Why are employees entering wrong data into their HRIS?

HR data is at the heart of a company’s management, but their reliability largely depends on the ability of employees to complete their personal information effectively, according to 63% of companies (2024 Benchmark by Le Cercle SIRH et Digital RH).

However, according to the 2024 Barometer of HRIS adoption in organizations by Shortways, the average score for HRIS adoption by their users is 3.5/5. This is still an insufficient score, considering that 78% of the professionals surveyed believe that poor HRIS adoption has an impact on data quality.

When the Human Resources Information System (HRIS) is not fully adopted, errors increase significantly: wrong recruitment dates, errors in payroll calculations, or misunderstandings on absence management or performance management forms. This has an impact on the other business processes that depend on the data: poor indicators = poor analysis and decision-making.

This article explores 5 reasons why your employees enter wrong data into their HRIS, and suggests ways to improve the situation 👇

Insufficient training and support
Poorly optimised HRIS ergonomics
Poorly defined HR processes
Resistance to change and lack of motivation
Lack of automated controls

1. Insufficient training and support

One of the major causes of incomplete or incorrect data in the HRIS is the lack of adequate training. Without constant support or reminders, data are often badly updated or incomplete. As a result, it’s not uncommon to find errors or incoherencies in the information recorded, which can be detrimental to the entire HR process.

In fact, when employees do not fully understand how to use the system or are not properly trained in the processes they need to follow, they are more likely to make mistakes. Data entry may seem easy, but it requires a good understanding of the expectations and rules specific to each field to be completed.

Furthermore, the support provided to users in the use of the HRIS should not be limited to the initial deployment of the tool. Very often, employees do not enter data correctly because they do not receive support when they encounter difficulties or when they have specific questions once they are in the RUN phase. In the absence of accessible and reactive support, they may circumvent problems by entering approximate information or relying on non-compliant practices.

💡 Tip: Implement continuous training tailored to user profiles, like micro-training modules integrated into the HRIS, to support users and remind them of good practices in real time.

2. Poorly optimised HRIS ergonomics

The design and the ergonomy of the HRIS play a crucial role in the accuracy of data entry. If the user interface is too complex, poorly thought-out or lacking in clarity, employees risk of misunderstanding where and how to enter data. Moreover, overly complicated navigation can waste time and increase errors, especially if employees are under pressure to complete their tasks quickly.

The ergonomics of the HRIS must be designed to offer a fluid navigation, enabling users to quickly find the information they need and easily understand the fields they need to fill in.

💡 Tip: Go for clean, user-friendly interfaces. Use automated input tools or drop-down menus that limit human error. Finally, if your tool is complex, make it easier to understand the processes by guiding users through the screens.

3. Poorly defined HR processes

Data entry errors are often the result of HR processes that are poorly defined or too complex for employees. When instructions are unclear or vary from one department to another, users risk to entering incorrect information simply because they don’t understand what is expected.

In companies where processes are not harmonised, each department may follow its own practices, leading to data incoherence. For example, data relating to employee hours worked and absences is validated by managers and is essential for payroll management. If this information is inaccurate or incoherent, payroll errors can cause significant frustration among employees, impacting on the social climate and requiring time-consuming rectifications.

💡 Tip: Create data entry guides accessible from the HRIS, presenting harmonised rules and instructions, explained in a simple way according to the users’ role. This will give them autonomy, raise awareness and help to ensure consistent and compliant data entry throughout the organisation.

4. Resistance to change and lack of motivation

Some employees may resist the move to digitisation, especially if they are familiar with more traditional, on paper, executing processes in a different way. Now everything must be rethought, and this upsets their habits. This reticence to change can manifest itself in a lack of motivation to enter data properly, or even in a minimal use of the HRIS, leading to errors or missing information.

Users who do not see the value of the tool or who are not convinced of its value to them personally are less inclined to make a conscious effort to ensure that their entries are correct. Resistance to change, even if it is sometimes discrete, has a direct impact on the quality of data collected.

We talk about it in our White Paper on this topic

💡 Tip : Explain in advance of any change the impact and advantages that this change will have for users, in order to remove as much resistance as possible, reassure users and get them on board in adopting these changes.

5. Lack of automated controls

Finally, another factor that contributes to incorrect data entry is the lack of automated controls in the HRIS. If the system does not have automatic checks to report inconsistencies or errors during data entry, these errors can go unnoticed until they cause malfunctions in HR processes.

Automated controls allow information to be validated in real time, ensuring that formats respect predefined norms or that required fields are filled in correctly (date format, amount format, etc.). Without these checks, users can make errors which, once propagated in the system, require manual, time-consuming and costly corrections.

💡 Tip: Activate automated controls in your HRIS to check data consistency and accuracy in real time.

Conclusion

Poor data entry in an HRIS is often the result of a number of interconnected factors: a lack of training, poor ergonomics, resistance to change or the absence of automated controls. To improve data quality, it’s essential to support your employees throughout their use of the HRIS, to train them regularly, and provide them with suitable tools that limit the risk of errors.

👉 Data quality is often hindered by several challenges. Learn more in this article: 3 obstacles to good data quality in HRIS.

👉 To find out more about solutions, discover our 8 secrets for ensuring that your employees enter good data.