In my work with HR teams across industries, I’ve observed how incomplete data often undermines their ability to make impactful decisions. Whether it’s reconciling fragmented systems, capturing data for non-traditional workforce, or preserving historical records, ensuring data completeness can feel like solving a massive puzzle.
Completeness isn’t just filling in the blanks, but building a foundation where every piece of workforce data contributes to accurate, holistic insights. In this article, I explore the second pillar of the ACT Framework—Completeness—and share practical strategies, inspired by my experience with clients, to achieve Complete People Data. If you missed the first article of the series on Accurate People Data, you can read here.
Why Data Completeness Matters
Complete data is critical for HR decision-making because every missing piece creates gaps that undermine insights and strategies, leading to significant challenges:
- Skewed Metrics: Incomplete workforce data leads to inaccurate diversity reports or headcount projections, making it difficult for leadership to make informed decisions.
- Missed Trends: Historical data gaps can prevent organizations from identifying key patterns, such as attrition risks in specific roles.
- Eroded Credibility: Inaccurate dashboards due to incomplete data damaged one client’s HR team’s credibility with leadership, delaying critical workforce decisions.
- Inefficiency: HR teams often spend excessive time reconciling missing data instead of generating actionable insights, leading to delays and frustration.
- Compliance Risks: Missing demographic data can result in failure to meet regulatory requirements like gender equality reporting, exposing organizations to penalties.
Without completeness, even accurate people data lacks the context required for effective reporting, compliance, and strategic decision-making.
Key Challenges in Achieving Completeness
While the importance of completeness is clear, achieving it comes with its own set of challenges. Basis my experience of working with several organizations, here are the most common barriers:
1. Overlooking Non-Traditional Workers
Contractors, freelancers, gig workers, and interns often operate outside the scope of traditional HRMS or ATS systems. This exclusion creates gaps in workforce planning, underestimates labour costs, and skews diversity metrics. One client realized their workforce cost estimates were off because they hadn’t included freelancers’ hours in payroll data.
2. Fragmented Systems
HR data is often spread across multiple platforms such as HRMS, ATS, Payroll, and Performance Management systems. When these systems do not communicate with each other seamlessly, important details fall through the cracks. For instance, at a growing startup, candidate certifications tracked in their ATS weren’t transferring to the HRMS after hiring. This resulted in gaps in employee skill data and delayed L&D interventions.
3. Historical Data Loss
Organizations frequently delete or archive data for exited employees, making it difficult to analyse past trends. A client’s inability to track historical attrition trends left them blinded to retention risks in critical roles.
4. Evolving Organizational Structures
Dynamic changes like reorganizations, mergers, and acquisitions often lead to mismatched or incomplete data. Older systems may fail to align with new structures, creating reporting gaps. Following a merger, one client struggled with inconsistent department codes across systems, leading to discrepancies in headcount reports and delaying strategic talent decisions.
Steps to Achieve Data Completeness
I have worked with organizations on their people data strategy, and found the following approach impactful in helping HR teams pursue Data Completeness:
Steps to Achieve Complete People DataSteps to Achieve Complete People Data
1. Integrate and Sync Systems
Connect platforms like ATS, HRMS, and payroll using APIs or middleware to ensure seamless data flow. For instance, syncing an ATS with an HRMS allows candidate details, such as skills and certifications, to flow into employee records, reducing data gaps and improving workforce planning.
2. Capture Data for Non-Traditional Workers
Expand data collection to include all workforce segments. A client improved their workforce planning by tracking gig workers in payroll systems and including contractors in DE&I metrics to gain a holistic view of workforce composition.
3. Preserve and Use Historical Data
Systematically archive data for exited employees to retain valuable historical insights. One client retained performance and compensation records for exited employees, which allowed them to analyse workforce evolution and develop better retention strategies.
4. Standardize Data Formats Across Platforms
Develop unified data entry standards to reduce inconsistencies. Aligning titles and department names across ATS, HRMS, and payroll systems ensure consistency in reporting and reduce reconciliation efforts.
5. Audit and Update Regularly
Periodic data reviews help identify and fill gaps while ensuring records remain current. After a major reorganization, it is a good practice to revisit demographic data and update employee records for compliance reporting.
Completing the ACT
Complete people data is essential for holistic decision-making. By integrating systems, capturing all workforce segments, and preserving historical data, HR teams can ensure their insights are representative, actionable, and trusted. These strategies are the foundation for overcoming challenges and unlocking the potential of Complete People Data.
With Accuracy and Completeness in place, the final step is ensuring data is Timely—the focus of my next article in this series on the ACT Framework.
Where have you encountered challenges with incomplete people data? Any best practices you can share?