This article aims to make the concept of Exploratory Data Analysis more accessible to HR professionals who might be new to data analytics.

As the workplace continues to evolve, the role of Human Resources is also transforming. This is where People Analytics comes in – a data-driven strategy for understanding and managing your workforce. But where should you begin? Let's explore Exploratory Data Analysis (EDA). In this article, we use EDA as a tool to help HR professionals extract meaningful insights from employee data. To see

What is Exploratory Data Analysis?

EDA is the process of examining and visualizing your dataset to get a comprehensive view of the data and understand its main characteristics, often before formal modeling or hypothesis testing. EDA is a great tool to learn if one wants to make decisions on evidence rather than assumptions or gut feelings.

Framework for EDA

Let's walk through the general steps that can be used to perform Exploratory Data Analysis on any dataset.

1. Know Your Data

• Understand the structure and content of your dataset
• Identify data types (numerical, categorical)
• Determine the scope of your data

Understanding your data types is crucial as it determines which analysis techniques you can use.

2. Visualize Your Data (a lot)

• Create various charts to represent different aspects of your data
• Choose appropriate visualization types based on data characteristics

Examples:

• Histograms for numerical data like age and salary
• Box plots to spot any outliers
• Correlation plots to see relationships between different factors
• Bar charts for categorical data like gender and department

Each chart type reveals different aspects of your data. For example, a salary histogram shows distinct salary bands, outlining the structure of the compensation system.

3. Look for Patterns and Outliers

A. Analyze Single Variables

• Examine distributions and trends within individual data points. Some examples listed below:
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  • Employee Age Distribution: Understand workforce demographics and inform strategies for retention and development programs
  • Hiring Patterns: Uncover seasonal or cyclical trends in hiring, which could help with future recruitment planning

B. Investigate Relationships Between Variables

• Use correlation analysis to highlight connections between different factors, such as between
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  • Job levels, salaries, and bonuses
  • Tenure and performance ratings
  • Education level and promotion rates

C. Identify and Analyze Outliers

• Spot outliers - data points that deviate significantly from the norm
• Determine if the outlier is result of a data error or genuine anomaly
• Consider potential causes of the outlier and its HR strategy implications

4. Generate Questions and Hypotheses

• Use your findings to formulate questions for further investigation
• Develop hypotheses about your workforce based on the data
• Common questions to consider:‍
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  • For compensation: Is there a clear hierarchy? How does it compare to industry standards?
  • For diversity: Are there imbalances in representation across departments or levels?
  • For performance: How do different factors correlate with high performance?

5. Plan Next Steps

• Outline potential actions based on your initial findings
• Identify areas that require deeper analysis

6. Iterate and Refine

• Revisit your analysis with new questions or hypotheses
• Continuously update your EDA as new data becomes available

The Power of EDA for HR

By using EDA, we transform raw data into actionable insights. This approach can help HR professionals:

• Make data-driven decisions about workforce planning
• Identify potential issues before they become problems
• Support arguments for new initiatives with solid data

Getting Started with EDA

You don't need to be a data scientist to begin using EDA in your HR practice. Start small:

• Identify a dataset you want to explore (e.g., employee satisfaction survey results)
• Use simple tools like Excel or Google Sheets to create basic charts
• Look for patterns or anything unexpected in your visualizations
• Ask yourself what these findings could mean for your organization

As you get more comfortable, you can explore more advanced tools and techniques. Remember, the goal of EDA is not to find definitive answers, but to ask better questions and guide further investigation. It's a powerful first step in leveraging data to create a more effective, equitable, and satisfied workforce.

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