Simple to Futuristic: How to Use People Analytics to Make your Company Better
From recruiting the right employees to maximizing engagement and productivity, HR analytics can help companies galvanize their most important asset — their employees. HR executives can use everything from simple math to savvy artificial intelligence tools to do this well.
The simple: using basic math to drive business decisions
“The road to analytics success isn’t always paved with data scientists,” writes David Creelman for the Society of Human Resource Management’s blog.
In fact, he offers four steps for using statistics to drive business decisions:
- We need to be clear about what question we are trying to answer.
- We need to gather the best available evidence — which, even if it not good, will be better than no evidence.
- We need to assess the quality of the evidence so we can make an informed judgment.
- Often, basic math is all we need to inform our judgment.
It’s also important for those in HR to recognize problems that call for more sophisticated solutions.
The middle-of-the-road: Improving the way companies collect and use employee data
Google is a model for this, writes Steffen Maier for Entrepreneur.
“At Google, surveys aren’t just about checking the pulse of the workplace, they’re about constantly striving to improve it,” he writes. The company “uses feedback to optimize different aspects of its people processes and align them with its unique work culture. As a result, the company reports an average participation rate of 90 percent.”
It works well because Google combines quantitative data and survey results with quantitative research.
“Google’s process provides HR insights into employee engagement. It also creates trust between employer and employee,” Maier writes. “Googlers feel a sense of equality because they directly shape how their company is run.”
The high tech: Unleashing artificial intelligence
“Nearly a quarter of all new hires leave within a year,” writes Steven Pearlstein for The Washington Post, “while Gallup reports that half of those who do stay reported being not engaged. The resulting drag on profits and productivity represent a multibillion dollar opportunity.”
Stepping into that opportunity are companies that focus on creating algorithms that predict what kinds of employees will be the most successful and stay the longest.
One software company “bases its scoring of job applicants on several thousand data points. It has found that having more years of experience in a job doesn’t necessarily correlate with a greater probability of success,” Pearlstein writes. “Moreover, factors that do correlate can vary markedly from one job category to the next — or even in the same job category at different companies.”
These algorithms are bringing dramatic changes to traditional hiring practices and job requirements, and they’re constantly learning about what defines employee success in different roles.
“The computer-driven process clearly reduces the irrational biases and prejudices that prevent lots of applicants from even being considered,” Pearlstein writes.