Case Studies Global HR Analytics improves Talent Retention Rates

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	Global HR Analytics improves Talent Retention Rates

Global HR Analytics improves Talent Retention Rates

Technology Category	Analytics & Modeling - Data Mining Analytics & Modeling - Predictive Analytics Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries	Telecommunications
Applicable Functions	Human Resources
Services	Data Science Services System Integration
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Data Mining

Analytics & Modeling - Predictive Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Telecommunications

Applicable Functions

Human Resources

Services

Data Science Services

System Integration

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.

About 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.

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.

Impact #1	Using synthetic repositories, the analytics team detected patterns leading to employee churn.
Impact #2	The team identified employees most at risk of leaving the company.
Impact #3	Developed targeted interventions to retain high-risk employees.

Impact #1

Using synthetic repositories, the analytics team detected patterns leading to employee churn.

Impact #2

The team identified employees most at risk of leaving the company.

Impact #3

Developed targeted interventions to retain high-risk employees.

Benefit #1	Expected 0.5% reduction of turnover rates.
Benefit #2	Expected double-digit million savings on hiring costs.

Benefit #1

Expected 0.5% reduction of turnover rates.

Benefit #2

Expected double-digit million savings on hiring costs.

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Overview

Global HR Analytics improves Talent Retention Rates

Operational Impact

Quantitative Benefit