Gartner expects the market for big data and analytics to generate $3.7 trillion in products and services and create 4.4 million new jobs by 2015. According to the U.S. Census Bureau, over 75.6 percent of households are online, meaning a large number of adult Americans are posting substantial personal information on the Internet. With this volume of data available, predictive analytics is emerging as an in-demand trend in human resources.
Businesses have increasingly used predictive analysis for years but it has only recently been applied in the area of human resources. Historically, firms have studied historical data in order to anticipate opportunity and risk, with industries such as financial, marketing, and healthcare utilizing analytics to consider factors such as credit scores, sales targets, and healthcare plans.
Data Analysis Applied to Functional Areas
Companies have volumes of employee, HR, and performance data, ranging from demographic information, performance reviews, educational history, job locations, and other factors relevant to workers. Talent analytics would allow managers to use this data scientifically to make decisions about workforces and plan for the future, such as anticipating leadership and talent gaps and developing candidate pipelines.
Talent Data Available to Companies
Source: Bersin by Deloitte
In the growing field of talent analytics, recruiters are using predictive analysis to screen job candidates’ potential to become good employees. Talent analytics can be utilized to cross-analyze a candidate’s resume with personal information available on social networks such as Facebook, giving employers a tool to understand a candidate’s interests and personality. Data and analytics give HR professionals the opportunity to take a forward-focused and data-driven approach to talent management and people development.
According to a Deloitte survey of 436 North American companies, talent analytics is helping build better talent outcomes in regard to leadership pipelines, talent cost reductions, efficiency gains, and talent mobility.
Resistance and Challenges
However, there is some resistance to the application of predictive analytics in human resources. While almost every business operation can be automated, the personal or ‘human’ aspect is difficult to integrate into technology. While it is getting easier to predict someone’s next action, we are still far away from digitized systems being able to understood and predict human emotion.
A study by Accenture finds that while analytics are becoming important to executive management, they are not considered a replacement for human resource departments. A survey by the Chartered Institute of Personnel and Development (CIPD) found that HR professionals also are reluctant to engage with numbers or are passive towards them, and only half of senior HR leaders actually think that they are able to link their data to key business and financial data.
Companies face challenges applying predictive analysis to human resources, as data analytics demands skills not commonly found in HR departments. A March 2014 report from Bersin by Deloitte found that 78 percent of large companies rated HR and talent analytics as ‘urgent’ or ‘important’ enough to place analytics as one of the top three urgent trends, but 45 percent of the same companies rated themselves as ‘not ready’ to pursue predictive analytics. A different study from Bersin by Deloitte shows staffing for analysis is a top area of investment, and 31 percent of organizations surveyed either hired or transferred additional staff in 2013 to boost their analytics capabilities. Another 22 percent of organizations hired external consultants to help in their analytics initiatives.
HR Executive’s Assessment of HR’s Analytics Capability Levels
3 Ways to Start
For companies looking to apply predictive analytics to their human resources function, the transition can be challenging. However, there are some potential starting points:
1) Add some unexpected roles
Adding some niche positions such as demographers, business intelligence specialists, or mathematicians can help to bring in a different view while still being hands-on with numerical analysis and generating insights from data.
2) Invest in the equipment
Equip the team with HR technology and training in visualization and project management. Also building a close relationship between HR and IT helps as often HR organizations working in predictive analysis have an IT specialist on the HR staff.
3) Make analytics user-friendly
Use tools such as dashboards to make analytics easy to understand for business units. Search for technology that incorporates the user experience and visualization, so that it is approachable to business users.