For a 30,000-employee industrials company growing rapidly through acquisitions, maintaining accurate HR data was a constant challenge. Each new acquisition brought an influx of disparate data, inconsistent structures, and compliance risks, particularly when meeting federal reporting requirements like Affirmative Action Plans (AAP), Equal Employment Opportunity (EEO), and Fair Labor Standards Act (FLSA) regulations. These gaps in HCM data fundamentals not only strained resources but also delayed strategic decision-making about talent across the organization.
When the company engaged us, their HR team faced a tangled web of challenges. Data preparation for newly acquired companies took up to six weeks, consuming hours of manual effort. Inconsistent job classifications, mismatched AAP codes, and errors in hierarchical reporting were just a few of the recurring issues. Over the course of our partnership, we implemented a series of targeted interventions to clean up their data, automate quality checks, and establish sustainable HCM data governance processes that transformed their HR function.
Diagnosing and Resolving Systemic HR Data Errors
The first step in addressing the company’s HR data issues was to thoroughly diagnose the root causes of their errors. Our diagnostics uncovered a range of problems, from blank or missing fields to misaligned reporting structures. For instance, many employee records were missing critical information, such as job or position data, making it nearly impossible to maintain compliance with federal reporting requirements. These gaps highlighted the need for better HCM data best practices around record-keeping and validation.
We also encountered complex issues related to hierarchical relationships within the data. In some cases, managers and subordinates were incorrectly assigned, creating circular reporting structures that led to confusion and undermined organizational clarity. Other inconsistencies included discrepancies between positions and their corresponding job records, as well as mismatches between organizational units assigned to employees and their roles.
To resolve these challenges, we implemented:
- Rigorous data validation processes that flagged errors and inconsistencies.
- Systematic cleanup of reporting structures, ensuring clarity and alignment.
- Enhanced reporting templates that captured the necessary data fields to meet compliance requirements.
These efforts not only reduced immediate errors but also laid the groundwork for more robust HCM data governance practices moving forward.
Establishing Standardized Validation and Configuration
After addressing the initial wave of data errors, we shifted our focus to preventing future inconsistencies. Standardizing HR data configurations across the parent company and its acquired entities was essential. Disparate systems and manual processes in the acquired companies frequently led to issues such as mismatches between Affirmative Action Plan (AAP) codes and Equal Employment Opportunity (EEO) categories. Similarly, pay scales and personnel subgroups were often misaligned, leading to significant compliance risks and reporting inaccuracies.
We developed standardized templates and automated validation rules that ensured alignment across all entities. For example, we predefined valid combinations of AAP and EEO codes, allowing the system to flag invalid entries before they propagated. We also implemented rules to align job classifications with pay scales, addressing common issues where managerial roles were not correctly tied to corresponding compensation structures.
By creating and deploying these templates and rules, we gave the HR team tools to validate data at the point of entry. This significantly reduced the volume of errors and manual corrections, enabling the team to focus on more strategic tasks. These efforts reinforced the importance of HCM data best practices in ensuring consistent and reliable data.
Automating Data Audits and Reporting
Data quality is not a one-time fix; it requires continuous monitoring. Recognizing this, we partnered with the HR team to automate their data quality audits. Previously, audits were conducted sporadically and often relied on labor-intensive processes. We introduced automated tools created in Applaud to generate monthly diagnostics, a cornerstone of effective HCM data governance.
These automated reports provided a clear picture of data health across the organization. For example, they identified discrepancies where employees’ organizational units did not align with their assigned positions, or where Fair Labor Standards Act (FLSA) classifications were mismatched. By detecting these errors close to real time, the HR team could address them proactively.
We also tailored the reports to meet the needs of different stakeholders, offering division-level breakdowns as well as consolidated organizational summaries. This allowed the company to prioritize corrections efficiently and maintain high levels of data integrity while adhering to HCM data best practices.
Building a Scalable Data Governance Framework
To ensure long-term success, we worked with the company to establish a robust HCM data governance framework. This involved defining clear ownership of HR data fields and creating processes for maintaining data accuracy. We documented standard operating procedures for data validation, error escalation, and corrective actions, ensuring that the HR team could sustain high data quality even as the organization continued to grow.
An integral part of this effort was updating training materials for HR administrators. These materials covered everything from field definitions and job classification standards to the proper configuration of manager assignments. By empowering the HR team with the knowledge and tools they needed, we helped build a culture of accountability and precision—essential for maintaining HCM data fundamentals across a rapidly expanding organization.
Optimizing Processes for Acquired Companies
One of the most significant outcomes of our engagement was the transformation of the data onboarding process for newly acquired companies. Previously, it could take up to six weeks to prepare and integrate HR data from a new acquisition—a process fraught with errors and inefficiencies. By adding field derivations, automated validity checks, and simplified handoffs, we reduced this timeline to just one week of part-time effort.
This optimization not only accelerated integrations but also ensured that acquired companies were aligned with the parent company’s standards from day one. Pre-configured templates and automated validation reduced the burden on both the parent company and the acquired entities, creating a seamless transition. These efforts showcased the practical application of HCM data best practices in real-world scenarios.
The Business Impact: From Chaos to Confidence
By the end of our engagement, the company’s HR function had undergone a dramatic transformation. Key outcomes included:
- Regulatory Compliance: The company achieved consistent compliance with federal AAP, EEO, and FLSA requirements, significantly reducing regulatory risks.
- Enhanced Data Quality: Automated diagnostics and continuous monitoring ensured that errors were caught and corrected before they could impact reporting or decision-making.
- Operational Efficiency: Manual data preparation efforts were reduced by 80%, freeing up valuable time for the HR team to focus on strategic initiatives.
- Scalable Processes: A robust HCM data governance framework positioned the company to manage HR data effectively across future acquisitions.
This transformation highlights the power of mastering HCM data fundamentals. Through strategic interventions in HCM data governance, quality, and migration, we helped this company turn its HR data challenges into strengths, providing a solid foundation for compliance and talent management. As a result, they are now better equipped to understand and leverage their workforce, ensuring their continued growth and success.
By emphasizing HCM data best practices, robust HCM data governance, and the essentials of HCM data fundamentals, organizations can build sustainable frameworks that not only meet compliance standards but also enable strategic workforce management. Let this story inspire your journey to smarter, more reliable HR data.
