What Happened and Why: The basis of HR Analytics!

Your company's data indicates a significant drop in performance on Thursdays every week during the first quarter of the year. After investigation, it was found that Thursday is the last working day of the week, and employees feel the fatigue of a full week, which affects their concentration and productivity. This led you to anticipate that they would deliver the same low performance on Thursdays in the next quarter. This prompted you to assign lighter, easily accomplishable tasks on Thursdays until a strategy for re-dividing and distributing tasks for the organization is finalized for the coming year.

Discovering the low performance resulted from descriptive analysis, and understanding the reasons came from diagnostic analysis. These two are the subject of this article and form the basis for more complex and advanced forms of analysis, which led you to predict the recurrence of past events (predictive analysis) and take corrective then preventive measures (prescriptive analysis). We will dedicate a separate article to each of these in the future, so stay tuned.

The true value in collecting HR data lies in the ability to decipher this data and transform it into deep insights that support strategic decision-making. This article focuses on the two fundamental types of HR analytics: Descriptive Analytics, which answers the question "What happened?", and Diagnostic Analytics, which answers the question "Why did this happen?". This article will delve into their methodologies and practical examples of how they are used to identify the root causes of observed trends and patterns in the workforce, enabling organizations to gain a deeper understanding of their human past and present.

Descriptive Analytics: What Happened?

Descriptive analytics is the most basic, or perhaps "primitive," level of data analysis. This type of analytics aims to answer the question "What happened?" or "What is happening?", by presenting historical data in simple reports and dashboards. These analyses are used to understand the current reality and build a foundation for examining organizational performance.

Practical Examples in HR:

• Monthly Employee Absenteeism Rate: Descriptive analytics can show the total number of absence days or the average absence days per employee during a specific month.

• Number of Employees Joined or Departed: These analyses show the net change in the number of employees over a defined period, providing an overview of workforce growth or contraction.

• Completed Training Rates: The percentage of employees who have completed a training program or course can be measured.

• Employee Turnover Rate: Employee turnover rates can be analyzed, comparing annual turnover between different teams or departments.

• Revenue Per Employee (RPE): This metric illustrates the average revenue contributed by each employee.

Advantages and Disadvantages:

• Advantages: Descriptive analytics is the simplest form of data analysis and requires only basic mathematical skills. It also excels in ease of understanding and visual presentation of data, making complex data easily digestible for stakeholders.

• Disadvantages: These analyses are limited to a simple examination of a few variables after the event has occurred. They cannot explain "why" or "how" the observed patterns happened. Therefore, they cannot be relied upon indefinitely on their own.

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Diagnostic Analytics: Why Did This Happen?

Diagnostic analytics is an advanced step beyond descriptive analytics. These analyses seek to answer the question "Why did this happen?", by linking variables and analyzing the underlying causes behind observed trends and patterns. They delve deeper into the data to identify contributing factors to these patterns.

Techniques Used:

Diagnostic analytics involves techniques such as:

• Data Drilling: To obtain a more detailed view of the data.

• Data Mining: To extract hidden patterns.

• Correlation Analysis: To test relationships between variables.

Statistical Analysis: To collect and interpret data and identify underlying patterns.

Practical Examples in HR:

• Identifying Reasons for High Absenteeism: If descriptive analytics reveals a rise in absenteeism, diagnostic analytics can help determine the root causes. This might involve looking into unplanned absences on specific days, after long periods between holidays, or when time-off requests were denied. Employee feedback surveys and exit interviews can also provide relevant information.

• Analyzing the Relationship Between Leadership Styles and Performance Outcomes: Diagnostic analytics can reveal whether certain leadership styles lead to higher or lower levels of performance or job satisfaction.

• Identifying Early Indicators of Resignation or Discontent: By analyzing data such as stress levels, decreased engagement, or changes in productivity, diagnostic analytics can identify factors contributing to employee turnover. For example, it can reveal a gap between employee expectations and the company's potential to meet those expectations.

• Improving Employee Engagement and Company Culture: By analyzing data from internal surveys and exit interviews, diagnostic analytics can uncover what makes employees feel connected and satisfied, and what does not.

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Advantages and Disadvantages:

• Advantages: Diagnostic analytics provides a more comprehensive interpretation of data, which helps in understanding the bigger picture and identifying factors that could lead to problems.

• Disadvantages: These analyses remain reactive, focusing on events that have already occurred. They cannot directly provide actionable insights for future planning; however, they build towards this goal, as understanding the cause often leads to solutions.

The transition from merely knowing "what happened" to understanding "why it happened" represents a crucial strategic step. While knowing that employee turnover is high (descriptive analysis) might be concerning, it doesn't offer solutions by itself. The real value lies in the ability to analyze the underlying reasons for this turnover (diagnostic analysis), such as poor development opportunities, dissatisfaction with compensation, or an unhealthy work environment. This deeper understanding enables HR departments to design sustainable and targeted corrective interventions to address problems at their roots, rather than merely superficial reactions or temporary fixes. This enhances HR's role as a strategic partner capable of tackling challenges from their origins, leading to long-term improvements in workforce performance.

Conclusion

Descriptive and diagnostic analytics are the foundation and the first step in the HR analytics journey. Through them, organizations can gain a clear understanding of their human past and present, and accurately identify the underlying causes of observed trends and patterns. This deep understanding is the basis upon which more advanced levels of analytics (predictive and prescriptive) are built, enabling HR to make more informed and effective decisions, and design strategies aimed at building a more productive and satisfied workforce. The ability to decipher the past and present is the first step towards shaping a better future for HR in any organization.