Predictive analytics: How to overcome the challenges Predictive analytics: How to overcome the challenges

Predictive analytics: How to overcome the challenges
27 Jun 2018

Imagine how useful it would be if you could accurately anticipate the risks and opportunities that lie around the corner. You could predict which employees were most likely to leave your team in the next six months, for example, or understand how planned business changes might affect employee engagement.

Succeeding in business boils down to making the right decisions at the right time. The more data you have to work with, the easier this process becomes, and technology such as predictive analytics can be of great value here.

Although it might sound like a recent invention, predictive analytics software has actually been around for years. It was, in fact, pioneered during the Second World War when the UK Government used it to decode German messages

One of its most common uses today, on the other hand, is undertaking credit checks. To generate an individual credit score, historical data is used to determine how likely it is that you will default on a purchase in future. 

But predictive analytics tools have become the subject of renewed attention recently due to a huge rise in the volume of data at our disposal. Areas of both our business and personal lives are now being recorded in data terms as never before. When combined with technology such as machine learning and robotics, the prospect of processing and making sense of such information becomes a reality. 

As a result, predictive analytics software is now being deployed across a whole range of industries to do everything from spotting cases of fraud in the financial services sector and undertaking risk-profiling in the insurance space to performing behavioural targeting in a marketing context. But now, it is poised to play a pivotal role in payroll too.

How could predictive analytics be used in payroll?

When attempting to gain strategic business insights, payroll data is all too often overlooked. But in reality, it holds a potential goldmine of information in areas such as employee reward, overtime and absence trends and general people costs – which tend to the single largest business expense. Indeed, as payroll departments act as a bridge between finance and people, its systems store a broader range of personal data than traditional HR systems, providing even greater scope for predictive modelling.

This means that by using the right analytics tools and pulling in data from other sources, employers have the opportunity to use their payroll data as the basis for anticipating future employee requirements more effectively. Such information can be employed to enhance recruitment, retention and reward strategies as well as improve financial and cash-flow forecasting.

Get it right and you are looking at being able to cut unnecessary costs, boost efficiency and encourage better performing and engaged employees.

The challenges

Despite the potential benefits that predictive analytics tools offer, there are also a number of challenges that must be overcome before the dream can become a reality. 

  1. Break down data siloes

To take full advantage, it is necessary to access as much data as possible in order to build a comprehensive picture of any trends and patterns that are impacting the business. Payroll data is one part of the picture here but it also needs to be considered alongside wider sources of information, including HR, too. If your data is held in a number of separate systems, it will be necessary to integrate or aggregate it into one place to make it easier to interrogate.

  1. Ensure your data can be trusted

For predictive analytics to be truly valuable, employers need accurate and comprehensive data without empty column fields and invalid entries. The good news is that payroll data tends to be some of the most complete and well-curated in an organisation, so pay information is often a really good place to start.

But the quality of other data sources may not be as good. So proceed with caution and recognise that it may be necessary to clean information and instigate change in the way it is managed and maintained as you go forward.

  1. Be realistic

Today’s tools can analyse data and see patterns that would otherwise be missed by humans – but they will never replace human experience and intuition. So discover how to make the most of the software by learning how to filter and interpret the insights they generate.

  1. Further develop your skills

It is important to have people on your team who know how to exploit the technology’s capabilities effectively. Payroll departments are often well placed here as they are likely to include data-savvy people who are, at the very least, used to analysing information in Excel. This means that payroll professionals could potentially have a great opportunity to broaden out their roles and take the lead over their HR colleagues in this domain.

  1. Avoid ‘technology for technology’s sake’

A big challenge for many organisations is defining a clear purpose behind why they would like to implement predictive analytics tools and understanding what they want to achieve. Bringing in new technology, skills and processes is a significant long-term investment so it is critical to clarify the rationale and identify the key expected outcomes. Doing so also makes success more likely.

Conclusion

Introducing predictive analytics tools to the payroll function will involve staff learning new skills and introducing a wider change management initiative. While there will undoubtedly be hurdles to overcome, such a move could generate both competitive advantage for the business as a whole and an important boost to the status and role of payroll within the organisation.

 David Woodward

David Woodward is vice president of product development for Europe, the Middle East and Africa at ADP. Prior to his current role, he worked for a number of international organisations within the human capital management space, most recently as chief product officer at SD Worx UK.

 

Imagine how useful it would be if you could accurately anticipate the risks and opportunities that lie around the corner. You could predict which employees were most likely to leave your team in the next six months, for example, or understand how planned business changes might affect employee engagement.

Succeeding in business boils down to making the right decisions at the right time. The more data you have to work with, the easier this process becomes, and technology such as predictive analytics can be of great value here.

Although it might sound like a recent invention, predictive analytics software has actually been around for years. It was, in fact, pioneered during the Second World War when the UK Government used it to decode German messages

One of its most common uses today, on the other hand, is undertaking credit checks. To generate an individual credit score, historical data is used to determine how likely it is that you will default on a purchase in future. 

But predictive analytics tools have become the subject of renewed attention recently due to a huge rise in the volume of data at our disposal. Areas of both our business and personal lives are now being recorded in data terms as never before. When combined with technology such as machine learning and robotics, the prospect of processing and making sense of such information becomes a reality. 

As a result, predictive analytics software is now being deployed across a whole range of industries to do everything from spotting cases of fraud in the financial services sector and undertaking risk-profiling in the insurance space to performing behavioural targeting in a marketing context. But now, it is poised to play a pivotal role in payroll too.

How could predictive analytics be used in payroll?

When attempting to gain strategic business insights, payroll data is all too often overlooked. But in reality, it holds a potential goldmine of information in areas such as employee reward, overtime and absence trends and general people costs – which tend to the single largest business expense. Indeed, as payroll departments act as a bridge between finance and people, its systems store a broader range of personal data than traditional HR systems, providing even greater scope for predictive modelling.

This means that by using the right analytics tools and pulling in data from other sources, employers have the opportunity to use their payroll data as the basis for anticipating future employee requirements more effectively. Such information can be employed to enhance recruitment, retention and reward strategies as well as improve financial and cash-flow forecasting.

Get it right and you are looking at being able to cut unnecessary costs, boost efficiency and encourage better performing and engaged employees.

The challenges

Despite the potential benefits that predictive analytics tools offer, there are also a number of challenges that must be overcome before the dream can become a reality. 

  1. Break down data siloes

To take full advantage, it is necessary to access as much data as possible in order to build a comprehensive picture of any trends and patterns that are impacting the business. Payroll data is one part of the picture here but it also needs to be considered alongside wider sources of information, including HR, too. If your data is held in a number of separate systems, it will be necessary to integrate or aggregate it into one place to make it easier to interrogate.

  1. Ensure your data can be trusted

For predictive analytics to be truly valuable, employers need accurate and comprehensive data without empty column fields and invalid entries. The good news is that payroll data tends to be some of the most complete and well-curated in an organisation, so pay information is often a really good place to start.

But the quality of other data sources may not be as good. So proceed with caution and recognise that it may be necessary to clean information and instigate change in the way it is managed and maintained as you go forward.

  1. Be realistic

Today’s tools can analyse data and see patterns that would otherwise be missed by humans – but they will never replace human experience and intuition. So discover how to make the most of the software by learning how to filter and interpret the insights they generate.

  1. Further develop your skills

It is important to have people on your team who know how to exploit the technology’s capabilities effectively. Payroll departments are often well placed here as they are likely to include data-savvy people who are, at the very least, used to analysing information in Excel. This means that payroll professionals could potentially have a great opportunity to broaden out their roles and take the lead over their HR colleagues in this domain.

  1. Avoid ‘technology for technology’s sake’

A big challenge for many organisations is defining a clear purpose behind why they would like to implement predictive analytics tools and understanding what they want to achieve. Bringing in new technology, skills and processes is a significant long-term investment so it is critical to clarify the rationale and identify the key expected outcomes. Doing so also makes success more likely.

Conclusion

Introducing predictive analytics tools to the payroll function will involve staff learning new skills and introducing a wider change management initiative. While there will undoubtedly be hurdles to overcome, such a move could generate both competitive advantage for the business as a whole and an important boost to the status and role of payroll within the organisation.

 David Woodward

David Woodward is vice president of product development for Europe, the Middle East and Africa at ADP. Prior to his current role, he worked for a number of international organisations within the human capital management space, most recently as chief product officer at SD Worx UK.