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How Analytics Drives Talent Development


Tue Feb 10 2015

How Analytics Drives Talent Development

Organizations are increasingly relying on advanced analytics to make better, more strategic business decisions. Unfortunately, many talent management professionals are not applying advanced analytics to their organization’s biggest asset—its people. But that is changing quickly, according to a new whitepaper from UNC Kenan-Flagler Business School_,_ Driving Talent Development with Data. 

Whitepaper co-authors, UNC Executive Development Director Kip Kelly and Vetrics founder and CEO Gene Pease, explore how leading-edge companies are exploiting the potential of human capital analytics to improve talent acquisition, employee engagement, retention, and talent development. 


Driving Talent Development with Data defines human capital analytics, also known as human resources analytics or talent analytics, is the “application of sophisticated data mining and business analytics techniques to human resources data.” According to the research, several trends have converged to accelerate the growth of talent analytics: 

  • Amount of data that is available to companies is growing exponentially.

  • Cheaper and faster technologies have made it more affordable than ever to collect and analyze large datasets.

  • New technologies enable the structuring of unstructured data.

  • Annual and quarterly reports are giving way to real-time decision making and predictive analytics. 

Although talent analytics is taking hold in organizations, the process is still intimidating to many leaders. Kelly and Pease advise organizations that want to venture into human capital analytics to start by taking smaller measurement steps. More important, it is helpful for these organizations “to view the human capital analytics process as a measurement continuum,” the authors write. The continuum looks something like this: 

  • Anecdotes—tell the story behind the numbers.

  • Scorecards and dashboards—summarize talent management strategies and associated measurements.

  • Benchmarks—compare with an organization’s actual practices.

  • Correlations—describe the statistics where two or more variables move together.

  • Isolation and causation—discerns why a metric or key performance indicator has changed.

  • Predictive analytics—measure the impact of the causes and prescribe future investments.

  • Optimization—offers insight into where human capital investments are working and presents options for improvement. 

In addition, Driving Talent Development with Data recommends that organizations follow a five-step process for creating a human capital analytics measurement strategy: 

  • Step 1: Drive alignment with business goals.

  • Step 2: Establish business measures of success.

  • Step 3: Guide the development of content that’s aligned with business needs.

  • Step 4: Provide in-process measures for continuous improvement.

  • Step 5: Prove and improve. 

According to Kelly and Pease, “Best-in-class organizations see measurement is an intentional process and have a measurement strategy in place that guides the design, deployment, and analysis of each investment. A measurement process provides a mental model for everyone in the organization to use and provides a common language that makes it accessible and achievable by everyone involved.” 

For details on each step, guidelines for creating a measurement map, and examples of organizations successfully using human capital analytics, download the UNC whitepaper, Driving Talent Development with Data.


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