HR Should Understand the Risks and Rewards of Using Data Analytics



​Big data is changing the employment landscape, and HR professionals who embrace data analytics as they examine employee behaviors and preferences have a great opportunity to improve productivity in the workplace, as long as they do it with an understanding of the risks. 

Recent advances in analytics are concentrated in three main areas: data, computing power and algorithms, said Zev Eigen, Ph.D., J.D., and Marko Mrkonich of law firm Littler Mendelson, speaking at the SHRM 2017 Annual Conference & Exposition. They explored the benefits and potential risks of using data analytics to augment HR decision-making processes.

The workforce is changing, said Eigen, who is Littler's global director of data analytics in Los Angeles. "We no longer have homogenous workforces with employees working in one room together. People change jobs a lot. There is more diversity and globalization. These things require a different approach."

HR professionals must find a way to embrace big data while also communicating legitimate concerns about how information is gathered, used and stored, said Mrkonich, who is a shareholder in Littler's Minneapolis office. "If you're not on the bandwagon, I guarantee that there are people in your organization who want you to be."

What Is Data Science?

Data science isn't that complicated, Eigen said. The scientists behind it have essentially two skill sets—mathematics (calculus and linear algebra) and computer programming—and they typically do three things:

• Predict stuff—such as what is the likelihood an employee will quit in the next six months?
  
• Classify stuff—including which groups of employees are likely to innovate or collaborate effectively.
  
• Identify patterns—such as whether there is evidence to suggest that wage and hour data have been tampered with?
  

Data analytics can help HR professionals make decisions for their workplace. For example, classification tools can be used to predict whether an applicant will be a high-or low-performing employee or whether a group of people will work together collaboratively, Mrkonich said. The data analyzed can be structured (like the information a job applicant fills out on a form) or unstructured (like the facial expressions made during an interview).

Eigen said he prefers the term "data-scientific approach" rather than "big data." Big data implies that a business has to use a giant data source or has to be a large company in order to use data analytics—but that's not the case.

The analysis doesn't have to use complex data, and there are tools available from vendors at different price points that will suit businesses with various needs and budgets.

Selecting HR Applications

Dr. Archana Arcot, GPHR, a conference attendee, told SHRM Online that she wants to know how data analytics can best be used to assist in making selection decisions and internal promotions and for conducting performance evaluations. Arcot is an HR business leader for the Americas at Infosys McCamish Systems in Atlanta. "What are the emerging trends? And from a legal perspective, what does HR need to know about using data analytics?" she asked.

The biggest challenge for predicting developments in the HR space is gathering the right high-quality data, Mrkonich noted.

Eigen cited HiredScore as a resource that uses predictive analytics to help businesses identify the right job candidates and reduce the cost per hire, as well as Cherry Tree Data Science, a vendor that offers tools to help employers safely hire applicants with criminal records. Employers can use this service as part of a second-chance hiring program for ex-offenders and to expand their applicant pool, he said.

Creative data analytics solutions can reduce biases in hiring, Mrkonich noted.

"Where someone went to school, good grades and references are terrible predictors of how well someone will do on the job," Eigen added. He said HR professionals need to question the status quo. "If your current methods are working, that's great—but you should also look at some alternatives to measure the success of your current methods, because old-school methods may be suboptimal," he said. "You have to test and compare methods to make sure they are working."

HR professionals should test key areas where there may be room for improvement, such as the company's performance evaluation process and turnover rate.

Experiment, test and retest, Eigen and Mrkonich said. Think about new ways to address these workplace issues.

Legal Risks

There are always legal risks involved in using data to help make employment decisions. Mrkonich said employers should be aware of those risks but shouldn't shy away from data analytics just because of them. There are ways to deal with those risks, just as there are ways to deal with privacy risks, he noted.

Some of the risks presented are the same as they are for other screening methods, such as applicant testing tools. "Don't move your own discretion to the backseat," Eigen said. If an algorithm tells you to hire only men, for example, you know not to do that, so review the data you gather using your knowledge of HR practices.

Employers should work with their attorneys and conduct analyses of their workplace practices under attorney-client privilege. If they find any discrepancies—for example if they perform a compensation analysis and find that men are paid more than women for substantially similar work—they should be prepared to address the issue.