People analytics: a practical introduction for HR
A practical introduction to people analytics: the four levels of analysis, how it differs from HR reporting, the questions it answers, a six-step start for small teams, and data privacy and ethics.
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- TeamCheck Editorial Team
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- العربية
Key takeaways
- People analytics uses data about people at work to explain why things happen and to inform decisions, not only to report what happened.
- Accurate, consistently defined reporting is the foundation that every deeper analysis depends on.
- A small team can start by choosing one business question, cleaning the relevant data, analysing simply and turning the findings into a recommendation.
- Data quality, privacy obligations and ethical checks are part of the work, because trust is what keeps employee data honest.
People analytics is the practice of collecting, analysing and interpreting data about people at work to answer business questions and improve decisions about hiring, development, engagement and retention. It moves HR from reporting what happened, such as how many people left last quarter, toward explaining why it happened and what to do next. It is also called HR analytics or workforce analytics.
You do not need a data science team to start. This guide explains how people analytics differs from routine HR reporting, the questions it can answer, a six-step way for a small team to begin, and the data quality, privacy and ethics rules that keep the work trustworthy.
What is people analytics?
People analytics applies structured analysis to data about employees and candidates so that decisions rest on evidence rather than on assumption alone. In their review of the research, Marler and Boudreau (2017) described HR analytics as an HR practice enabled by information technology that uses descriptive, visual and statistical analysis of data related to HR processes, human capital and organizational performance to establish business impact and support data-driven decisions.
The terms vary. A scoping review by Tursunbayeva, Di Lauro and Pagliari (2018) found that "people analytics", "HR analytics", "workforce analytics" and "talent analytics" are used for overlapping ideas, with no single agreed definition. In practice, what matters is less the label than the discipline: a clear question, reliable data, sound analysis and a decision at the end.
The four levels of analysis
People analytics work is often described in four levels, each building on the one before.
| Level | Question it answers | Example in HR | Typical method |
|---|---|---|---|
| Descriptive | What happened? | Turnover rate by department last year | Counts, rates, dashboards |
| Diagnostic | Why did it happen? | Which factors are linked to higher turnover in one department | Comparisons, correlations, survey analysis |
| Predictive | What is likely to happen? | Which teams show early signs of rising turnover risk | Statistical or machine learning models |
| Prescriptive | What should we do? | Which interventions to try first, and where | Scenario analysis, experiments, judgment |
Most organizations spend most of their effort at the first two levels, and that is fine. Accurate descriptive data and careful diagnosis answer many real questions. Predictive and prescriptive work needs good data, enough volume and strong governance, and it adds risk if those foundations are weak.
People analytics vs HR reporting
HR reporting and people analytics use much of the same data, but they serve different purposes.
| Aspect | HR reporting | People analytics |
|---|---|---|
| Starting point | A standard set of metrics | A business question |
| Main output | Regular reports and dashboards | Insight and a recommendation |
| Typical question | How many people did we hire? | Which hiring methods are linked to better performance after a year? |
| Time focus | Past and present | Past, present and likely future |
| Data used | Usually one system at a time | Often several sources combined |
| Audience | HR operations and managers | Leaders making a specific decision |
| Success measure | Accurate, timely numbers | A better decision that people act on |
Reporting is the foundation for analytics, not a lesser version of it. If headcount, turnover and hiring numbers are not accurate and consistently defined, any deeper analysis built on them will mislead. Standards can help with consistent definitions: ISO 30414, whose second edition was published in 2025, sets requirements and recommendations for human capital reporting and disclosure, covering areas such as workforce composition, costs, productivity, turnover, and skills and development.
Questions people analytics can answer
Good people analytics starts with a question leaders care about. Some common ones follow.
Attrition and retention
- Where is voluntary turnover highest, and is it rising or falling?
- At what point in the employee lifecycle do people leave most often, for example in the first year?
- What do exit survey responses and engagement results suggest about the reasons?
Engagement and employee experience
- Which statements in the engagement survey score lowest, and in which departments?
- How do results differ by manager, and which teams have improved since the last survey?
- Which themes appear most often in open comments?
Measures such as eNPS give a quick read of advocacy; our guide explains what eNPS is and how to use it. For the questions themselves, see our list of employee engagement survey questions.
Hiring quality
- Which sources and selection methods are linked to stronger performance or longer tenure?
- Where do candidates drop out of the process, and does that differ by role?
- How long does it take to fill critical roles, and where are the delays?
Skills and capability
- Which skills does the organization have now, and which will it need?
- Where are the largest gaps between required and current skills?
- Which employees are ready, or nearly ready, for critical roles?
A skills matrix is a practical way to collect and view skills data at team level before you analyse it across the organization.
How to start a people analytics practice in 6 steps
A small team can deliver useful analysis within a few months by keeping scope narrow and focusing on one question at a time.
- Pick one business question. Choose a question tied to a decision leaders need to make, such as "Why is first-year turnover higher in our customer service teams?" Avoid starting with "What does our data tell us?", which rarely leads anywhere specific.
- Map the data you have. List the sources that could help answer the question: HR records, survey results, assessment results, exit data, performance ratings. Note where each lives, who owns it and how reliable it is.
- Clean and define. Agree definitions before analysing, for example what counts as voluntary turnover and how tenure is calculated. Fix obvious errors, remove duplicates and document every decision so the analysis can be repeated.
- Analyse simply first. Start with descriptive comparisons across departments, tenure bands or time periods. Only move to more advanced methods if simple analysis cannot answer the question.
- Turn findings into a recommendation. Present the answer, the evidence and its limits in plain language, with one to three suggested actions. Leaders need to know what to do, not every chart you produced.
- Act and measure again. Agree who owns each action and when you will check the effect. Repeating the measurement, for example in the next survey cycle, shows whether anything changed.
The value of people analytics is not in the dashboard. It is in the decision that changes because of what the data showed.
Skills and roles you will need
Early on, the work is usually done by one or two people combining HR knowledge with comfort in spreadsheets and basic statistics. As the practice grows, organizations often add skills in data engineering, statistics and data visualization. Equally important are skills in consulting and communication: framing questions with leaders and explaining results without overstating them.
Data quality, privacy and ethics
People data is personal and sometimes sensitive. How you handle it matters as much as what you find.
Data quality
- Consistent definitions. The same metric should mean the same thing in every report and department.
- Complete records. Missing fields, such as exit reasons or job levels, can skew results. Track how much data is missing before drawing conclusions.
- Enough volume. Small groups produce unstable results. A rise from two leavers to four is a doubling, but it may be chance.
- Documented sources. Record where each data set came from and when it was extracted.
Privacy
Employee data is personal data, and its collection and use are regulated in many countries. In Saudi Arabia, the Personal Data Protection Law, overseen by the Saudi Data and Artificial Intelligence Authority (SDAIA), sets obligations for organizations that process personal data. Check the requirements that apply where you operate, including what employees must be told, the purpose for processing, who can access the data and how long it is kept. This is a general note, not legal advice.
Practical safeguards include:
- Collecting only the data you need for the question.
- Reporting survey and assessment results in groups large enough to protect individuals, and not showing results for very small teams.
- Limiting access to identifiable data to the people who need it.
- Setting and following retention periods.
Ethics
Legal compliance is the minimum, not the goal. Some ethical questions to ask before any analysis:
- Would employees be surprised? If people would be uncomfortable learning how their data is used, reconsider or explain it openly first.
- Could it harm individuals? Predictions about who might leave or who has potential can become self-fulfilling if used carelessly. Use them to inform support for groups, not to label individuals.
- Is the model fair? Models trained on past decisions can repeat past biases. Check results across groups before using them.
- Is there a human in the loop? Analytics should inform decisions that people make and can explain, not replace them.
Being open about what you analyse and why builds the trust that honest survey responses depend on.
Where tools can help
Analytics depends on clean, well-structured data at the source. With TeamCheck's Employee Experience solution, survey results come with an organization structure view showing results per department, satisfaction rates per manager and department, results per statement, employee comments, and a downloadable detailed report and executive summary. TeamCheck's Technical Assessments include real-time dashboards, and psychometric assessment reports can be exported for further analysis. You can see how the modules fit together on the TeamCheck platform page.
Common mistakes
- Starting with the data, not the question. Exploring data without a decision in mind produces interesting charts and no action.
- Building dashboards nobody uses. A dashboard is only useful if it answers questions someone has. Ask before you build.
- Ignoring data quality. Inconsistent definitions and missing records lead to confident but wrong conclusions.
- Confusing correlation with cause. Two things moving together does not mean one causes the other. Look for other explanations before recommending action.
- Over-reading small numbers. Percentages based on a handful of people swing widely. Report counts alongside rates.
- Reporting on individuals by accident. Slicing data too finely can expose a single person's responses. Set minimum group sizes and keep to them.
- Stopping at insight. Analysis that does not lead to an agreed action, and a follow-up measurement, is unlikely to change anything.
Frequently asked questions
What is the difference between people analytics and HR analytics?
In practice the terms are used almost interchangeably. Some people use "HR analytics" for analysis of HR processes and functions, such as recruitment efficiency, and "people analytics" for a broader view that links workforce data to business outcomes and employee experience. Research reviews note that the terms overlap without a single agreed definition, so focus on the questions you answer rather than the label.
What data is used in people analytics?
Common sources include core HR records (headcount, roles, tenure, movements), recruitment data, performance and development records, engagement and other employee surveys, assessment results, exit data and learning records. Some organizations add operational or financial data to link people measures to business results. Use only data relevant to the question and handle it according to the privacy rules that apply.
Do you need a data scientist to start people analytics?
No. Many useful questions can be answered with accurate data, clear definitions, a spreadsheet and careful descriptive analysis. What matters most at the start is HR knowledge, a good question and the ability to explain findings clearly. Data science skills become more important as you move toward predictive models, combine large data sets or automate analysis.
What are the first metrics to track in people analytics?
Start with metrics linked to the questions leaders are asking. Common starting points are voluntary turnover, including first-year turnover, time to fill roles, engagement survey results by department and manager, and internal movement rates. Define each metric precisely, track it consistently over time and add new metrics only when a new question needs them.
Summary
People analytics helps HR move from reporting what happened to explaining why and deciding what to do. Start with one business question, map and clean the data you have, analyse simply, turn findings into a clear recommendation and measure again after acting. Protect data quality, privacy and trust at every step, and use predictions to support people, not to label them. To see how survey results by department, manager and statement can feed this work, explore TeamCheck's Employee Experience solution.
References
We cite sources so you can read further. Links open external websites.
- An evidence-based review of HR Analytics (Marler & Boudreau). The International Journal of Human Resource Management, 2017. doi.org/10.1080/09585192.2016.1244699 (opens in a new tab)
- People analytics: A scoping review of conceptual boundaries and value propositions (Tursunbayeva, Di Lauro & Pagliari). International Journal of Information Management, 2018. doi.org/10.1016/j.ijinfomgt.2018.08.002 (opens in a new tab)
- ISO 30414:2025 Human resource management: Requirements and recommendations for human capital reporting and disclosure. International Organization for Standardization (ISO/TC 260), 2025. committee.iso.org/sites/tc260/home/news/content-left-area/news-and-updates/iso-30414-2025-strengthening-hum.html (opens in a new tab)
- Personal Data Protection Law (National Data Governance Platform). Saudi Data and Artificial Intelligence Authority (SDAIA), 2021. dgp.sdaia.gov.sa/wps/portal/pdp/home (opens in a new tab)
Written by
TeamCheck Editorial Team
TeamCheck is an HR talent intelligence platform. Articles are researched and written by the TeamCheck editorial team.