HR data quality: Its definition, importance, and 5 actionable steps to improve it

Written by Sherif Luka , Solution Engineering Manager and Sebastiaan Bos , Director of Product @ Nakisa .

The article was originally published on the Nakisa website . Get more insights in the original blog post.

In today’s business landscape, HR data drives strategic decisions and shapes the future of the organization. However, the value derived from HR data is only as strong as its quality.??

In this comprehensive post, we’ll delve into the definition of HR data quality, its impact on the company’s bottom line (spoiler: an average of $12.9 million is lost each year, according to recent research by Gartner ), and common challenges organizations face in maintaining HR data quality. Drawing on our 20 years of experience in the field, we've designed five actionable steps for ensuring your company uses highly accurate HR data as a foundation for strategic decisions. Without further ado, let’s get started!?

What is HR data quality???

HR data quality refers to the accuracy, completeness, and relevance of the data stored and managed within a company's human resources systems. It encompasses the integrity of information related to employees, their roles, performance, compensation, as well as other critical HR metrics such as DE&I and turnover analysis.

Accuracy?

HR data must be error-free, up-to-date, and centralized across all data sources. HR teams assess data accuracy by evaluating whether the team can trust this data and identifying reasons for any doubts. Crucial steps include identifying errors, synchronizing data across systems, and archiving outdated information. Building trust in HR data requires eliminating sources of errors to maximize its utility and reliability.??

Breadth

Data breadth represents data completeness and representativeness. It’s essential for HR professionals to assess whether the data comprehensively represents the organization's employees and identify any gap. Encouraging employees to contribute additional data points and leveraging predictive modeling techniques can enhance data breadth, providing deeper insights into workforce dynamics.?

Consistency

HR professionals should establish and consistently adhere to uniform data formats and collection methodologies across the enterprise. Assessing consistency involves examining whether methods have changed over time and identifying any time gaps without data. HR teams should ask themselves: Has the standard been consistently applied? Are there any anomalies or issues in the data format? Standardizing data definitions and formats streamlines data management processes, enabling more effective analysis.?

Depth

HR data analysis must delve into granular details to provide targeted insights into workforce management and planning. HR teams must ensure they have all necessary HR data and that it’s granular enough to measure, target, and analyze the workforce at both the business unit and individual levels.?

Data quality vs. data integrity vs. data governance: What is the difference??

When exploring data quality, you can also come across the terms “data integrity” and “data governance”. Let’s clarify them as well. Data integrity ensures that data remains accurate, consistent, and trustworthy throughout its lifecycle, preventing accidental or intentional alterations or corruption. Techniques such as data encryption, access controls, and audit trails are employed to maintain data integrity and prevent unauthorized modifications or deletions.?

On the other hand, data governance is a framework of policies, procedures, and processes that oversee how organizations manage, protect, and utilize their data assets. It provides guidelines and structures for ensuring data quality, integrity, security, compliance, and accessibility, aligning data management practices with organizational objectives and regulatory requirements.?

What are the effects of poor data quality on the typical enterprise??

The insights derived from an organization's HR analytics are only as valuable as the data itself. The commonly heard adage "garbage in, garbage out" succinctly captures this concept: if poor-quality data is input into systems, the resulting outputs will also be compromised. Unfortunately, many companies only become aware of HR data quality issues when business and HR users are already confronted with accumulated errors. Such oversights can significantly impair leaders' ability to make strategic decisions for their organizations, ultimately impacting the bottom line.?

Here are four critical impacts organizations may face when their HR data falls short of the mark:?

• Wrong strategic decisions: C-level executives may make decisions about large-scale workforce restructuring, opening new branches, or pursuing mergers or acquisitions without accurate data backing. Despite optimism, this can lead to unforeseen challenges in employee morale, skill gaps, and cultural alignment, resulting in resistance, delays, and failed outcomes.?
• Loss of confidence in system use: Poor data quality can lead to a loss of confidence in the use of systems, resulting in inefficient utilization or even abandonment of solutions that could automate processes and save resources.??
• Legal implications and financial consequences: Breaching legal commitments, such as failing to meet gender equality reporting requirements, can result in significant financial penalties. For example, in France, breaching the Gender Equality Index can lead to fines of up to 1% of the organization's annual payroll bill.?
• HR staff work duplication and attrition: Approximately 80% of the overall analysis process is spent on cleaning or preparing data, leaving only 20% for actual analysis. This inefficiency can lead to duplication of work, increased rework, and higher staff attrition rates.

What are the reasons for poor HR data quality?

According to our 20-year experience in the field, the most common challenges for maintaining HR data quality include data silos, outdated HR technologies, manual data entry and inefficient change management processes.??

Data silos?

Global companies often rely on multiple HR systems in various countries, and those lack integration and data centralization. This results in information being scattered across various systems and departments, leading to fragmentation and impeding efforts to integrate data. Without a centralized approach to data management, organizations face difficulties in ensuring consistency and accuracy across their HR datasets.??

Outdated HR technologies

Not only do the legacy HR systems lack integration and scalability to support large enterprises’ needs, but they also have no visibility into missing or incorrect data. HR teams just don’t know about data quality issues until they got accumulated with time and become too evident. As a result, HR professionals lose trust in the systems they use and turn to simple tools, for example Excel or PowerPoint. While these tools may provide temporary relief, they lack the capabilities to effectively handle the complexities of modern HR data requirements and can only be functional if a lot of manual work is involved.??

Manual data entry

Manual data entry is another reason why so many companies have poor HR data. Manual methods are both time-consuming and error-prone, leading to data quality degradation over time: typos, data entered in the wrong field, missed entries, duplicated entries, conflicting or mismatched entries are all possible scenarios. During large transformations, manual data entry and changes can be extremely inefficient.??

Inefficient change management processes

During strategic organizational changes, enterprises often face inefficient change management processes, resulting in numerous iterations between stakeholders, delays in implementing organizational changes, and poor data quality.??

As we can see, there are many reasons for poor data quality. Let’s see how to address them in our next section.?

Want to improve your HR data quality? Find out the 5 actionable steps that ensure your HR data stays accurate and up-to-date in the original blog post.