People Analytics enables HR to make data-driven decisions linked to the organization's top and bottom line. That’s why many companies are upgrading with People Analytics skills. But what is People Analytics, and how can you use it in practice?
Table of contents:
- What is People Analytics?
- Who is responsible for People Analytics?
- Why use People Analytics?
- How to use People Analytics in practice
- Mastering the employee experience
What is People Analytics?
People Analytics is also referred to as HR Analytics or Workforce Analytics and is a data-driven method to better understand employees in the organization. People Analytics enables HR to make data-driven decisions in connection with, e.g., recruitment, onboarding, retention, etc. That way, HR doesn't have to make decisions based on its own gut feelings. You might think of People Analytics as HR’s navigational system for its daily work.
People Analytics can transform employee insight into tangible business insight that can be acted on, such as:
- Why do employees leave the company?
- How can we lower the redundancy rate?
- To what extent do we offer an inclusive employee experience?
- How does job satisfaction relate to performance?
- What is the optimal span of control?
who is responsible for People Analytics?
The work with People Analytics is usually placed with and owned by HR. But it does not necessarily have to be an HR task. If HR, e.g., doesn't have the analytical skills needed, it could be a good idea to involve Finance, IT or a third team if you prefer to keep People Analytics work in house.
Why use People Analytics?
It is with good reason that many companies are upgrading their work with people Analytics and trying to create a more data-driven culture in HR. People Analytics can be used to enhance your decision-making process. A data-driven decision-making process in HR has a number of advantages, such as:
- Making it possible to connect the employee experience to the top and bottom line
- People Analytics lets you structure, e.g. your diversity and inclusion work.
- People Analytics provides a better understanding of employee turnover and retention
- Reveals the optimal span of control
How to use People Analytics in practice
You can work with people Analytics on several levels, depending on the data insight you need, the purpose of the insight, and the available resources HR has to work with it.
Specific examples of the five levels of People Analytics:
Level 1: Basic reporting
Here, your People Analytics activity can be, e.g., retrospective ad hoc reporting.
Insight could be: “36 engineers left the company last quarter.”
Level 2: Advanced reporting
Here, your People Analytics activity can be regular reporting.
Insight could be: “12% more engineers left the company this year compared to last year. The increase is primarily due to the Eastern Region.”
Level 3: Diagnostics
Here, your People Analytics activity can be an exploratory analysis across data sets, hypothesis testing or static methods.
Insight could be: “Time on same project and amount of training have a big influence on employee turnover among our engineers.”
Level 4: Predictive
Here, your People Analytics activity can be the use of statistical models to predict future events.
Insight could be: “Employee turnover among engineers will increase 5% in the Eastern Region in 2023.”
Level 5: Prescriptive
Here, your People Analytics activity can be fully or partly automated AI that monitors, predicts or suggests solutions.
Insight could be: “Employee turnover among engineers will decrease 7% with investment in 10% more promotions next year.”
If you don't already, you should consider People Analytics as a strategic business tool that provides you with the necessary employee data to develop an even stronger organization.
Mastering the employee experience
The book “Mastering Employee Experience – 16 specific steps to take in your EX transformation” gives HR and top management 16 specific initiatives for implementing a transformation of employee experience over a 3-year period. Download your free book extract here.
