Case Studies > Global HR Analytics improves Talent Retention Rates

Global HR Analytics improves Talent Retention Rates

Customer Company Size

Large Corporate

Product

Synthetic Data Repositories

Tech Stack

Synthetic Data Generation
Data Anonymization

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Employee Satisfaction
Productivity Improvements

Technology Category

Analytics & Modeling - Predictive Analytics
Analytics & Modeling - Data Mining
Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Telecommunications

Applicable Functions

Human Resources

Services

Data Science Services
System Integration

About The Customer

The customer is a large telecommunications company with over 90,000 employees. The company operates on a global scale and faces significant challenges in managing its workforce. Employee turnover is a critical issue, and the company is keen on finding innovative solutions to improve talent retention. The company deals with a vast amount of sensitive employee data, which is subject to stringent legal regulations. This makes it difficult to analyze the data effectively and derive actionable insights. The company is looking for ways to streamline its HR processes and leverage data analytics to enhance employee satisfaction and reduce turnover rates.

The Challenge

A large telecommunications company wanted to find new ways to reduce employee turnover and improve talent retention. Over 90,000 people’s sensitive data needed to be collected and analyzed to identify patterns in employee churn. However, legal regulations locked up the data, and manual anonymization of datasets for each analytics project took 6 weeks on average.

The Solution

The HR Analytics project took off when readily shareable synthetic copies of siloed HR data assets were created and distributed. The resulting synthetic data was no longer classified as personal data and so it was exempt from legal regulations. The synthetic copies were statistically highly representative of the original data, enabling the analytics team to find the same insights they would have found in the original. This approach allowed the company to bypass the lengthy manual anonymization process, significantly speeding up the analytics projects. The synthetic data enabled the team to detect patterns leading to employee churn, identify employees most at risk, and develop targeted interventions to retain talent.

Operational Impact

Using synthetic repositories, the analytics team detected patterns leading to employee churn.
The team identified employees most at risk of leaving the company.
Developed targeted interventions to retain high-risk employees.
Enabled the company to take proactive steps to improve employee retention.
Increased team productivity by streamlining the data analysis process.

Quantitative Benefit