HRMS Integration: Getting All Your HR Data in One Place

Your HRMS is a rich source of employee data and many systems provide in-depth reporting capabilities. To maximise the return on this functionality, there is a need to bring together data from other systems through HRMS integration. Integration of your business systems provides maximum visibility to all relevant data: HR, payroll, timekeeping, expenses, financials, etc. This visibility then provides decision makers with the HR data that they need to make strategic changes to the way the HR department is run. But how do we go about integrating our HR data systems in the first place?

Many companies have a reporting or data warehouse, where key data elements from various systems are sent, and then combined into reports and other toolsets, such as manager dashboards. A manager dashboard may include data from many different sources fuelled by HRMS integration. For example, name would come from the core HRMS, hours worked or absenteeism from a timekeeping system, employee costs from payroll, and overall roll-ups of costing data from a finance or general ledger system. Having one access point for all of this data is the most efficient solution for a manager.

Capabilities & Complexities

HR analytics that brings together key data elements can be a rich source of decision making. This analytics functionality may produce a simple headcount report, or may be used for heavy and targeted analysis: compensation data could be combined with sales to ensure that your top sales representatives are receiving appropriate commissions.

It’s important to design the data that will be received from the various systems based on the expected analytics that will be produced

Combining different sources of data through HRMS integration is not without its complexities, however. There are technical considerations such as database sizing, in particular if you are bringing in payroll data which can often involve staggering amounts of data per employee. It’s important to design the data that will be received from the various systems based on the expected analytics that will be produced, rather than importing all possible fields from every system.

Defining which system is the ‘owner’ of a data element can also prove difficult. At many organizations, the ‘HR system headcount’ may be different from the ‘Finance system headcount’ due to differing requirements: while HR counts one employee in a seat, Finance may only count a .5 or .25 for an unpaid intern, for example. Where data variances such as these exist, it’s important that everyone is clear on the definitions, especially if this data will be distributed through the organization.