Data Quality in People Analytics

In today's business landscape, organizations are increasingly relying on people analytics to make informed workforce decisions. From talent acquisition to performance management to employee retention and reasoning the terminations people analytics has the potential to transform HR. However, there’s a fundamental aspect of People Analytics that often goes unnoticed—or worse, ignored—until problems arise, which is Data Quality.

“Data really powers everything that we do.” — Jeff Weiner.

The key questions are: why is data quality important, and if it’s so crucial, why is it often overlooked? Let’s dive into these questions. By the end, we’ll hopefully gain a clearer understanding or at the very least, an appreciation of why data quality truly matters.

Why Data Quality is important in People Analytics

In people analytics, data quality refers to the accuracy, completeness, consistency, and reliability of data related to the workforce. When data quality is high, teams and leaders can be confident that their insights are grounded in reality. Here’s how good data quality impacts various areas of people analytics:

1. Improved Decision-Making: When data is accurate, leaders can rely on the insights derived from it to make better decisions about hiring, promotions, training, and retention strategies.
2. Enhanced Employee Experience: Quality data helps identify real pain points in the employee lifecycle, from onboarding to exit, enabling teams to improve the employee experience.
3. Efficiency in Reporting and Compliance: High-quality data makes it easier for teams to meet compliance requirements and produce reliable, timely reports without backtracking to fix errors.
4. Predictive and Prescriptive Analytics: With high-quality data, organizations can leverage advanced analytics (e.g., predicting attrition or identifying skill gaps) to make proactive interventions, rather than reactive ones.
5. Confidence in Data: With proper data quality checks in place, leaders and the HR teams can show high confidence in what they are seeing on a dashboard.

Why Data Quality is ignored?

If data quality is so important, why do so many organizations overlook it? I came across few of the below situations when this crucial step is ignored (or overlooked)

1. Focus on Quick Wins Over Long-Term Quality: In the race to show value from people analytics, teams often prioritize quick wins over a deeper investment in data infrastructure and integrity. This however, leads to analytics that are delivered fast but aren’t always reliable.
2. Siloed Data Sources: Data for people analytics often comes from various systems (e.g., HRIS, ATS, EXS, LMS), which can lead to inconsistent or duplicate information. When data is spread across siloed systems, maintaining quality becomes a challenge that many organizations choose to bypass rather than solve as this is relatively time taking process.
3. Limited Awareness or Understanding: One big pain point is not all HR professionals are data experts. As a result, data quality issues often doesn't hold a space in the prioritization and can go unnoticed or be dismissed as low priority. Without an understanding of data principles, even basic quality checks may be skipped.
4. Resource Constraints: Lack of budget is a long term issue with People Analytics but, this trend is changing. Ensuring data quality requires dedicated resources, both in terms of time and skilled personnel. Many HR departments due to lack the budget or capacity to enforce rigorous data quality standards, instead opt for shortcuts or “good enough” data.

What are the steps to solve this issue?

Here are some steps that I try to enforce in my current organization to bring data quality into focus within People Analytics:

1. Data Audits: Conduct data quality checks to identify discrepancies, redundancies, and outdated information in the initial stages of data layer. Catching these issues early ensures that they don’t affect the analytics and insights.
2. Educate and Train stakeholders: Have regular trainings to educate all stakeholders about the importance of data accuracy and how it can impact the People metrics.
3. Automation and Validation Tools: Use data validation rules and automation tools to catch errors in the ETL flow. For instance, preventing empty fields to be loaded into the data storage or flagging data quality issues at the initial stages of ETL flow can reduce future clean-up work.
4. Culture of Accountability for Data Quality: Just as data quality is critical in other departments like, finance or marketing, the same principle should apply to Stakeholders and People Analytics professionals. By making data quality a shared responsibility, teams can maintain high data quality across the board.
5. Data Governance: Lastly, it’s essential to establish clear guidelines for data entry, management, and maintenance. Doing so helps prevent inconsistencies and data gaps, ensuring the accuracy and reliability of employee information over time.

I would like to conclude this by stating that, While data quality in People Analytics may seem like a back-end issue, its impact is clearly visible in front-end dashboards. It’s essential to treat data quality as the foundation of People Analytics but not an afterthought. By making data quality a priority, organizations can build trust, improve the reliability of their dashboards, and empower leaders to make truly data informed decisions that improves the organization forward.