Bekaert follows the principle of “data quality by design,” meaning that data quality is built into processes from the very beginning. This is achieved through three key principles:

• Relevance first: Only collect data that is genuinely needed for analysis. For example, information such as the number of children employees have is not stored centrally because it does not contribute to HR analytics.
• Smart field validation rules: Optional fields are avoided wherever possible and replaced with mandatory fields or logical validation rules to prevent errors.
• Make data visible: Bekaert chooses to display data at all times, even when it is incomplete or inaccurate. This encourages users to take action more quickly to improve data quality. A concrete example involved bonus calculations based on HR data. Employees were initially asked to review the underlying data to ensure bonuses could be calculated correctly, but little action was taken. Once bonus outcomes became visible, employees were surprised by some of the results. These reactions immediately triggered action, leading to direct improvements in data quality.

In addition, Jorn emphasized that data quality is an ongoing process. It is not a one-time exercise, but a continuous area of focus in which it is essential to understand which data has an impact on analyses and business outcomes.

Bekaert uses a combination of systems to make HR data accessible and actionable, tailored to the needs of the organization:

• SuccessFactors: The central database where all core data is collected, including employee, performance, and engagement data.
• Visier: A specialized people analytics platform that Bekaert uses to build dashboards and reports. Among other things, it supports annual reporting. Visier combines data from SuccessFactors with information from finance and engagement systems, creating a more complete picture.

AI functionality within Visier: One of Visier’s strengths is its built-in AI functionality, which helps identify trends and supports organizations in achieving their goals. For example, when aiming to increase the proportion of female employees to 30%, the AI can recommend which actions and drivers are most important to help achieve that objective.

Jorn also emphasized that effective reporting is about more than just numbers. Storytelling plays a crucial role:

• Focus on the question behind the question: Rather than simply reporting what someone asks to see, Bekaert looks for the underlying need. For example, behind a request for a headcount report was actually the need to gain insight into how workforce size evolved throughout the year. By first exploring the underlying question, they avoided creating a report that merely displayed headcount figures.
• Reporting for different audiences: HR teams often have different reporting needs than business teams. That is why Bekaert combines HR data with other types of data, such as financial metrics. This integrated approach makes the value of HR data visible to other departments and helps foster collaboration. In addition, combining different data sources enables broader, multidisciplinary analyses, for example to demonstrate the ROI of HR programmes.

The approach taken by Bekaert’s HR Technology team demonstrates that successful HR analytics starts with strong data quality and is strengthened by purposeful reporting. By leveraging smart tools and placing storytelling at the heart of their approach, Bekaert turns data into a strategic asset for better HR decision-making. In addition, Bekaert emphasizes the importance of thinking about the desired end report from the very beginning of a project and aligning the choice of tools accordingly.

Take inspiration from Bekaert’s approach and discover how your organization can also unlock value from HR data. Would you like to get started with analyzing your own HR data? Contact us. Or are you looking for even more inspiration on HR analytics? Then be sure to subscribe to our bi-monthly newsletter for tips and trends in HR analytics.