People analytics is about analysing data about people to solve business problems. It’s sometimes called HR analytics, talent analytics or workforce analytics. One academic paper defines it as ‘a number of processes, enabled by technology, that use descriptive, visual and statistical methods to interpret people data and HR processes.' 

Examples of people analytics in action

You can use people analytics to gain insights on:

• Enhancing employee morale: By measuring what drives engagement and adapting practices to boost employee morale.
• Improving retention: By identifying groups with high attrition and tailoring incentives to curb attrition. 

For more examples see:

• Solving business problems with people analytics: case studies
• Valuing your Talent
• Human capital analytics and reporting: theory and evidence.

Why is people analytics important?

These terms describe the maturity of an organisation’s people analytics capability. Most organisations can do descriptive analytics and few can do prescriptive analytics. But this is changing as more digital solutions offer analytics out of the box. Having a mature people analytics capability expands what you can analyse and automate. Using advanced analytics doesn’t always give you more valuable insights. Depending on the problem you are solving, you can get valuable insights with just descriptive analytics.

• Level 1a – descriptive analytics: Describes a particular period or a historical trend. For example, employee turnover and  ‘lost time’ rate due to absence. 
• Level 1b – descriptive analytics using multidimensional data: Combines different types of data to investigate a specific idea. Like combining leadership capability data with engagement scores to measure leadership effectiveness. 
• Level 2 – predictive analytics: Uses data to predict what might happen. For example, looking at historical workforce data and external labour market trends and using these to build a model that predicts the organisation’s future workforce needs. The data needs to be relevant, high quality and robust for predictions to be reliable. 
• Level 3 – prescriptive analytics: Uses the results of descriptive and predictive analytics to immediately recommend actions. An example of this is an online learning platform. It recommends courses for a learner based on their interests and career goals, as well as the courses that they and similar learners have taken

We define the terms below:

• Quantitative data: The number of employees and average age are examples of quantitative data. Quantitative data are quantifiable.
• Qualitative data: Performance appraisals and exit interview notes are examples of qualitative data. Qualitative data describes the qualities observed by someone and is subjective. It is useful for understanding the ‘what’, ‘why’ and ‘how’ of something. You can turn qualitative data into quantitative data. For example, you can summarise a performance appraisal as a performance rating. 
• Correlation: When two or more things happen around the same time and there may or may not be a causal link between them (see causation below). For example, there's a link between poor health and performance-related pay, but poor health isn't always due to performance related pay. 
• Causation: When something happens, it causes something else to happen. For example, during school holidays more employees with school-aged children go on leave. To prove causation, you usually need to analyse data from different points in time.

People analytics can help identify whether one or more things can reliably predict something else. To build a predictive model, you use historical and current data to forecast future outcomes. If a model reliably predicts something, you can say there’s a correlation and describe the strength of the relationship as a number. But correlation does not imply causation. 

Remember, organisations are part of a wider network of people. You need to look beyond what the data says before drawing conclusions. When analysing diversity data, for example, think about what other factors might be affecting the numbers. Perhaps the reason there are few frontline employees from different backgrounds is because people in the local residential areas aren’t diverse. Or perhaps there are no bus or train services to get to work. Uncovering these insights might lead you to advocate for better public transport to your workplace.

It varies. There might be a central people analytics team. Or several people analytics specialists in different teams across the organisation. Others prefer to outsource their analytics. In practice, organisations usually take a hybrid approach.

There should be a data owner who oversees the management of people data. They oversee the maintenance, security and compliance of people data across systems.  Find out more in our data protection factsheet.

People analytics projects should align to both the business and HR strategy. Solving a critical business issue is likely to create the most value for the business and spur demand for more insights from people data.

A people analytics strategy should have three aims:

1. Connect people data with business data to inform business leaders and help them make decisions.

2. Enable HR leaders to use insights from the analytics to design and implement appropriate HR activities.

3. Measure HR’s effectiveness in delivering against its objectives. 

Read our practitioner’s guide on building a people analytics strategy and capability. For more on the theory, see our research report Human capital analytics and reporting.

The people analytics process should follow nine steps from planning through to evaluation. In practice, the process can be shorter. For example, if you can reuse a recent data audit, or if you've automated some analysis and reporting:

1. Plan: Develop the objectives for the analytics activity. Map stakeholder needs and design the questions.
2. Define critical success factors: Define the measures that will show if the project has been a success. These might include: on-time delivery, project impact and user feedback. 
3. Data audit: Identify what data is currently available and grade its quality. This will show data gaps to fill before progressing.
4. Design the process: Define roles and set objectives for team members. Define resource requirements and map stakeholders for the project.
5. Design the data collection strategy: Design the collection and processing stages of the analytics activity.
6. Data collection: Collect data from existing data sets (e.g. absence records) or collect new data (e.g. run an engagement survey).
7. Analyse data: Analyse the data in line with the stakeholders’ requirements. 
8. Report data: Recommend solutions to the problem and further areas of investigation if needed.
9. Evaluate: Review the process and check impact. Update process as required.

Books

Ferrar, J. and Green, D. (2021) Excellence in people analytics: how to use workforce data to create business value. London: Kogan Page.

Hall, C., Mattox, J. R., Parskey, P. (2020) Learning Analytics: using talent data to improve business outcomes. London: Kogan Page.

Jang, D. S., Edwards, M., Edwards, K. (2024) Predictive HR analytics: mastering the HR metric. London: Kogan Page.

Visit the CIPD and Kogan Page Bookshop to see all our priced publications currently in print.

Journal articles

Arena, M., Hines, S. Golden III, J. (2023) The three Cs for cultivating organizational culture. Organizational Dynamics. Vol 5, No 1. Reviewed in Bitesize research.

Fu, N., Keegan, A. and McCartney, S. (2023) The duality of HR analysts' storytelling: Showcasing and curbing. Human Resource Management Journal. Vol 33, Issue 2, pp261–286. Reviewed in Bitesize research.

Jeffery, R. (2019) Amazing insights you can learn from people analytics. People Management (online). 21 February.

Kennedy, E. J. (2020) Can data drive racial equity? 3 December. Reviewed in Bitesize research.

CIPD members can use our online journals to find articles from over 300 journal titles relevant to HR.

Members and People Management subscribers can see articles on the People Management website.