Enabling data-driven payroll insights and monitoring


Unlock the value of payroll data to save costs, combat fraud


Regardless of the organization or industry, everyone has payroll. Unfortunately for many, the data-driven insights that can be discovered through payroll data remain untapped, just out of reach.


Payroll data has universal appeal for delivering strategic and operational improvement, but it’s seldom used to its full potential.


“The data is real, except very few leaders know how to leverage it,” said Joe Ranzau, a Managing Director of the National People and Organization practice at Grant Thornton LLP. “However, if you optimize it the right way, there’s tangible value that comes out of being able to harness this.”


Payroll administrators seldom have bandwidth to discover these strategic insights because they are always in a tight pay cycle, under pressure to get everyone paid correctly and on time. As a result, opportunities can be missed to reduce unnecessary spend and mitigate risks of underpayment, fraud and more.


Headshot of Joe Ranzau

“If you optimize [data] in the right way, there’s tangible value that comes out of being able to harness it.”

Joe Ranzau

Grant Thornton Managing Director, National People and Organization practice

When payroll administrators do see red flags, they are often forced to address them manually due to time and role constraints rather than creating an automated process to prevent problems in future pay cycles. Individuals outside the payroll function may struggle to understand payroll data because it is extremely detailed, often down to the minutes someone worked. To derive insights from the data, it needs to be transformed into bite-sized, digestible chunks that are meaningful to the business.


This transformation seldom occurs in the normal course of business processes. To compound the complexity, finance managers and system administrators often don’t have access to the granular data that can yield strategic insights. The result is that executives are left with less visibility into normal business transactions versus outliers or pervasive or problematic issues.


Headshot of Meredith Murphy

“The use of key risk indicators and actionable payroll dashboards has value across the business – from operations to management, compliance and audit.”

Meredith Murphy

Grant Thornton Principal, Risk Analytics Leader

As a result, the strategic benefit of payroll data can be stifled without a dedicated business analysis of the hundreds of earnings codes and potentially millions of individual transactions for which payroll is responsible.


In most organizations, the operations side of the business, not payroll, makes overtime decisions. Payroll, on the other hand, has responsibility for translating those hours into a pay statement in the most accurate, timely and compliant manner possible. The difference between roles and responsibilities can unintentionally result in inappropriate payroll costs, fraud schemes and ineffective controls.


Case study: Payroll indicators shed light on inflated payroll

Below is a representative example of the powerful insights derived from payroll data for a Grant Thornton manufacturing client:

  • Data finding: Less than 5% of hourly workers account for 30% of overtime spend.
  • Leadership hypothesis: Seasonal demands and extended overtime for certain key personnel caused the anomaly. This hypothesis was quickly disproven with data analytics.
  • Root cause: Tens of millions of dollars were being spent inappropriately. Mismanagement, fraud, system complexity and data availability all contributed to the problem.
  • Underlying problem: Due to the complexity and manual nature of the time capture process and a fragmented technology landscape, executives could not see the labor spend or execute operational control to manage it. Reporting based on the general ledger and forecasted hours was at too high a level to provide actionable insights.
  • Solution: Company executives were advised to incorporate real-time data analytics into their payroll system, with ongoing risk control monitoring for outliers. A roadmap for process and system harmonization helped get them started as they built payroll data analysis into their operations.
  • Leadership takeaway: It is common for variability to be explained away by business conditions (e.g., seasonality, one-time events). Using analytics, problematic trends can be proactively addressed and more accurate root causes can be identified.


Some client examples of value include instances when operational metrics (e.g., timeliness of response to customer requests) are tied to employee incentives that unintentionally result in excessive and inappropriate overtime being paid. “There’s a cascade of impacts,” Ranzau said.


These are the types of insights that payroll data can reveal, if leadership knows where to look. The problems that payroll and time data can unmask are largely industry-agnostic, although the nature of the issue — as well as the solution — is likely to vary based on company and event-specific circumstances.





What the data reveals


Although it’s almost inevitable that hidden efficiencies exist in payroll data, there’s a significant variation in the issues that might be uncovered. They include:

  • Cost optimization. Can costly overtime hours be reduced or eliminated by adjusting delivery timelines, shifting to another location, or even hiring new employees? Is one unit of the company more productive with fewer employees? Data can reveal these insights with a direct result of cost savings.
Headshot of Meredith Murphy

“Using data to identify and mitigate fraud risk has tremendous value to companies. Our clients with more mature data and analytic capabilities use continuous monitoring dashboards to proactively identify outliers, high-risk activity and fraud risk scenarios for executives and management.”

Meredith Murphy

Grant Thornton Principal, Risk Analytics Leader

  • Fraud. Data can show if certain employees or groups are reporting hours they don’t actually work, paying employees who have been fired or never existed (i.e., ghost employees), or even are claiming mileage for distances they haven’t driven. “Using data to identify and mitigate fraud risk has tremendous value to companies,” said Meredith Murphy, Principal and Risk Analytics Leader for Grant Thornton. “Our clients with more mature data and analytic capabilities use continuous monitoring dashboards to proactively identify outliers, high-risk activity and fraud risk scenarios for executives and management.” 
  • Employee burnout. An analysis might also demonstrate that people in one location of the company are working far more hours than people elsewhere. If that’s the case, employee burnout may lead to costly turnover problems that can be proactively managed, if known.
  • Health and safety issues. Health or safety hazards for employees and customers can arise when an individual works too many hours in certain professions and this issue isn’t promptly addressed. Furthermore, some companies integrate employee time data with safety incidents data and training compliance records to determine potential root causes to safety events.
  • Compliance shortcomings. Inadvertent breaches of federal, state, and local regulations can easily go undetected if a comprehensive review of payroll and time data is not undertaken.
  • Mismanagement. Managers who haven’t been trained properly or lack insight into the overall operations of the business may make decisions that seem right, but actually are misguided. These inefficient practices may go unnoticed without an analysis of payroll data. 



Methods for discovering insights


Getting to actionable business insights isn’t easy, but it can be done. Here are some of the tactics that can be used to harness payroll data for operational improvement:

  • Standardize operating model. Large enterprises often grow in bursts, sometimes through mergers and acquisitions, which can lead to disparate operating models. This leads to more complexity for payroll teams to manage and even more complex data that must be aggregated and transformed.
  • Master data management. Clearly defining the sources of truth and what each bit of data means allows everyone to be on the same page and establishes consistency.
  • Transform data and build a centralized data repository. Transform and align large and vast data sets to allow for enhanced and more simplified reporting. Payroll and time data often comes from many disparate systems. By centralizing data, the potential of analytics is unlocked.
  • Establish a payroll data center of excellence. Many companies have data analytics teams that have experience with a wide range of data, tools and technologies. This model provides more flexibility in how and when you overlay analytics and automation. The challenge is building payroll expertise into these teams. Without deep expertise in payroll, their ability to make an impact is limited.
  • Develop audit and compliance monitoring. Automated controls and monitoring can be used to identify and monitor key risks and controls. See Table A for example risks and analytics.



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