First Capitol’s Focus Is Workforce Data
Why care about workforce data? Because it is becoming an ever-increasing part of operating costs. According to the U.S. Department of Labor:
Employer costs for employee compensation now average more than $36 per hour worked.
Wages and salaries averaged more than $24 per hour worked and accounted for more than 68% of these costs.
Benefit costs accounted for almost 32% of these costs.
Total employer compensation costs for private industry workers averaged more than $34 per hour worked.
Total employer compensation costs for state and local government workers is more than $49 per hour worked.
At the same time, regulatory compliance is becoming a growing risk for employers. The impact of using inaccurate workforce data for complying with regulations can be significant.
U.S. companies spend $2 trillion annually on government regulations.
The average first-year cost for a new business to comply with regulations is $83,019. That does not factor in the significant penalties that can be assessed for failing to comply with federal, state and local regulations.
Many times, compliance failures are the result of organizations using inaccurate data. Sometimes, they can be the result of a flawed approach to data analysis. Other times, they can be caused by not having people with deep regulatory knowledge to help apply the data correctly in addressing regulatory requirements.
The largest institutional investors in the world understand the importance of workforce data and metrics, which is why they petitioned the U.S. Securities and Exchange Commission to require publicly traded businesses to provide dramatically increased transparency into their human capital management practices, viewing it as a significant part of a company’s performance and liability risk assessment.
Accurate Data is the Foundation
In order to make decisions using workforce data that your organization can trust, your data needs to be accurate. Using accurate data is the foundation for critical business decisions in today’s world. And the use of inaccurate data by U.S. businesses is having a massive impact.
On average, U.S. organizations believe 32% of their data is inaccurate, according to research by Experian Data Quality.
Only 38% of executives have a high-level of trust in their data.
91% of respondents believe revenue is affected by inaccurate data in terms of wasted resources, lost productivity, or wasted marketing and communications spend.
IBM has determined that inaccurate data cost the U.S. economy $3.1 trillion in 2016.
First Capitol’s Data Quality Management Process
The challenge of obtaining accurate workforce data starts with collecting that data from a mix of separate and often disjointed data bases: Payroll, Time and Attendance, Leave of Absence, Benefits Administration and automated HR systems. This is where First Capitol’s Data Quality Management process plays an important role.
First Capitol’s Data Quality Management process works to efficiently extract information from these disjointed databases, where it is consolidated, cleansed, and validated to assure its accuracy. From this Single Source of Truth, First Capitol provides its business and regulatory compliance services and insights to help businesses and governments to increase operating efficiencies, lower financial and regulatory risk, and improve bottom line results.
Triangle of Trustsm
The Triangle of Trustsm is the intersection of data, analytics and regulatory expertise necessary to move raw data to successful regulatory compliance. Success can only be achieved when all three segments of the Triangle are in alignment.
Workforce data, such as Payroll, Time and Attendance, Leave of Absence, Benefits Administration and general HR information, are frequently scattered across various databases and platforms. Processes must be in place to ensure that the data being used is accurate.
Once you have validated the accuracy of your data, it must be analyzed properly for use in addressing specific work purposes, such as regulatory compliance. Understanding how to interpret data so that it answers the requisite questions is critical. Organizations must have the right information and processes to correctly interpret data.
Given the severity of the financial risk of regulatory non-compliance, you want people with a deep knowledge of applicable regulations and how to correctly apply them to your data.
Our GIGO Score provides clients with an analysis of the accuracy of their payroll data.
The higher the GIGO Score, the more accurate the data. The lower the score, the dirtier the data.
The GIGO Score helps employers monitor data health and determine whether problem points are trending up or down from month to month. By alerting employers data anomalies and inconstancies, they can take steps to address these data accuracy issues in their databases to avoid inaccurate regulatory filings that can result in significant financial penalties.
If your data is dirty, First Capitol can undertake a program to provide immediate improvements to workforce data quality to ensure that organizations are using the most accurate information when using information to comply with federal and state regulations or to make important business decisions.
First Capitol determines your organization’s GIGO Score by reviewing payroll data fields, such as names, social security numbers, hire and termination, wages, and rates of pay.
The GIGO Score analysis identifies inconsistencies affecting data health, such as:
Inaccurate hire and termination dates for employees
Duplicate social security numbers and other IRS identifiers
Nonsensical data like improbable rates of pay
Unrealistic hours of service worked by individual employees
Data values that don’t fit data formats
Obtaining a monthly GIGO Score can identify the pain points that are causing data quality fluctuations so that they can be addressed.
As your GIGO Score improves each month, you will see how the quality of the workforce data your organization is using makes a difference in your regulatory compliance processes and helping you to avoid costly regulatory penalties.