AT A GLANCE

  • There is a shift in the HR industry from a traditional reliance on intuitive decision-making to a more data-driven approach
  • Examples of the difference between the currently used approach and the emerging data-driven approach highlight the needs for this shift, and help illustrate the expanded capabilities of data-driven decision-making in talent management
  • A study found that firms that most effectively managed their workforces by using analytics improved their firm’s profits by as much as 65%

 

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September 03, 2015

Top Reasons to Shift to a Data-Driven HR Model

There’s a shift happening in the HR industry. The shift is from a traditional reliance on intuitive decision-making among HR professionals to a more data-driven approach. This transition goes beyond just the use of metrics and is instead a comprehensive approach, where data is used continuously to improve every aspect of talent acquisition, retention, development, innovation, and workforce productivity.

A driving force behind this shift is the realization of employers that major HR decisions result in direct business impacts. And just as other areas like supply chain, sales, and finance have undergone similar transitions, HR is now next in line to be strategically and measurably improved so that data about one of the largest corporate variable expenditures – labor costs – can be analyzed and utilized. Under this new approach, HR will not only align to corporate goals, but directly and quantitatively impact these goals and outcomes.

The biggest shift under a data-driven approach will be to move away from backward-looking metrics and towards forward-looking data and predictive analytics. Historically, HR has relied almost completely on historical metrics, and this focus on the future means that HR will have the opportunity to forecast trends and alert executives about upcoming talent problems and opportunities.

Examples of Data-Driven HR Capabilities

Examples of the difference between the currently used approach and the emerging data-driven approach highlight the need for this shift, and help illustrate the expanded capabilities of data-driven decision-making in talent management.

Predictive Hiring: Data enables HR professionals to continually update the hiring criteria factors that accurately predict future job performance and retention of new hires. It also allows for accurate determination of which selection tools (such as the number of interviews, interview questions, grades, reference check scores, etc.) accurately predict good hires.

Succession Planning: Data allows HR managers to predict how long workers and new hires are likely to stay with the company and how high in the organization they are likely to reach in their career trajectory.

Learning: Data can reveal the most effective approaches for increasing individual and corporate learning speed and effectiveness. This is particularly important in a rapidly moving global environment where rapid self-directed learning is an important competency.

Performance Management Trends: Data can be used to demonstrate when and if performance management efforts will convert weak performers into above average performers. Algorithms can also forecast when the productivity and innovation levels of individual workers are likely to plateau.

Compensation and Benefits: Rather than relying on market salary surveys or intuition, a data-driven approach will be able to identify the types and amounts of rewards and benefits that have the highest measurable impact on worker productivity and retention.

Reasons to Shift

There are many reasons why companies should start looking at shifting towards data-driven decision-making. A recent study by the Harvard Business Review found that firms that most effectively managed their workforces by using analytics improved their firm’s profits by as much as 65 percent.

Predictive Analytics Prepare Companies for the Future: The most appealing feature of data-driven decision-making is the ability to predict trends and upcoming threats and opportunities. Trends and data will provide managers with the opportunity to make more informed and accurate decisions about what is likely to happen in the future.

Discover if Existing Approaches are Working: Metrics allow HR executives to see if their existing programs and tools are working. Analytics can also be used to assess the effectiveness of new talent programs.

Identify Hidden Causes of Problems: It’s difficult to improve weak performing programs without knowing the critical success factors that make them effective. By using forward-looking data analysis, HR can identify the hidden root causes of HR problems.

Continuous Improvement: The revealing of under-performance data can awaken leaders to realize that there is  room for improvement, even if everything seems to be running fine on the surface. Since data highlights process errors, the use and distribution of data reports can significantly decrease the number of major errors and weak decisions within the HR function.

Speed Up Talent Decisions: One of the biggest talent management problems in large organizations is inconsistency in decision-making. By gathering and reporting data on best practices, companies are more likely to see more consistent and accurate talent management decision-making across the organization.

Opportunity to be Strategic: Strategic decision-making is largely comprised of being forward-looking and impacting business goals. By implementing predictive analytics, HR can contribute substantially to the corporate business plan by forecasting, assessing risk, and preparing for the future.

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