Bersin by Deloitte Research: Human Resources Leaders Need to Harness Predictive Analytics Technologies

OAKLAND, Calif., Feb. 11, 2015 /PRNewswire/ -- Human resources leaders need to harness new out-of-the-box predictive analytics technologies that could help them make better talent decisions — without relying on sophisticated and expensive data scientists. Those are the findings of a new research report from Bersin by Deloitte, "Facilitating Data-Driven Decision-Making through Talent Analytics Technologies."

Summarized in a complimentary WhatWorks® Brief, the findings show that as pressure mounts for HR to provide instant statistical data and analysis to support business decisions, more HR organizations are looking at talent software systems for decision-making power. In response, HR software providers are racing to incorporate predictive models, richer reporting and dashboard functionality, and improved workforce planning capabilities into their solutions. Such software could help organizations improve retention, strengthen leadership pipelines and improve employee career paths. While these predictive analytics solutions may put a great deal of power into the hands of HR and business leaders, organizations need to make sure the data is accurate and is interpreted accurately.

"Organizations are struggling to build their internal analytics skills and are looking for answers in technology," said Karen O'Leonard, vice president, benchmarking and analytics research, Bersin by Deloitte, Deloitte Consulting LLP. "In fact, half of human capital management technology buyers cite analytics as the top driver of their purchase decision.ⁱ However, technology alone is not the answer. HR organizations need to know how to glean from what may be a morass of statistical data the information they need to make meaningful change."

The research shows how a large global pharmaceutical organization recognized that to execute on its growth strategy, it needed to improve employee retention, particularly among its salesforce. Using data gathered on both active and terminated sales managers, the analytics team created a model to predict the likelihood of employee turnover. While some managers believed that compensation was a primary driver of turnover, the model found that other factors played a greater role, including the employee's tenure, marital status and the tenure of their supervisor. The organization was then able to take proactive steps to reduce the turnover.

Such examples demonstrate how predictive analytics solutions can act as catalysts for changes in the way an organization manages its talent. However, the research also shows that as organizations begin to adopt these solutions, they should take steps to prepare their employees and check their data. Specifically, the research suggests organizations should:

• Validate the accuracy of predictive models over time and within different segments of the employee population (by geography, job level, etc.). For example, how many of the high-risk employees actually left within a 6-12 month period?

• Look for analytics solutions that reveal the factors related to predictions so that HR can establish talent initiatives based on those identified factors. For example, for employees at high risk of leaving an organization, managers should target appropriate interventions such as career discussions, development programs, or mobility initiatives before the employee looks for new positions elsewhere.

• Seek solutions that offer action steps, not just data. For example, for employees at high risk of leaving an organization, the solution could indicate flexible work options if distance to work is a contributing factor.

• Set up data governance processes to ensure the quality of their data, and work to improve data quality over time.

• Establish development programs or send their staff to outside courses to help managers and HR staff correctly interpret and act on the data and predictions.

Finally, the research demonstrates that HR leaders should look for predictive analytics solutions that incorporate data both from a variety of internal systems (HR, financial, operational, compensation) and external sources (economic indicators, local labor market, employer brand reputation, etc.).

"The model's accuracy is a critical element," O'Leonard said. "All relevant data should be included to provide the HR community and the entire organization the information that they need to make timely business decisions."

Register to join Karen O'Leonard, vice president, benchmarking and analytics research, Bersin by Deloitte, Deloitte Consulting LLP, for her online webinar, "Data-Driven Talent Decisions: Where We Are Now, and Where We Want to Be," March 19, 2015, 2 p.m. ET / 19:00 GMT.

Those interested in learning more about Bersin by Deloitte or its WhatWorks® membership may email [email protected] or call +1 510 251 4400.

About Bersin by Deloitte
Bersin by Deloitte delivers research-based people strategies designed to help leaders and their organizations in their efforts to deliver exceptional business performance. Our WhatWorks® membership gives Fortune 1000 and Global 2000 HR professionals the information and tools they need to design and implement leading practice solutions, benchmark against others, develop their staff, and select and implement systems. A piece of Bersin by Deloitte research is downloaded on average approximately every minute during the business day. More than 5,000 organizations worldwide use our research and consulting to guide their HR, talent and learning strategies. For more information, please visit http://www.deloitte.com/bersin or http://www.bersin.com.

As used in this document, "Deloitte" means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see http://www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.

ⁱ "The Market for Talent Management Systems 2014: Talent Optimization for the Global Workforce,"Bersin by Deloitte/Katherine Jones, September 2014

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