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The Importance of Data Science in HR
However, it is becoming increasingly clear that our society is moving ever deeper into a knowledge based economy wherein knowledge, information and high level skills will be needed by both business and public sectors. The human capital element of the workforce, then, is just as relevant and important as it has always been. While implementing the best technologies is certainly important for successful business management, using those technological tools to improve human resource management will become ever more important for companies that are looking to find, develop, and retain the best human capital to help move the company in the right direction. Data science as it applies to human resources (HR), is thus an indispensable tool for small to midsize companies looking to get the most out of their workforce and personnel.
What is Data Science in an HR Context?
Data science applications in an HR context is also known as HR analytics, or people analytics. Essentially, data science for HR purposes analyzes people data that is collected through traditional HR departments or systems. This data can include absence management, payroll considerations, employee productivity statistics, mobile worker data, scheduling software, etc.
Data science applications for HR also takes into account certain information or statistics on business performance, such as operations performance data, business goals, process improvement initiatives, and others. The collection and analysis of people data and particular business-related data allows HR analytics to offer valuable understandings and insight into the value, efficiency, and overall performance of a company’s workforce. This data can subsequently be useful in modifying or implementing HR policies and practices and advise evidence-based decision making to help the company continue to grow and get the most from its workforce element.
What Can Data Science do for the World of HR?
• Improve Performance: Recent studies have shed light on the importance of employee performance for business profitability. In fact, one study found that a 5 percent increase in employee engagement could lead to a 3 percent growth in business revenue. Unfortunately, traditional employee review systems have become outdated and often don’t reflect real-time performance data. Data science applications, on the other hand, can allow HR members to gather information about employee performance that is based on both historical and real-time data. In fact, data science uses powerful algorithms that can even identify certain work habits and patterns as they pertain to both individuals and teams within the workforce.
• Employee Engagement: Predictive analytics that come with HR data science applications can also help HR managers and others identify work preferences for certain members of their team. Similarly, data science can help to recognize and classify factors that drive optimum performance for your workforce. For example, data science can help you identify which technologies add to employee productivity and help them stay engaged with their tasks from those that divert their attention from the task at hand. Also, predictive analytics can help HR departments discover which employees are most productive when given higher levels of freedom or autonomy from those who need more strict oversight.
• Recruitment and Hiring Process: Data science can also be useful to help employers, hiring managers, and other HR staff to follow and share employee related information. This can be extremely helpful when hiring managers have to go through hundreds of applications and are looking for unique insight into the profile of a worker looking to join the workforce. Data can be gathered and organized from diverse channels include social media profiles and wherever else a potential candidate may have left their “digital footprint.” Powerful algorithms associated with data science applications can allow HR personnel to analyze information about a candidate that might not necessarily be revealed in interviews or written applications.
• Employee Retention: As our economy continues to grow and more competitive jobs become available, dealing with high levels of turnover continues to be a challenge for companies and their HR departments. Managing employee turnover and retaining talent is essential for a company’s success. In fact, one recent study found that the true (and complete) cost of voluntary turnover was an astounding $109,676 per exiting employee. Data science and analytical tools can help HR departments and systems avoid these costs of voluntary turnover through identifying the greatest risks within the business for employee turnover, discover why their best talent might be tempted to leave, and predicting which employees present the greatest flight risk. This data can consequently be used to implement policies that encourage the best employee talent to stick with the company.
What Technical Skills Are Needed to Properly Evaluate Employee Data?
Of course, gathering a bunch of employee data is only useful if HR departments and systems know how to properly evaluate and utilize that data. For example, while analytics findings might help HR departments identify employees with the best performance numbers, it is equally important to understand that performance statistics don’t necessarily translate into leadership qualities. When evaluating members of the workforce for management promotion, a good HR leader will look beyond data on performance statistics to distinguish other skills that might be available through predictive analytics. The top employee performer is not necessarily the one most adept for leading a team.
Furthermore, HR departments also need to successfully make connections between the data collected and policies that are subsequently implemented. Once data science applications help you identify what perks or benefits drive employee performance, finding the most financially efficient ways to increase those benefits is key to successfully utilizing the data that is collected.
Lastly, HR departments that discover the benefits of data science for overall employee management might do well to assign a dedicated person for leveraging, analyzing, and sharing the data. Having someone take ownership of the information provided by data science can help to translate that data into achieving business objectives. Ideally, a data science supervisor should have skills in:
• IT: including managing system design and continuous improvement
• Business administration: to help in effective and true reporting on the value of the metrics being provided, and
• HR: to determine what needs to be measured in order to provide the most business value to a given organization.
Employee Management Software to Help Internal Personnel Processes
Data science for HR departments is a wide and diverse field that can help companies improve their workforce management. Employee management software and reporting is one element of HR analytics that can help companies understand internal processes and devise strategies to make the most effective workforce decisions.
Employee management software is a great tool for HR departments looking to streamline certain organizational processes while also improving the recruitment, training, and maintenance of your workers. Tangible improvements in productivity and efficiency are the expected outcomes when HR departments leverage and utilize the data it already has. Data science, then, can allow HR workers to gain a better understanding of their workforce and thus provide managers and company leaders with data, information, and suggestions that can help the business grow.