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Human resources departments have been slow to get on board with big data, and it’s not just a lack of forward thinking. They face big challenges when implementing hr data analytics, both of logistics and mindset.
A big data initiative requires HR to acquire data from all the different departments within the business. They have to acquire, sanitize, unify, and analyze data from multiple departments as well as from multiple business functions, including payroll and finance.
The problem gets even bigger for HR departments venturing outside their companies into the world of unstructured data and predictive analytics. They need people who have the skills to gather and prepare data for analysis in addition to performing analysis.
Only one out of three HR managers describes their big data proficiency as either “good” or “excellent.” For many managers, the problem goes all the way to back to graduate school. Those who complete an MS in HR Management instead of the MBA don’t experience the same quantitative rigor.
Also, most companies start by hiring quants for departments directly related to money, finance, and forecasting. Big data for HR has been an afterthought, not just for HR managers but also for the C-suite.
Data analysis is resource-intensive for IT, and many smaller companies simply don’t have the infrastructure for Hadoop and other analytics programs. Public cloud resources can be a great help for SMBs that want to analyze their own data, but an SaaS solution—from a company that’s already done unstructured data analysis—can be even better for companies that lack time, infrastructure, and in-house expertise.
When HR collects data on a candidate, particularly data from outside the company, the department has to consider privacy. Collecting sensitive information, such as personal health information or information about sexual orientation, can put HR in murky territory related to protected characteristics.
Laws related to the Fair Credit Reporting Act in the U.S. also come into play. Also, privacy laws in other countries, particularly EU countries, can become a minefield for HR data.
To many HR managers, the idea of implementing people analytics equals letting computers decide whom to hire. Although the desire to be ethical by sidelining computers is commendable, remember that using all available tools to hire the right people for the right jobs in the right companies is the ultimate ethical achievement for HR. It’s best for employees, shareholders, society—everyone.
Big data and predictive analytics support hiring, and they help us understand what makes candidates truly successful. But human beings still make the final decision. Thanks to data, they make better decisions.