“Humanizing” Big Data to Extract Answers to Your Hiring Problems

October 31st, 2013

Your company probably has a truckload of staffing data stored on computer drives and in good old-fashioned cabinet drawers. Just thinking about the sheer volume of it can be overwhelming, and it’s easy to disregard the quality of data when you don’t have a clear picture of its worth.

Your challenge – and the key to answering your staffing questions – is putting a human face on all that data. In other words, rather than turning people into statistics (egad, no!) you need to turn statistics into living, breathing information that can enhance the work lives, morale and productivity of your people.

No small task. But it’s very doable…and very much worth the effort.

Start With the Basics

Before you even begin to analyze data for staffing applications, you need to determine which questions need to be answered. For instance, if your issue is high turnover, which specific departments or areas are experiencing this revolving-door effect? If you have too many vacancies, then how long does your hiring process take?

Define your problems and challenges first, and then put the resources in place to unveil the solutions.

Take the Guesswork out of Hiring

U.S. companies spend about $46,000 a year on training programs for newly hired employees. And the cost of replacing an individual who turns out to be the wrong hire can range from $50,000 to more than $300,000 depending on the position. In light of such harsh financial realities, an increasing number of companies are turning to big data analysis to help with their hiring practices. Of equal importance is the need to retain top talent and prevent leading performers from becoming flight risks.

Data analysis takes the assumptions and guesswork traditionally associated with hiring and retention and capsizes it, replacing it with real scientific data. It’s all about marrying human resources with data resources for optimal results and value.

Listen to These Success Stories

Big data has disproven many preconceived notions about who makes a good employee and who doesn’t. Hiring managers may have ideas about which candidates to single out, but only through data analysis can they back up their opinions with hard facts. Consider:

  • A regional bank was threatened by an alarmingly high turnover rate among its front-line employees, including customer service representatives. Its traditional solution was to implement frequent pay raises in order to try and retain staff. However, data analysis showed that raising pay by 10 percent shaved only one-half a point off the bank’s turnover rate. In actuality, workers felt dissatisfied, not underpaid.
  • At Xerox, data analysis determined that one of the best predictors that customer service employees would stick with their jobs was that they live nearby so they could get to work easily. This and other findings helped Xerox decrease its attrition rate by 20 percent in a pilot program that was extended based on its success.

For decades, companies have collected data related to pay and other HR issues. But comprehensive analysis goes beyond simple comparisons and theories and makes much more complex – and accurate – decisions possible.

For up-to-the-minute industry guidance on making big data work for you, and how to staff accordingly, read our related posts or contact the team at Select Group, Inc., today!

Comments are closed.