HR analytics is the use of people-data in analytical processes to solve business problems. HR analytics uses both people-data, collected by HR systems (e.g. payroll, absence management) and business information (e.g. operations performance data). At its core, HR analytics enables HR practitioners and employers to gain insights into their workforce, HR policies and practices, with a focus on the human capital element of the workforce, and can ultimately inform more evidence-based decision making.

The factsheet examines the importance of HR analytics in understanding if an organisations workforce is generating value. It provides an overview of the quantitative and qualitative forms of HR data, including the cause and effect relationships between data sets. The factsheet looks at the main levels of HR analytics capability and considers the key people responsible for HR analytics in a workplace. It examines the aims of an HR analytics strategy and the nine steps of the HR analytics process, from planning to evaluating. The factsheet concludes by looking at several examples of HR analytics in action.

HR analytics is both a strategic and operational concept that enables organisations to understand and articulate important aspects of their workforce through using data and evidence. It is a growing discipline that continues to gain considerable traction across the profession, but our HR Outlook survey data has shown that the capability to conduct HR analytics remains low. Therefore the profession should view data and analytics as an emergent HR capability, and one that requires further investment, in terms of both capability and research.

Organisations which follow good practice should have up to date, clearly defined data which is robust and of high quality, and which is used in a consistent way by skilled experts able to complete analytics activity and communicate it to business and HR stakeholders in regular and accessible reports.

HR analytics is a HR practice enabled by information technology that uses descriptive, visual and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making.

Analytics may be used to look at the traits of the workforce, in particular its human capital: the value of individual knowledge, skills and experience of individuals and teams. This is also known as human capital analytics. When an organisation reports on the insights gathered through HR analytics, it’s often known as human capital reporting.

Our research report Human capital analytics and reporting: theory and evidence summarises key academic concepts for practitioners to apply through HR analytics strategy.

Why is HR analytics important?

HR analytics enables HR and their major stakeholders to measure and report key workforce concepts, such as performance, well-being, productivity, innovation and alignment. This in turn enables more effective evidence-based decisions by strategic business functions. HR analytics enables HR teams to demonstrate the impact that HR policies and processes have on workforce and organisational performance, and can be used to demonstrate return-on-investment and social-return-on-investment for HR activity. Business managers are increasingly interested in how to use HR concepts more effectively, and so HR analytics is an important way in which HR teams can evaluate and improve people and business performance.

HR data is information about any aspect of employees or the HR management system. Data comes in many forms, and may be quantitative or qualitative.

Quantitative data can be measured and illustrated through numbers

  • Objective
  • How many? How much?
  • Facts are value-free / unbiased
  • Measurable
  • Report statistical analysis. Basic element of analysis is numbers
  • ‘Counts the beans’
  • Examples: number of employees, remuneration rates, productivity

Qualitative data can’t be measured and are often subjective assessments representing an individual’s view of something.

  • Subjective
  • What? Why?
  • Facts are value-laden and biased
  • Interpretive
  • Report rich narrative, individual; interpretation. Basic element of analysis is words/ideas.
  • Provides information as to ‘which beans are worth counting’
  • Examples: employee opinion survey feedback, appraisals and performance reviews, learning and development outcomes

For example, a fondness for chocolate is qualitative data as it relates to an individual’s preference towards chocolate, while the dimensions of a chocolate bar is quantitative data as it relates to its numerical size (length, width and height). In HR, an individual’s age or performance rating is quantitative data, whereas their engagement data (such as job satisfaction) is qualitative.

Data is held in many places in an organisation but ideally should be managed by a specific data owner who is responsible for maintaining it, keeping it secure and ensuring management in line with the data protection policy. Only those responsible for the HR data should be able to change any aspect of the HR data itself (for instance changing terminology or definitions for specific HR indicators). Find out more in our data protection factsheet.

Correlation and causation

HR analytics can help identify cause and effect relationships, by investigating the relationship between two sets of data to be investigated, and determining whether the relationship is correlational or causal.

  • Correlation is when two or more things or events happen around the same time which might be associated with each other, but they aren’t necessarily related in a cause-effect relationship. It implies a mathematical relationship between two things which are measured, and is often described numerically with a value between 0 and 1, where 0 is no relationship and 1 is a fully predictive relationship. For example, there is a 0.01 correlation between eye colour and height, so knowing someone’s eye colour does not mean you know how tall they are. They are virtually independent. But there is a 0.8 correlation between smoking and incidence of lung cancer, so it's possible to say that smokers are more likely to develop lung cancer. However, this doesn’t necessarily imply smoking causes lung cancer in every case.

  • Causation is when one event or thing happens and as a result of it happening, another event or thing happens. If the first event did not happen, then the second does not happen. There is not a mathematical/probabilistic relationship between the two, but instead a time-based cause-effect relationship.

Just because two things correlate, it doesn't necessarily mean there's a causal (or cause-effect) relationship between them. For example, an increase in sales in a team which also has high engagement does not necessarily mean that engagement causes more sales. Other factors, such as improved training, or increase in customer-facing staff, may also be causing an increase in sales. Therefore, it's important to analyse as much data as possible before drawing conclusions.

HR analytics uses workforce or HR data, either qualitative or quantitative, to investigate a certain concept with the help of computer programmes and modelling techniques. There are three main levels of HR analytics capability. Most organisations are able to do level 1 only, very few are able to complete level 3 analytics:

  • Level 1a– descriptive analytics: Uses descriptive data to illustrate a particular aspect of HR, for example recording absence, annual leave, and attrition and recruitment rates. At level 1, no analysis is applied to the data beyond using it to describe a certain concept, or illustrate its change over time (sometimes called trend analysis). See our factsheets which give commonly-used absence measures and measures of turnover and retention.

  • Level 1b – descriptive analytics using multidimensional data: Combines different data sets, or types of data, to investigate a specific idea can help to uncover interesting relationships between different HR activities and processes. Using two different types of data to create an analytics output is known as multidimensional analytics (for example, combining leadership capability data with engagement scores to measure leadership effectiveness).

  • Level 2 – predictive analytics: Uses data to predict future trends can help HR professionals to plan for future events and scenarios, and ensure they are able to deliver to the business. Predictive analytics for forecasting requires high quality and robust data, and specialist technology and capability.

  • Level 3 - prescriptive analytics: Applies mathematical and computational sciences to suggest decision options to take advantage of the results of descriptive and predictive analytics. Prescriptive analytics specifies both the actions necessary to achieve predicted outcomes, and the interrelated effects of each decision.

Approaches differ across organisations and sectors with businesses developing individual approaches to how they conduct HR analytics. Large organisations often build their own central HR analytics team to own and manage the HR analytics process and provide insights to customers/users in the business. Some organisations prefer to base individual HR analysts within small centres of expertise to explore specific concepts (for example, the impact of L&D activity may be based in the L&D team) and deliver outputs from a de-centralised model. Others prefer to outsource analytics to external experts who can use HR data to provide insights and guidance from an independent expert perspective.

Organisations tend to move between approaches according to their HR operating model. As such, different combinations of HR analytics technology and management can co-exist at one time.

HR analytics strategy

HR managers running analytics activity should tie the outputs of their analytics process into both the HR strategy and the business strategy. Taking a strategic and planned approach to HR analytics, for example tackling a specific business issue, is likely to create the most value for the business and create further demand for HR insights.

Connecting HR data with the strategic objectives of the business can help HR managers to demonstrate the return on investment (ROI) of HR. The type of data used will depend on the strategy and operations of the organisation, so for example a sales-driven organisation is more likely to collect performance data such as sales per employee to differentiate between employees and reward them accordingly.

The HR analytics strategy should have three aims:

  • Connect HR data with business data to demonstrate a particular aspect of the organisation that business leaders should be informed about to help them make decisions.
  • Enable HR leaders to design and implement HR management activity in an efficient and effective manner.
  • Allow the business and HR to measure the effectiveness of HR in delivering against its objectives.

HR analytics process

The HR analytics process should follow nine steps from planning through to the evaluation of the process:

  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 data sources. This can be from drawing on established data sets (for example. absence records) or running new data collection processes (for example, engagement survey).

  7. Analyse data: Depending on the customer requirements, analyse the data and develop insights in the form of recommendations and guidance for the users of the data.

  8. Report data: Report in a clear and simple way illustrating a solution to their issue, or further areas of investigation if further data is required.

  9. Evaluate: Review the data-analytics-insights process and evaluate impact. Review and update process as required.

HR analytics can be applied to virtually any aspect of HR activity. For example:

  • Enhancing employee morale: instead of absorbing the costs of losing key employees, organisations can mitigate against increased attrition rates by measuring the happiness and well-being of their employees and adapting their offer to employees accordingly. Career-development planning, and learning and development for high performers are both ways in which HR departments can use HR data to help improve the morale of the workforce.

  • Driving business performance: HR analytics can help to address performance issues by identifying workers with strong leadership skills and flagging those which do not mix with the culture of the team or organisation. By better matching job applicants or future successors to the right positions, organisations can improve their overall performance.

  • Improving retention: An organisation which is suffering from high turnover of key employee groups can use HR analytics to anticipate areas with specific issues and can then tailor their incentives to curb attrition accordingly. Better measuring the impact of HR activity on turnover can illustrate the specific needs of certain employee groups, for example adapting incentives for senior leaders to meet their needs if they have specific requirements to keep them from leaving.

More case studies of HR analytics in action can be found on the Valuing your Talent website and in our research report Human capital analytics and reporting: theory and evidence.

Books and reports

BASSI, L., CARPENTER, R. and McMURRER, D. (2012) HR analytics handbook. McBassi & Company.

HUUS, T. (2015) People data: how to use and apply human capital metrics in your company. London: Palgrave Macmillan

SMITH, T. (2013) HR analytics: the what, why and how... London: CreateSpace Independent Publishing Platform.

Journal articles

ANGRAVE, D., CHARLWOOD, A., KIRKPATRICK, I., LAWRENCE, M., and STUART, M. (2016) HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal. Vol 26, No 1, January pp1–11

BOUDREAU, J. and CASCIO, W. (2017) Human capital analytics: why are we not there?Journal of Organizational Effectiveness: People and Performance. Vol 4 No 2, pp119-126.

HIGSON, C. (2015) Accounting for people. London Business School Review. Vol 26, No 3. pp15-17.

MARLER, J.H. and BOUDREAU, J.W. (2017) An evidence-based review of HR analytics. International Journal of Human Resource Management. Vol 28, No 1. pp3–26.

MCAFEE, A. and BRYNJOLFSSON, E. (2012) Big data: the management revolution. Harvard Business Review. Vol 90, No 10, October. pp61-66,68.

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 Edward Houghton.

Ed Houghton

Edward Houghton: Senior Research Adviser:Human Capital and Governance

Edward Houghton is the CIPD's Senior Research Adviser: Human Capital and Governance.  Since joining the institute in 2013 he has been responsible for leading the organisation's human capital research work stream exploring various aspects of human capital management, theory and practice; including the measurement and evaluation of the skills and knowledge of the workforce. He has a particular interest in the role of human capital in driving economic productivity, innovation and corporate social responsibility. Recent publications have included “A duty to care? Evidence of the importance of organisational culture to effective governance and leadership” for the Financial Reporting Council’s Culture Coalition, and “A new approach to line manager mental well-being training in banks” an independent evaluation of the Bank Workers Charity and Mind partnership to deliver mental health awareness training in the UK financial services sector. 


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