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Achieving growth ambitions and managing talent
in an increasingly competitive and complex environment



Finding the right balance between global and local demands.

Consolidating HR and Payroll resources into a central Shared Services function brings with it considerable cost savings, thanks to streamlined systems and processes. However, there is often a concern about the loss of local knowledge, made even more challenging when you consider that there are on average 20 thousand changes in payroll regulation globally each year. Hiring specialists in each market is not an option for most, with many companies opting to outsource to leverage an external partner’s local market expertise.


A single view of your employees can feel like an elusive goal.

Gaining a single view of employees can be challenging even when operating at a single market level but with company expansions across borders, there are a whole new set of challenges to contend with. If, for example, the company is achieving growth thanks to mergers and acquisitions, as a HR or Payroll professional, you will likely inherit their legacy systems too, leaving you with a patchwork of platforms to battle with.


" 89% of companies believe that having a complete view of their employees is “critical”, yet only 30% of them have that complete overview."

Source: ADP Research


Fragmentation of data & systems impacts more than just costs.

A single market may have several systems in place to manage the varying aspects of HR and Payroll, such as one for Time and Attendance and another for administering Payroll. Extrapolate this across markets and not only will operating costs be high but your ability to gain actionable insights from employee data will be limited. With the war for talent being central to any large business, being able to leverage employee data to better understand what is driving employee engagement or to provide staff a roadmap for promotion based on the average promotion timescales are vital. To achieve this requires greater accessibility and consistency in the data.


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