Interactive Audio Lesson
Listen to a student-teacher conversation explaining the topic in a relatable way.
Understanding Descriptive Analytics
Unlock Audio Lesson
Signup and Enroll to the course for listening the Audio Lesson
Let's start with descriptive analytics. This type of analytics helps us understand past events, like employee turnover. It tells us what has happened over a specified period.
Can you give us an example of how descriptive analytics is used in HR?
Certainly! For example, by analyzing last year's turnover rates, HR can pinpoint trends and determine why employees left.
So descriptive analytics is really about looking back?
Exactly! Remember, we can think of it as the βwhatβ of analytics. It lays the foundation for further insights.
To recap, descriptive analytics answers the question: 'What has happened?'
Exploring Predictive Analytics
Unlock Audio Lesson
Signup and Enroll to the course for listening the Audio Lesson
Now, let's move on to predictive analytics. This involves forecasting what may happen in the future.
So, does that mean it can tell us who is likely to leave the company?
Yes, that's right! For instance, by analyzing patterns in employee data, we can predict which employees are at risk of leaving.
What kind of data would be used for that?
Good question! We would look at factors like performance ratings, satisfaction surveys, and even attendance records.
To summarize, predictive analytics answers the question: 'What might happen in the future?'
Understanding Prescriptive Analytics
Unlock Audio Lesson
Signup and Enroll to the course for listening the Audio Lesson
Finally, we have prescriptive analytics. This form not only predicts but also suggests actions to achieve specific outcomes.
How does that work exactly?
Great question! For instance, if predictive analytics indicates a risk of high turnover, prescriptive analytics may recommend specific retention strategies, like enhanced training programs.
So itβs like a road map for HR decisions?
Exactly! Remember: prescriptive analytics answers the question: 'What should we do?'
In conclusion, HR analytics is vital in moving from intuition-based to evidence-based decision-making.
Introduction & Overview
Read a summary of the section's main ideas. Choose from Basic, Medium, or Detailed.
Quick Overview
Standard
This section explains the importance of HR analytics, describing its three main types: descriptive, predictive, and prescriptive analytics. It emphasizes how HR analytics transitions organizations from intuition-based to evidence-based decision-making.
Detailed
HR analytics is a key driver in modern human resource management, facilitating data-driven decision-making. This section elaborates on three key forms of analytics: Descriptive Analytics helps HR professionals understand past performance, Predictive Analytics forecasts future HR trends, and Prescriptive Analytics offers actionable advice for improving HR outcomes. By moving from intuition-based decisions to a focus on data, organizations can enhance hiring, retention, performance, and overall effectiveness.
Audio Book
Dive deep into the subject with an immersive audiobook experience.
Types of Analytics
Unlock Audio Book
Signup and Enroll to the course for listening the Audio Book
Type
Purpose
- Descriptive Analytics: Understand what has happened (e.g., turnover last year)
- Predictive Analytics: Forecast what may happen (e.g., who may leave next)
- Prescriptive Analytics: Suggest actions (e.g., how to reduce attrition)
Detailed Explanation
This chunk explains the three main types of HR analytics. Descriptive analytics is about understanding past events, like how many employees left the company last year. Predictive analytics looks into the future and gauges potential scenarios, such as predicting who might leave next based on patterns. Lastly, prescriptive analytics doesn't just provide insights; it suggests actions to improve situations, like strategies to reduce employee turnover. Understanding these types helps HR professionals adopt a more data-driven approach to their work.
Examples & Analogies
Imagine you are a coach for a sports team. Descriptive analytics is like looking at last season's stats to understand how your team performed. Predictive analytics is like using player performance data to predict who might be injured next season. Prescriptive analytics is akin to developing a training program based on these insights to enhance player fitness and reduce injuries.
Moving Towards Evidence-Based Decisions
Unlock Audio Book
Signup and Enroll to the course for listening the Audio Book
HR analytics helps move from intuition-based to evidence-based decisions.
Detailed Explanation
This chunk emphasizes the transition from making decisions based on gut feelings or intuition to making them based on solid evidence drawn from data analysis. Evidence-based decisions are more reliable, as they are backed by factual data rather than assumptions. In HR, this means that actions such as hiring, promotions, or strategy changes are made based on analyzed data rather than solely on personal judgment.
Examples & Analogies
Think of this transition like switching from guessing the best route to a destination based on previous experiences to using a GPS. The GPS provides real-time data and traffic patterns, leading you to the best route, much like HR analytics guides decisions with factual insights rather than simply relying on past experiences or intuition.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
-
Descriptive Analytics: Understanding past events in HR.
-
Predictive Analytics: Forecasting future events in HR.
-
Prescriptive Analytics: Suggesting actions based on predictions.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
-
Analyzing last year's employee turnover rates to understand patterns and causes.
-
Using employee performance data to predict retention risks.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
π΅ Rhymes Time
-
Descriptive tells the tale, of events gone by; Predictive looks ahead, to what may lie nigh. Prescriptive shows the path, to take and not to shy.
π Fascinating Stories
-
Once a wise HR manager used past data (descriptive) to see trends, predicted future turnover (predictive), and prescribed training programs (prescriptive) to improve employee retention.
π§ Other Memory Gems
-
Don't forget: 'D, P, and P' for Descriptive, Predictive, and Prescriptive - the three pillars of HR analytics.
π― Super Acronyms
Remember D = Past, P = Future, and P = Actions (DPP) for understanding analytics types.
Flash Cards
Review key concepts with flashcards.
Glossary of Terms
Review the Definitions for terms.
-
Term: HR Analytics
Definition:
The application of data analysis in HR to improve decision-making and outcomes.
-
Term: Descriptive Analytics
Definition:
Analyzing past data to understand trends and events.
-
Term: Predictive Analytics
Definition:
Forecasting future trends based on historical data.
-
Term: Prescriptive Analytics
Definition:
Offering recommendations on actions to improve outcomes.
Purpose
- Descriptive Analytics: Understand what has happened (e.g., turnover last year)
- Predictive Analytics: Forecast what may happen (e.g., who may leave next)
- Prescriptive Analytics: Suggest actions (e.g., how to reduce attrition)
- Detailed Explanation: This chunk explains the three main types of HR analytics. Descriptive analytics is about understanding past events, like how many employees left the company last year. Predictive analytics looks into the future and gauges potential scenarios, such as predicting who might leave next based on patterns. Lastly, prescriptive analytics doesn't just provide insights; it suggests actions to improve situations, like strategies to reduce employee turnover. Understanding these types helps HR professionals adopt a more data-driven approach to their work.
- Real-Life Example or Analogy: Imagine you are a coach for a sports team. Descriptive analytics is like looking at last season's stats to understand how your team performed. Predictive analytics is like using player performance data to predict who might be injured next season. Prescriptive analytics is akin to developing a training program based on these insights to enhance player fitness and reduce injuries.
--
- Chunk Title: Moving Towards Evidence-Based Decisions
- Chunk Text: HR analytics helps move from intuition-based to evidence-based decisions.
- Detailed Explanation: This chunk emphasizes the transition from making decisions based on gut feelings or intuition to making them based on solid evidence drawn from data analysis. Evidence-based decisions are more reliable, as they are backed by factual data rather than assumptions. In HR, this means that actions such as hiring, promotions, or strategy changes are made based on analyzed data rather than solely on personal judgment.
- Real-Life Example or Analogy: Think of this transition like switching from guessing the best route to a destination based on previous experiences to using a GPS. The GPS provides real-time data and traffic patterns, leading you to the best route, much like HR analytics guides decisions with factual insights rather than simply relying on past experiences or intuition.
--