You can combine employee data from different systems by creating a central layer that connects HR systems, engagement surveys, and operational data through shared keys, such as employee ID or team. This gives you a complete picture of the employee experience instead of isolated fragments. In this article, we answer the most frequently asked questions about HR data integration, from which systems matter to the most common mistakes.<\/p>\n
Relevant employee data lives in multiple systems at once. The most common sources are the HR information system (HRM\/HRIS), the payroll system, the absence management system, the learning and development platform (LMS), the employee satisfaction survey, and any pulse surveys. No single system contains the full picture.<\/p>\n
In practice, we see that organizations quickly use five or more systems, each capturing a part of reality:<\/p>\n
The challenge is not that this data is missing. The challenge is that it is scattered and rarely connected.<\/p>\n
One system shows you that<\/em> something is happening, but not why<\/em>. A high absenteeism rate in your HR system tells you nothing about the workload or team climate underlying it. Only when you place engagement scores, absence data, and team feedback side by side does an explanation emerge.<\/p>\n Another common problem: organizations measure engagement through an annual employee survey, but never link those scores back to turnover or performance indicators. The result is that HR directors and managers work with loose puzzle pieces. They see a low score on employee happiness, but don't know which teams are affected, what the cause is, or which action would have the most impact.<\/p>\n Isolated data also leads to slow decision-making. If your engagement data is only available after three months and absence data comes in weekly, those two sources are difficult to compare. Timeliness and granularity must align for data to be actionable.<\/p>\n You connect employee data by choosing one common key \u2014 usually the employee ID \u2014 and linking all systems to it. You then choose a central location where the combined data comes together: a data warehouse, a BI tool, or an integrated XM platform. After that, you define which combinations of data you want to analyze.<\/p>\n In practice, this involves a number of steps:<\/p>\n Privacy is not a side issue here. Once you combine data at the individual employee level, you must handle GDPR carefully. Set minimum group sizes for reporting so that individual employees cannot be identified from combined scores.<\/p>\n The most valuable insights emerge when you combine engagement data with absence, turnover, and operational performance. These combinations reveal what isolated scores conceal: which teams are at risk, where the real cause of attrition lies, and which factors drive employee happiness.<\/p>\n Teams with low engagement scores often show higher absence frequencies. By placing these two data sources side by side, you can identify early which teams need extra attention. This makes the shift from reactive to preventive HR policy concrete.<\/p>\n The eNPS, the employee Net Promoter Score, measures how likely employees are to recommend their employer. Combine that score with attrition data, and you quickly see whether low recommendation rates precede actual departures. This gives HR the opportunity to engage in conversations earlier.<\/p>\nHow do you connect employee data from different systems?<\/h2>\n
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Which data combinations yield the most insights?<\/h2>\n
Engagement and absence<\/h3>\n
eNPS and turnover<\/h3>\n
Drivers and development data<\/h3>\n