Using HR data to strengthen employee engagement

Employee engagement remains a major challenge for organisations. According to Gallup’s State of the Global Workplace 2026 report, only 20% of employees worldwide were engaged at work in 2025. Behind this figure lie very tangible issues: a lack of purpose, insufficient recognition, limited development opportunities, unsuitable tools or a weakened relationship with management.

HR data can help organisations understand these situations and make better-informed decisions. It can reveal early warning signs, track changes in the employee experience and measure the effectiveness of the actions taken.

However, HR data is not a magic solution. Simply accumulating information is not enough and can even be counterproductive when data is poorly defined, unreliable or collected without a clear purpose. To create value, it must be relevant, secure, properly governed and translated into concrete action.

What is HR data?

HR data, often associated with People Analytics, covers the information used to understand and manage the different stages of the employee journey: recruitment, onboarding, learning, mobility, performance, compensation, engagement and departure from the organisation.

It can come from a range of sources:

• The human resources information system or HRIS

• Recruitment and applicant tracking tools

• Career conversations and performance reviews

• Learning platforms

• Employee engagement surveys and internal questionnaires

• Career development and internal mobility records

• Absence, retention and turnover indicators

• Qualitative feedback from employees and managers

This information only becomes valuable when it helps answer a clearly defined question. Why do new employees leave during their first year? Do some teams face greater barriers to accessing learning opportunities? Do managers have the resources they need to support their teams?

The right approach is therefore to start with a business issue and then select only the data required to investigate it. This avoids multiplying dashboards without a clear purpose and focuses efforts on decisions that can genuinely make a difference.

Can HR data measure employee engagement?

Engagement reflects the psychological connection employees have with their work, team and organisation. It is influenced by factors such as purpose, management quality, recognition, autonomy, working conditions and development opportunities.

No single indicator can measure this reality perfectly. Low turnover does not necessarily mean that employees are engaged. Conversely, a temporary increase in departures may result from the economic climate, an organisational change or developments in the labour market.

To build a more accurate picture, organisations need to combine several types of data:

• Self-reported indicators from engagement or pulse surveys;

• Aggregated behavioural indicators, such as participation in learning programmes or internal mobility;

• HR indicators, such as retention, absence and voluntary turnover;

• Qualitative information from interviews, focus groups and management conversations.

Combining these perspectives can reveal meaningful trends. It never replaces dialogue with employees or an understanding of the context in which the data was produced.

Which HR indicators should organisations track?

The choice of indicators should depend on the objective. A limited number of understandable and actionable measures is more useful than a highly complex dashboard that nobody uses.

Some indicators also require careful interpretation. Absence rates, for example, may signal an organisational issue, but do not explain its cause. The eNPS provides a simple measure of employees’ willingness to recommend their organisation, but it cannot replace a structured survey of the factors that drive engagement.

Consistent definitions are equally important. If turnover or internal mobility is calculated differently across subsidiaries, periods or tools, comparisons become unreliable. A shared data dictionary should specify the definition, source, calculation method, update frequency and owner of each indicator.

How can HR data help improve engagement?

Listen to employees more effectively

Annual surveys remain useful for monitoring long-term trends, but organisations can supplement them with shorter surveys at specific moments, such as after onboarding, a learning programme, an internal move or a business transformation.

Frequency is not an objective in itself. Repeatedly asking for feedback without sharing the results or implementing improvements can create survey fatigue and damage trust. Every survey should therefore be followed by clear communication: what was observed, what will be addressed and what cannot be changed immediately.

Give managers actionable information

Managers play a central role in the employee experience. Data can help them identify differences between teams, prepare for career conversations and monitor development actions.

However, a dashboard should not become an individual scoring or surveillance tool. Results are best presented at a sufficiently aggregated level and accompanied by context that helps explain possible causes. The goal is to open a conversation, not to generate an automatic judgement.

Personalise learning and development

By combining existing skills, career aspirations and the organisation’s future needs, HR teams can design more relevant development pathways. This approach also makes it easier to identify bridges between roles and encourage internal mobility.

Effectiveness cannot be measured solely by the number of courses completed. Organisations should also examine whether employees acquired the intended skills, applied them in their work and contributed more effectively to professional objectives.

Improve recruitment and onboarding

HR data can show where the main recruitment difficulties occur: poorly matched applications, excessive delays, candidate withdrawals, a gap between the advertised role and the reality, or high early attrition.

It can also help compare the performance of different recruitment channels and improve the candidate experience. The criteria used must nevertheless remain objective, relevant and directly connected to the skills required. The recruitment guide published by the CNIL, the French data protection authority, explains that candidates’ personal data must be processed in accordance with the GDPR throughout the recruitment process.

Anticipate future skills requirements

Analysing roles, skills and career paths can reveal gaps between the resources currently available and the organisation’s future needs. It helps organisations choose between external recruitment, learning, internal mobility and the automation of selected tasks.

This process must remain dynamic. Skills frameworks that are overly detailed or rarely updated quickly become obsolete. It is more effective to start with critical skills, establish clear update rules and involve employees and managers in validating them.

From measurement to action: building a continuous improvement loop

The value of HR data lies not in the dashboard itself, but in the decisions it enables. An effective process can follow six steps:

1. Define a precise business question. For example: understand the increase in voluntary departures among recently hired employees.

2. Select the relevant indicators and sources. Departure data, tenure, team, onboarding pathway and qualitative feedback.

3. Check data quality and comparability. Shared definitions, sufficiently complete records and a relevant analysis period.

4. Involve the right people in interpreting the results. HR, managers, employees, the Data Protection Officer and employee representatives, depending on the project.

5. Implement a targeted action. Improve onboarding, train managers or clarify development opportunities.

6. Measure results and adjust. Compare indicators before and after the action without confusing correlation with causation.

This approach enables organisations to test hypotheses progressively and avoid large-scale programmes whose impact is difficult to measure.

Protecting HR data to preserve trust

Much of the information processed by HR is personal data. Some data may be particularly sensitive or indirectly reveal information about health, ethnic origin, political opinions, trade union activity or private life.

Its use must therefore be governed by a clear framework:

• Define an explicit purpose for every processing activity;

• Identify the appropriate legal basis with the relevant specialists;

• Collect only the data that is necessary;

• Inform candidates and employees clearly;

• Restrict access according to roles and responsibilities;

• Protect data during storage, transfer and analysis;

• Define appropriate retention periods;

• Enable people to exercise their rights;

• Assess risks and carry out a data protection impact assessment where required.

In France, the CNIL published a new reference framework for HR data retention periods in April 2026. It also states that information collected during a performance review must be directly relevant and necessary to the employee’s role.

In Luxembourg, organisations can use the resources provided by the National Commission for Data Protection to structure their GDPR accountability and compliance processes.

For engagement surveys, aggregating results, defining a minimum number of respondents per group and separating responses from identity records can reduce re-identification risks. Pseudonymisation can strengthen protection, but does not automatically turn personal data into anonymous information.

Trust is a prerequisite for success. If employees fear that they can be identified or assessed without their knowledge, they may be less willing to answer honestly. This affects both data quality and the relationship between the organisation and its workforce.

What role should artificial intelligence play in HR data?

Artificial intelligence can now support many HR processes: CV analysis, matching people’s skills with roles, learning recommendations, summarising open-ended responses and detecting trends in internal surveys.

These applications can accelerate selected tasks and enrich analysis. They also create risks: historical bias in data, discriminatory criteria, limited explainability, decisions that are difficult to challenge or disproportionate monitoring.

An OECD study on algorithmic management, based on a survey of more than 6,000 companies in six countries, highlights the growing use of these tools as well as concerns about accountability and the ability to understand how they work. The International Labour Organization has also warned about the limits of an exclusively data-driven approach to human resource management.

The European AI Act also classifies certain systems used for recruitment, candidate selection and worker management as high-risk AI systems. Organisations therefore need to identify the systems concerned, document how they are used and maintain appropriate human oversight.

Before introducing an AI solution into an HR process, organisations should ask several questions:

• Which data feeds the system, and is it reliable?

• Can the outcome be explained to the people concerned?

• Have potential biases been identified and measured?

• Who remains accountable for the final decision?

• Can a person challenge the outcome or ask for a review?

• Does the provider reuse the data for other purposes?

• Are the security and confidentiality measures sufficient?

AI should remain a decision-support tool. It must not turn a probabilistic indicator into an assumed truth about a candidate or employee.

Making HR data a driver of trust and performance

HR data can help organisations recruit more effectively, develop skills, support managers and improve the employee experience. Its potential depends less on the volume of data collected than on its quality, governance and the organisation’s ability to act on the insights it provides.

A responsible approach requires balance: using data to understand work without reducing people to scores, personalising support without introducing permanent monitoring, and automating selected analyses while retaining informed human decision-making.

DEEP helps organisations establish effective data governance, from defining roles and responsibilities to improving quality, security and compliance. Our experts can also help structure your strategy through our Data Consulting services, turning data into sustainable, well-managed drivers of action.

Main sources used to write this arti

• Gallup, State of the Global Workplace 2026.
• Deloitte, 2026 Global Human Capital Trends.
• CNIL, Work and personal data (in French).
• CNIL, Reference framework for HR data retention periods, April 2026 (in French).
• CNIL, Recruitment guide (in French).
• European Commission, European regulatory framework for artificial intelligence.
• OECD, Algorithmic Management in the Workplace, 2025.
• International Labour Organization, AI in Human Resource Management: The Limits of

•