Discover how synthetic data in Workday can revolutionize HR processes while maintaining privacy compliance and enabling advanced AI-driven insights.
Synthetic data is fake data that mimics real-world data. In Workday environments, synthetic data can serve as a powerful alternative to production HR data – for training Artificial Intelligence (AI) models, testing system integrations, and developing new analytics capabilities – without exposing sensitive employee information.
Workday unites company, policy, employee, financial, and other data in a single cloud platform, making it an ideal candidate for synthetic data generation tools. Its comprehensive employee lifecycle management – from recruitment to retirement – generates vast amounts of data that can be synthetically replicated for various business purposes while maintaining strict privacy standards.
Unlike traditional data masking techniques that simply scramble or hide existing information, synthetic data creates entirely new datasets that preserve the statistical relationships and patterns of original data without containing any actual employee records. With Gartner predicting that 60% of data for AI will be synthetic in 2025, the adoption of this technology in HR systems like Workday is inevitable.
HRTech providers are increasingly leveraging synthetic data to train AI models for recruitment processes.1
AI can use synthetic data generation to simulate various candidate profiles, helping HR teams refine job descriptions and improve screening processes. In Workday's recruitment module, synthetic data enables organizations to test and optimize their hiring algorithms without using real candidate information.
AI models trained on synthetic data can improve candidate screening by predicting which applicants are most likely to succeed within an organization. By analyzing synthetic data patterns, AI can simulate diverse recruitment scenarios, optimizing the model for better candidate-matching algorithms.
The most significant advantage lies in bias reduction. Synthetic data allows for "bias-free" training by enabling the model to analyze a balanced dataset across all demographic factors, thus reducing the risk of discrimination in HR processes. This is particularly crucial for large enterprises using Workday's talent acquisition features, where unconscious bias in historical hiring data could perpetuate discriminatory practices.
| Synthetic data generation | Benefit | Impact |
| Candidate simulation | Improved job descriptions | Higher quality applicants |
| Bias-free training data | More equitable AI models | Reduced discrimination risk |
| Screening optimization | Better candidate matching | Increased retention rates |
According to HR author, speaker, and board advisor, Tess Hilson-Greener, 45% of employees sourced through referrals tend to stay with a company for more than 2 years, indicating how data-driven recruitment improvements can significantly impact retention.
AI models require large amounts of training data to accurately assess employee performance. Synthetic data creation allows for experimentation with various performance metrics, helping AI algorithms learn to identify performance trends and growth opportunities without exposing real employee data.
Organizations can use synthetic employee data to model different learning scenarios, test new training modules, and optimize development programs before rolling them out to actual employees. This approach ensures that training initiatives are more targeted and effective while protecting individual employee privacy.
A synthetic dataset for a particular employee gives HR professionals the ability to assess workforce performance, fine-tune training programs, or optimize recruitment processes, without compromising privacy. The synthetic data approach allows HR teams to experiment with different learning pathways, understand which combinations of skills and experiences lead to better performance outcomes, and create more engaging development experiences that boost job satisfaction and retention rates.
Companies that regularly provide feedback have been shown to maintain higher engagement levels, with 43% of highly engaged employees receiving feedback weekly.2
Synthetic data in Workday allows HR teams to identify patterns and predict engagement issues before they escalate.
Predictive analytics for attrition risk is a perfect example. AI models trained on synthetic data that mimic real employee behavior and job satisfaction scores can help identify at-risk employees, providing actionable insights into retention strategies while safeguarding personal data.
By creating synthetic employee profiles that mirror real engagement patterns, organizations can test different intervention strategies and understand which factors most significantly impact employee satisfaction. This proactive approach allows HR managers to address potential problems in work-life balance, team dynamics, and job satisfaction early, maintaining high levels of employee morale.
For example, a significant percentage of employees value work-life balance over salary, suggesting that HR strategies informed by insights generated from synthetic financial data should prioritize flexible work arrangements and wellness programs to enhance worker satisfaction and retention.
Synthetic data solutions can model various business scenarios, like expanding or downsizing, enabling AI to forecast future HR needs more accurately. Workday's planning and analytics modules can leverage synthetic data to simulate different organizational structures and workforce compositions.
This capability proves invaluable for strategic planning, allowing organizations to understand the implications of different staffing decisions without exposing actual employee data. HR teams can model scenarios such as:
High turnover rates are a persistent challenge, with industries like healthcare experiencing some of the highest resignation rates. Synthetic data in healthcare helps predict turnover risks and identify retention strategies tailored to specific organizational needs.
Before rolling out new workplace policies, HR teams can now make use of generative AI synthetic data techniques to simulate their potential impact. This capability is crucial for fine-tuning policies related to work arrangements, compensation changes, and employee wellness programs.
Workday's configuration flexibility combined with synthetic test data ensures that policy changes achieve intended outcomes without unforeseen complications. For example, organizations can model the financial impact of benefit changes, test new performance review processes, or evaluate the effectiveness of diversity and inclusion initiatives.
One of the most significant advantages of synthetic data in HR is its inherent privacy-protection feature.
Traditional data anonymization techniques, such as removing names, addresses, or other Personally Identifiable Information (PII), are often insufficient because anonymized data can sometimes be reverse engineered to identify individuals.
With synthetic data, however, privacy is inherently protected because the data is entirely fabricated and holds no direct link to real employees.
Privacy laws, such as CPRA, PSI DSS, HIPAA, GDPR, and DORA European regulations, impose strict rules on the collection and handling of personal data. Synthetic data companies help enterprises stay compliant by providing data that is "privacy-safe" from its inception.
The privacy advantages of synthetic data become even more critical when considering Workday's extensive data ecosystem. Unlike native Workday data masking, that can still leave traces of original information, synthetic data provides complete privacy protection while maintaining data utility for analytics and AI applications.
While synthetic data can help prevent a Workday data breach, there are still challenges to consider.
For synthetic data to be effective, it must accurately represent the statistical distributions of the real-world data it models. Poorly generated synthetic data can lead to underfitting or misrepresentations, reducing the model's performance.
The core advantage of synthetic data in Workday is that it maintains patterns and correlations from real-world data, enabling models to learn more effectively without overfitting to sensitive information. However, making sure that synthetic data accurately reflects real-world complexities and distributions is crucial. Organizations must validate that their synthetic datasets maintain the intricate relationships present in Workday's object-oriented data model.
Another challenge is ensuring that synthetic data captures rare but important cases, such as unusual employee behaviors or exceptional performance metrics, which may be crucial for predictive accuracy. Workday must consider these factors to maintain the reliability and value of synthetic data in training AI models.
K2view Synthetic Data Management addresses these challenges through a unique approach that leverages business entities – such as customers, orders, or loans – that are automatically modeled on the metadata from individual datasets. A business entity approach maintains data relationships, hierarchies, and referential integrity across all systems.
For example, K2view ensures that synthetic employee records maintain logical relationships between compensation, benefits, and performance data, as well as organizational structures, while providing complete privacy protection for PII and other sensitive data.
Discover K2view Synthetic Data Management, the AI-powered
synthetic data generation tools that secure sensitive HR data.