Data Science: Revolutionizing Labor Management

How data tools and AI will benefit construction companies

In the intricate world of construction, collective bargaining agreements (CBAs) serve as the compass guiding labor relations. These agreements, negotiated between unions and employers, dictate everything from pay rates to working conditions. However, the complexity of CBAs often makes payroll very complicated very quickly and can be daunting for both employees and employers to deal with.

Challenges of collective bargaining agreements

There are many factors at play: variable shift differentials, tiered overtime rates, jurisdictional rules, travel time, and more. You also need to track and ensure adequate breaks are taken, track paid time off accrual rates, and any benefit contributions. Add all of these in with variable hours worked each week and multiple jobs and it can be challenging for companies to accurately calculate compensation and adhere to union agreements. We’ve seen some companies using very manual processes every payroll cycle as well as handwritten notes to try and account for all the nuances and that creates uncertainty for both the company and employees.

The role of data science

This is where data science comes in—the intricacies of CBAs are the perfect puzzle for hungry data scientists to unravel. Armed with advanced algorithms, code, and computer programs, data scientists excel at handling complex data structures and automating intricate calculations. By leveraging data science, companies can streamline labor management processes, reduce errors, and ensure fair compensation for workers. Essentially, data scientists can take the manual processes and have them happen automatically. They can also take more data points to create more transparency in reporting and show patterns.

Additional challenge of prevailing wage

Amidst the complexities of CBAs, prevailing wage regulations further complicate matters. Prevailing wages, mandated for certain types of work on public projects, ensure fair compensation and standardize rates across regions. Understanding prevailing wage rates is crucial for companies operating in the public sector and further highlights the importance of data science in navigating regulatory landscapes.

Future data tools and innovations

The potential for innovation in this space is huge especially with AI.

Examples:

• Automated systems that parse CBAs, flag inconsistencies, and offer actionable insights for HR departments
• Data science upgrades that mitigate manual processes
• Predictive models could be implemented to analyze linguistic nuances to recommend optimal benefits packages.
• Data tools for employees to use to weigh the benefits of hard-to-value items that don’t have a monetary value like flexibility and uniqueness.
• PEOs and time keeping tools can make upgrades to their services to better incorporate CBA complexities using machine learning and AI.

These types of data tools would not only enhance efficiency but also foster greater transparency and compliance.

Benefits for stakeholders

For workers, data science means peace of mind—knowing they’ll receive fair compensation for their hard work. For companies, it translates to cost savings, reduced compliance risks, and improved employee satisfaction. And for unions, it signifies a reaffirmation of their negotiated agreements, upheld consistently across the board.

Outlook

In today’s rapidly evolving construction landscape, data science isn’t just a luxury—it’s a necessity. By embracing data-driven solutions, companies can navigate the complexities of CBAs with confidence, fostering a culture of fairness and trust. The era of labor management powered by data science is upon us, promising a brighter future for all stakeholders in the construction industry.

Interested in exploring data solutions for your construction company? Contact your Keiter Opportunity Advisor or the Keiter Data Solutions Team. Email | 804.747.000. We are ready to share our knowledge and ideas to help improve your business processes and solve your challenges.