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Understanding People Analytics for HR Professionals

By Dr. Mesut Akdere, Director of Purdue HR Analytics Interactive Learning Lab, Purdue University

Dr. Mesut Akdere, Director of Purdue HR Analytics Interactive Learning Lab, Purdue University

Big data is everywhere, even in HR. Whether we call it People Analytics, HR Analytics, or Talent Analytics, big data in HR is here to stay and will fundamentally change the way we do HR? Applying data science to the people side of the business is a new concept taking HR departments by storm. Stemming originally from single HR data warehouse record system approaches, people analytics was concerned with simple HR metrics such as ‘time to hire’, ‘turnover cost’, and ‘retention rate’. The idea behind people analytics is to capture, store, analyze, and monetize the information related to an organization’s HR areas and functions. By the nature of its function, HR has always collected employee data. However, it has been taken data to a new realm through people analytics—in which such data is continuously extracted to better understand and manage employees.

As employee productivity becomes even more critical in workplace environments that require a highly complex and integrated workflow, people analytics presents new frontiers to HR and other business functions. Nevertheless, traditionally, HR aimed to identify ways to justify its value by demonstrating the return-on-investment (RIO) is limiting the potential people analytics presents in identifying the right candidates for the job, helping employees become highly productive and satisfied with their jobs, and driving organizational goals. The focus in people analytics should transform from individual employees to interactions among employees to get a more in-depth view of the organization, especially when collaborative teamwork is the organizational norm so that the organization better identifies social groups employee associate themselves in designing teams.

"People analytics demonstrate success in reducing time-to-hire and increasing quality of hire leading to increased revenue, decreasing attrition rates, increasing performance and revenue"

While big data’s 5 “V”s (variety, volume, veracity, velocity, and value) apply to people analytics, volume and velocity may differ compared to other areas of data science. For velocity, the measure of how fast the data is coming in, HR-related data is mostly static and only change over longer periods of time when promotion or termination occurs. Similarly, for volume, or the quantity of data collected and stored, it is not possible to generate terabytes of HR-related data for a single organization. Furthermore, HR professionals are not traditionally trained in data science (although people analytics has become of one the courses taught in HR programs and SHRM includes it in its Human Resource Curriculum Guidebook). Data visualization can help HR departments to effectively and accurately identify patterns, trends and correlations in their organizations, enabling HR professionals interpret vast data and identify relevant information in areas such as people data (employee demographics, competencies, skills, and engagement), program data (learning and training program-related attendance, participation, and outcomes), and performance data (employee performance ratings, performance management data, and succession planning).

In addition to internal organizational data, external labor and workforce industry data can be incorporated for predictive analytics in key HR functions including recruitment and hiring, performance evaluation, training and development, and retention both at the individual and organizational levels. At the individual level, the outcomes concentrate on the improvement of employee performance, job satisfaction, and decreasing employee turnover intention. At the organizational level, the focus is on the financial impacts of these outcomes as well as the impact of HR programs to the organizational strategy as a strategic business partner.

People analytics demonstrate success in reducing time-to-hire and increasing quality of hire leading to increased revenue, decreasing attrition rates, increasing performance and revenue. However, challenges remain in the practice of people analytics as more skilled and competent HR professionals in data science are needed to revamp HR departments to strategically align people analytics with business needs. Furthermore, ethical concerns around employee privacy and data security remain a significant challenge, requiring organizational policy development. But, as people analytics become more holistically integrated into all HR functions, organizational capacity to identify more complex employee patterns and trends will be enhanced through data-driven decision making to advance both employee and organizational success.

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