniques. Using sophisticated statistical methods, HR teams can predict workforce trends and influence future outcomes that can lead to measurable financial benefits. Changing from simple descriptive analytics to more advanced diagnostic, predictive and prescriptive analytics can help elevate your HR practices. Here are some ideas to help your organization strengthen its HR analytics capabilities:

Invest in essential tools

Allocate resources to the tools for collecting quality data and performing predictive analysis. Consider investing in an advanced HRIS (human resources information system), statistical tools such as R and Python, and data visualization platforms like Visier and Tableau.

Build analytical skills

Focus on training programs that can improve your HR team’s data literacy and statistical skills. Encourage and reward employees for pursuing external HR data analytics courses and certifications.

Start with pilot projects and refine

Begin with small-scale pilot projects to test predictive and prescriptive models. Collect feedback and make necessary adjustments based on the results. Once you refine your approach, you can expand these initiatives to benefit the organization.

Evaluate your data infrastructure

Ensure your data infrastructure can support predictive and prescriptive analytics. A system that integrates data sources, manages data cleaning, generates reports and supports data governance can be helpful.

Create a data-driven culture

Encourage a culture where data is key to achieving success. Provide employees with the skills to use data to their advantage in their roles. Make data accessible across departments and support experimentation to promote transparency and collaboration. Leaders can set the standard by showing the value of data-driven decisions.

HR analytics can turn raw data into actionable insights, improving decision making and organizational performance. By evolving from basic descriptive analytics to more advanced methods like predictive and prescriptive analytics, HR can more effectively address workforce challenges and align strategies with business goals. Investing in the right tools, building analytical skills and creating a data-driven culture can maximize the benefits of HR analytics and drive strong improvements within your organization.

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