Although data analytics software has been around for a while, adoption is not as widespread as you might think, particularly in payroll functions. About 60% of the market, for example, would like to do something constructive with the technology, but do not necessarily have the right tools, or employee records, in place to undertake meaningful data analysis.
A further 20% are not even considering going down this route, but the remaining 20% are leading-edge adopters. Most publicly-traded, multinational companies in areas such as security, technology, IT and finance use such tools extensively, for instance. As a result, they are able to answer key business questions such as:
- What services could we offer internal customers that they are not yet aware they need?
- How could we save our clients time?
- How could we cut the number of interactions our internal customers need to have with us in relation to each payroll run?
- How could we increase efficiency and improve the relationship with our clients?
So with this in mind, here are a couple of things to think about if considering going down this route:
- Conduct a gap analysis
The first thing to do is conduct a gap analysis. This means understanding where you are today and preparing to answer these questions:
- Of what does your payroll ecosystem consist?
- Who is processing what payroll, how and what data are you receiving?
- In what format do you receive your data?
- Consolidate and standardise
Secondly, it makes sense to consolidate and standardise your payroll function, which includes processes, applications, reporting tools, the language in which you do business, dates, dashboards and the like. It is usually possible to standardise about 80% of global payroll, with the remaining 20% consisting of local nuances in each territory.
Doing so will provide you with more control, especially if you work with multiple vendors. Consolidation and standardisation are key to creating a workable, manageable network.
What next?
Over the next two or three years, robotics and artificial intelligence will become more and more important, especially for those companies that are already using analytics tools. The idea is that the more automated your data lifecycle is, the more likely it is that your data will clean, which means, in turn, that your analytic output should be more accurate too.
For example, when reviewing a report that indicates how much it costs to process payroll in a particular country, it is important to think about how the information was initially entered into the system. If it was manually typed in locally, someone could easily make a typo, for instance.
As a result, reducing human ‘touchpoints’ to ensure your base data is 100% accurate can make all the difference in the world.
Lee-ann Kilroy is solutions support director at global payroll services provider, iiPay. She joined the company from a UK top 40 accountancy firm and has held key leadership roles in its operations, implementation and project management teams. Lee-ann works alongside the company’s enterprise team to provide custom solutions for all accounts.
Although data analytics software has been around for a while, adoption is not as widespread as you might think, particularly in payroll functions. About 60% of the market, for example, would like to do something constructive with the technology, but do not necessarily have the right tools, or employee records, in place to undertake meaningful data analysis.
A further 20% are not even considering going down this route, but the remaining 20% are leading-edge adopters. Most publicly-traded, multinational companies in areas such as security, technology, IT and finance use such tools extensively, for instance. As a result, they are able to answer key business questions such as:
- What services could we offer internal customers that they are not yet aware they need?
- How could we save our clients time?
- How could we cut the number of interactions our internal customers need to have with us in relation to each payroll run?
- How could we increase efficiency and improve the relationship with our clients?
So with this in mind, here are a couple of things to think about if considering going down this route:
- Conduct a gap analysis
The first thing to do is conduct a gap analysis. This means understanding where you are today and preparing to answer these questions:
- Of what does your payroll ecosystem consist?
- Who is processing what payroll, how and what data are you receiving?
- In what format do you receive your data?
- Consolidate and standardise
Secondly, it makes sense to consolidate and standardise your payroll function, which includes processes, applications, reporting tools, the language in which you do business, dates, dashboards and the like. It is usually possible to standardise about 80% of global payroll, with the remaining 20% consisting of local nuances in each territory.
Doing so will provide you with more control, especially if you work with multiple vendors. Consolidation and standardisation are key to creating a workable, manageable network.
What next?
Over the next two or three years, robotics and artificial intelligence will become more and more important, especially for those companies that are already using analytics tools. The idea is that the more automated your data lifecycle is, the more likely it is that your data will clean, which means, in turn, that your analytic output should be more accurate too.
For example, when reviewing a report that indicates how much it costs to process payroll in a particular country, it is important to think about how the information was initially entered into the system. If it was manually typed in locally, someone could easily make a typo, for instance.
As a result, reducing human ‘touchpoints’ to ensure your base data is 100% accurate can make all the difference in the world.
Lee-ann Kilroy is solutions support director at global payroll services provider, iiPay. She joined the company from a UK top 40 accountancy firm and has held key leadership roles in its operations, implementation and project management teams. Lee-ann works alongside the company’s enterprise team to provide custom solutions for all accounts.
