Payroll Analytics Builds Bonds Down Under

A 15-hour flight gives you a lot of time to think. That was my experience as I traveled from Salt Lake City to Sydney, Australia. Most people would be thinking about what they want to see, do, and experience during their first time in Australia. For me, it was an anxiety-filled flight.

I was traveling to the TAPS Payroll Conference hosted by PayrollOrg’s Australian sister organization, The Association of Payroll Specialist (TAPS). I have completed hundreds of live presentations, created a solid slide deck, and taught several classes on payroll. There was no reason for anxiety, except for the difference in the audiences who had previously attended my presentations.

Payroll requirements in Australia and the United States differ. And being unsure of how to deliver this presentation without Australian payroll knowledge only fueled my anxiety. However, my anxiety abated as the flight neared Sydney. I realized the topic of my presentation itself would bridge the differences.

After all, the topic was payroll analytics, a hot topic that organizations and payroll practitioners worldwide are paying closer attention to.

The Six Steps of Payroll Analytics

Payroll analytics is just one of many components of business analytics.

Businesses complete analytic processes with an organization-wide focus to help understand overall operations. Business leaders also understand the need to focus on results within individual departments, including compensation, benefits, finance, and payroll. Given the payroll department can handle up to 70% of an organization’s expenditures, payroll analytics is a critical component of complete business analytics.

Payroll analytics are accomplished by using the following six steps:

1. Define a business problem or area where greater insight into payroll data is needed.
2. Find the location of the payroll data needed to conduct the analysis and collect it.
3. Develop hypotheses (assumptions and theories) from the data related to the problem or area of focus.
4. Define and execute necessary tests to provide the insight needed for the problem or area of focus.
5. Analyze the test results to determine if the problem or issue can be defined through the analytics or if more tests are needed.
6. If further analysis or data testing is needed, refine the hypothesis, determine what additional data is needed, and retest or complete the necessary new testing.

The Role of Data in Analytics

Organizations worldwide have data that can be analyzed to help business operations. However, not knowing Australian payroll requirements made it difficult to create my initial presentation. What data could I use as examples if I did not know their data needs, reporting requirements, or payroll processes?

I decided to make this personal. With some variations, personally identifiable information is universal data. Sharing more about myself provided examples of important employee data points collected by organizations worldwide and showed we are linked through payroll analytics, even if our payroll requirements differ. The discussion could now move to understanding the many testing types and uses for payroll analytics.

The necessary data and tests to complete payroll analytics differ for each problem or area of focus. The hypotheses of these areas and the available data drive the testing that must be completed. For this reason, the testing of your organization’s payroll data will differ from other departments and organizations. The beauty of payroll analytics is that the six-step process remains the same for all analytics, making the process easy to remember and repeatable. It also provides an opportunity to create new data, gather other insights from the same analytical testing, and use analytical testing results from internal departments to aid in future analytical testing. While these advantages go beyond the initial insight gained from analytics, the analytics will only be as good as the data itself.

Good Data, Bad Data

Most of us have heard of the concept “garbage in, garbage out” (GIGO). The analytical process and results are impacted most by inaccurate and missing data. Good, clean data is a necessary component of the analytical process.

Bad data issues include inaccurate entries, transposed numbers, incorrect formatting (e.g., a system field uses seven characters, but only six are entered), data entered in the wrong field, and data missing from important fields. Organizations and payroll departments must understand the importance of good, clean data. Keep in mind that much of the payroll data is owned and maintained by other departments. Therefore, payroll relies on organization-wide accuracy to ensure accurate analytics.

Becoming a data-driven organization requires an organization- wide initiative to improve data and data accuracy after the initial clean-up. Data-driven organizations have greater faith in their data and analytics allowing them to make good, impactful decisions based on reliable facts. There is a growing need for analytical experts in organizations, but payroll professionals can become more impactful business partners by recognizing the importance of advanced payroll analytics.

Analytics and Payroll Processes

Payroll departments may feel it is impossible to move to more advanced analytics before establishing basic analytics. Do you know how much data analysis is already being completed by your department? This may not be easy to answer if there is a misunderstanding of the full scope of analytics.

I found many of the TAPS conference attendees at my session were unaware they were already performing payroll analytics. When asked about their proficiency with payroll analytics, most attendees felt they had limited experience. This sparked a discussion on the different types of payroll analytics. Most basic payroll analytics are part of our normal payroll processes, such as balancing and reconciliations. Let’s go back to the six-step process using an organization’s process of balancing and reconciling uploaded timecard data as in the following example:

1. Define the issue—To ensure that the payroll system is processing the uploaded timecard data correctly.
2. Collect the data—The source file from the timekeeping system, reports from the timekeeping system, and the output reports from the payroll system.
3. Develop the hypothesis—It is an assumption that if the input and output information match, then the timecard data upload process is accurate.
4. Test the hypothesis—Match the details from the timekeeping system with the output reports, including individual employee information (the entry totals and ensuring it was applied to the correct employees) and totals.
5. Analyze the test results—If all individual employee information and totals match, then our hypothesis that the timecard data upload process was accurate has been affirmed.
6. Refine as needed—Our hypothesis was validated and therefore does not need to be refined. However, we can use test information to develop further tests, such as overtime, wage expenses, and time reporting accuracy.

Consider using these steps on other payroll processes. You will see that you and your payroll department perform many more payroll analyses than originally believed. Session attendees confirmed that observation when speaking with them after the session. They recognized there was more to learn about payroll analytics and were eager to do so.

Learn More With PayrollOrg

PayrollOrg offers several classes on payroll analytics. Foundations of Payroll Analytics is designed to help individuals of all levels of analytic knowledge understand the background of analytics, basic analytics that can be performed on payroll data, the need for and best use of graphics, and presenting analytics as part of a business plan.

The second course in the payroll analytics education path is Intermediate Payroll Analytics. This class is designed for individuals with basic analytic and analytic process knowledge and can develop hypotheses and tests of payroll data beyond regular payroll run analysis. For payroll professionals looking to learn about developing key performance indicators (KPIs) to monitor and track performance, analytics to understand and improve accuracy, and details on leveraging payroll data to help improve business results, this course is for you.

Later this year, the newest course—Advanced Payroll Analytics—will be the third and final course in the payroll analytics education path. This course will be available for individuals with some experience in advanced analytics and who want to take payroll analytics (and their career) to the next level. This class will explore technological advancements that can improve efficiencies in completing analytic processes, assist in developing new data points and analytic tests, and how technology can be leveraged to help efficiency, accuracy, and improved customer service opportunities that can help turn written words into quantifiable data.

United by Analytics

Organizations use payroll analytics to learn from the past, help develop and update current processes, and understand what they need to know for future business success. This is not limited to organizations here in the United States.

I have first-hand knowledge of this as I had the pleasure of presenting Foundations of Payroll Analytics virtually to a group of Australian payroll practitioners in March 2024. A few of those attendees met up with me at the TAPS conference. One had to take a selfie with me as she met her first “payroll celebrity” from another country, while another brought two of her colleagues to get excited about attending future sessions of Foundations of Payroll Analytics.

“We really appreciate you sharing your knowledge and experience with our members,” said Jason Low, Head of TAPS. “We had some fantastic feedback during and after the event, and your contribution made a real impact.”

While all of these moments were special, the recent TAPS conference was successful for me as I connected with Australian payroll professionals and left them excited about payroll analytics. It was a great way to appreciate and understand that while payroll requirements may be different for each country we are in, we are all linked through the same universal need for payroll analytics.

Kevin A. Valuet, CPP, is a Director of Payroll Training for PayrollOrg.