People analytics is about gathering and analysing data about people in a workforce. It’s sometimes called HR analytics or workforce analytics. People data is found in HR systems, from other departments like IT and sales, and from external sources such as salary surveys. Using people data offers the opportunity to contribute to an organisation’s strategy by creating insights on what people can do to drive change.

This factsheet explores what people analytics is, why it’s important and how it’s used. It introduces key terms such as correlation, causation, predictive and prescriptive. It also discusses who is responsible for people analytics as well as the strategy and process.

People analytics is about analysing data about people to solve business problems. It’s sometimes called HR 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. These analytical processes are related to key ideas such as human capital, HR systems and processes, organisational performance, and also consider external benchmarking data’.

Five reasons for using people analytics:

  1. It can be used to measure a workforce, for internal and external stakeholders, in a range of areas such as performance, wellbeing, and inclusion and diversity. See more on workforce planning.

  2. It enables more effective evidence-based decisions on improving workforce and organisational performance.

  3. It can demonstrate the impact of HR policies and processes on workforce and organisational performance.

  4. It can be used to estimate the financial and social return on investment of change initiatives.

  5. ‘Analytics and creating value’ is a core knowledge element in our Profession Map, with ‘people analytics’ as a specialist knowledge element.

People analytics can be applied to almost an aspect of HR activity. For example:

  • Enhancing employee morale: Organisations can measure the drivers of employee engagement and adapt their practices accordingly to enhance employee morale.

  • Improving retention: Organisations suffering from high turnover of key employee groups can use people analytics to anticipate areas with specific issues and tailor incentives to curb attrition. Find out more on turnover and retention.

There’re more case studies of people analytics in action in our Valuing your Talent web pages and in our research report Human capital analytics and reporting: theory and evidence.

Find out more about how HR and finance professionals are using people data in our report People analytics: driving business performance with people data in association with Workday, as well as the summary reports People analytics: international perspectives.

Potentially. Technology makes it easy to seamlessly collect data about people. Websites visited, time spent on specific apps, comments made on the organisation’s social networking site. Organisations can monitor their workforce within the bounds of law where they operate. Even if it’s lawful, how an organisation collects and uses monitoring data can be contentious, particularly if employees feel that it’s irrelevant, unnecessary or too intrusive. Watch Don’t be creepy: how to use data for good by Dr Heather Whiteman of the University of California, Berkeley at ‘People Analytics & Future of Work’ 2020.

If introducing employee monitoring software, it’s important to:

  • Be transparent. Explain clearly what you’re monitoring and why.
  • Consult with employees to ensure the measures are relevant and necessary. Measures can be about ensuring compliance as well as helping employees become better at their jobs.
  • Be mindful of cultural differences and monitor your system to make sure it does not discriminate against minority groups.

Descriptive, predictive and prescriptive analytics are terms which are often used to describe the maturity level of the people analytics capability in an organisation.

  • Level 1a – descriptive analytics: Uses descriptive data to show, for example absence and annual leave records, and attrition and recruitment rates. At level 1 data is used to describe a snapshot at particular point in time or a trend. See our factsheets which give commonly-used absence measures and employee turnover and retention measures.

  • 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 future trends. For example, looking at historical workforce data and external labour market trends 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 automatically recommend options. For example, an online learning platform that recommends courses for a learner based on their interests, career goals and past courses.

Most organisations can do descriptive analytics but few as yet can do prescriptive analytics. This is changing though as more apps offer analytics out of the box. Having a mature people analytics capability expands what you can analyse and automate. However, as discussed in People analytics effectiveness: developing a framework, using more advanced analytics doesn’t always bring more value to the organisation. Valuable insights can come from descriptive analytics.

People analytics can help identify whether one or more things can reliably predict something else. To do this, we use quantitative and/or qualitative data to build a predictive model. If the model reliably predicts something, we say there is a correlation and describe the strength of the relationship as a number. But correlation does not imply causation.

  • Quantitative data: is quantifiable and objective. It can be described in numbers. The number of employees, average age and salary range are examples.

  • Qualitative data: describes the qualities observed by someone and is subjective. It is useful for understanding the ‘what’, ‘why’ and ‘how’ of something. Employee engagement, performance appraisals and exit interview notes are examples of qualitative data. Qualitative data can be turned into quantitative data. For example, a performance appraisal can be summarised as a performance rating.

  • Correlation: is when two or more things that happen around the same time might be associated with each other. For example, a survey reviewed in In a Nutshell issue 106 found a link between employee perceptions of corporate social responsibility (CSR) and their work engagement. But the survey cannot prove that positive perceptions of CSR result in high work engagement.

  • Causation: is 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.

Remember that organisations are not closed systems. It’s important to look beyond the analytics and consider other factors that can’t easily be measured before drawing conclusions. When analysing race data, for example, consider where structural discrimination can hide. A lack of diversity in frontline staff might reflect a long-term lack of investment in public transport and residential segregation.

It varies. Large organisations may have a centralised people analytics team that provide insights to stakeholders in the organisation. Some organisations prefer a decentralised approach where individual HR analysts within small centres of expertise provide insights within their specialist domain. Others prefer to outsource their analytics. In practice, organisations usually take a hybrid approach.

Although data is held in many places in an organisation, it should ideally be managed by a specific data owner. The data owner is responsible for ensuring that data is maintained and kept secure according to the organisation’s data protection policy. Only those responsible for the people data should be able to change the structure of the people data itself, such as the definitions for specific HR indicators. Employees and managers can view and update some of their personal data through self-service. Find out more about data protection in the UK.

People analytics projects should align to both the business and the HR strategy. Solving a critical business issue is likely to create the most value for the business and create further demand to create 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. A sizeable minority of the people profession find this part challenging. Almost a quarter of respondents to our People Profession Survey 2020 said that they don’t have clear measures of success for measuring their impact.

Our practitioner’s guide explores the first steps to building a people analytics strategy, developing simple analytics capabilities. In our research report Human capital analytics and reporting: theory and evidence, we summarise key academic concepts to apply in a people analytics strategy.

The people analytics process should follow nine steps from planning through to evaluation. In practice, the process can be shorter. For example, a recent data audit can be reused, or when analysis and reporting have been automated.

  1. Plan: Develop the goals and purpose for the analytics activity. Map the requirements of the customer and plan questions/queries which will be answered by the analytics process.

  2. Define critical success factors: Define the measures that will show if the project has been a success. Examples of what these can be based on include: delivery on time, impact of project, feedback from users..

  3. Data audit: Map the data which is currently available and grade its quality. This will illustrate where any gaps in data may be, which should be filled 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 (for example, absence records) or collect new data (for example, by running an engagement survey).

  7. Analyse data: Analyse data and create insights, in line with the stakeholders’ requirements.

  8. Report data: Report a solution to the problem clearly and recommend further areas of investigation if needed.

  9. Evaluate: Review the process and evaluate impact. Update process as required.

Books and reports

EDWARDS, M. and EDWARDS, K. (2016) Predictive HR analytics: mastering the HR metric. London: Kogan Page.

KHAN, N. and MILLNER, D. (2020) Introduction to people analytics. London: Kogan Page.

MARR, B. (2018) Data-driven HR: how to use analytics and metrics to drive performance. London: Kogan Page.

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

Journal articles

BASKA, M. (2018) Six ways analytics will future-proof HR. People Management (online) 6 June.

GARCIA-ARROYO, J. and OSCA, A. (2019) Big data contributions to human resource management: a systematic review. International Journal of Human Resource Management (online). 9 October. Reviewed in In a Nutshell, issue 93.

GREASLEY, K. and THOMAS, P. (2020) HR analytics: the onto-epistemology and politics of metricised HRM. Human Resource Management Journal, Vol 30, Issue 4, November. pp494-507. Reviewed in In a Nutshell, issue 103.

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

RASMUSSEN, T. and ULRICH, D. (2015) Learning from practice: how HR analytics avoids being a management fad. Organizational Dynamics. Vol 44, No 3, July-September. pp236-242.

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.

This factsheet was last updated by Hayfa Mohdzaini.

Hayfa Mohdzaini: Senior Research Adviser

Hayfa joined in 2020 as the CIPD's Senior Research Adviser in Data, Technology and AI. She started her career in the private sector working in IT and then HR, and has been writing for the HR community since 2012. Previously she worked for another membership organisation (UCEA) where she expanded the range of pay and workforce benchmarking data available to the higher education HR community. Hayfa has degrees in computer science and human resources from University of York and University of Warwick respectively.

She is interested in how the people profession can contribute to good work through technology.