4 Ways HR Analytics Can Improve Workplace Diversity

The U.S. has always been known as a melting pot; diversity is its strong suit. However, when it comes to corporate America, diversity has historically been lacking. Although 92 percent of U.S. population growth is attributed to ethnic groups and 36 percent of the workforce is comprised of people of color, only 21 minorities (that's right—21, not 21 percent) are Fortune 500 CEOs.

Fortunately, this norm is changing as more minorities are becoming key consumers, clients and leaders in the workforce. In the next 10 to 30 years, census data says that there will be no racial or ethnic majority in the United States. Projections also say that by 2020, minorities will make up 40 percent of the civilian labor force.

It's time for HR leaders to embrace the changing demographics—and thus, usher in a new era of innovation.

Plain and simple, a diverse talent pool leads to diverse ideas. There are multiple studies showing that diversity improves organizational bottom lines: McKinsey quarterly reported that between 2008 and 2010, companies with more diverse teams were top financial performers, and according to a study by Lu Hong and Scott E. Page, groups of diverse problem solvers outperform groups of high-ability problem solvers.

However, after years of trying to promote diversity by eliminating bias and discrimination in the workplace by legal means, it still exists. So, what can HR leaders do to combat ongoing bias?

Using big data for HR (predictive analytics, talent analytics, HR analytics and human capital analytics) may be the solution to cutting out discrimination and bias while fully embracing the demographic shift.

HR analytics is not simply about raw data; it's about what insights that raw data can provide to answer questions relevant to your staff. While HR analytics may look to the past for information, its main function is to shine a light on current behavior and predict future behavior.

Here are four questions HR analytics can potentially answer to help organizations move past discrimination and bias:

1) What variables influence our compensation structure?

Without in-memory technology, all HR data—turnover rates, salaries, employee demographics, lists of available positions, etc.—was stored on different disks in a database. If you wanted to compare salaries to turnover rates and gender, you'd need to first locate the data, then retrieve the data from different disks before you even begin analyzing; the whole process could take weeks.

In-memory analytics speeds up the process with faster, cheaper and more powerful memory chips that can be put in the server's memory rather than the database. That means complex data can be controlled and manipulated almost in real-time. For example, when deciding on performance bonuses, HR can quickly run a report detailing the twenty-year history of performance bonuses compared to years worked, department revenue, revenue by location, gender and male:female ratio. Patterns of bias in the past can be easily identified, prompting bonus structures based on solid data.

2) Who's likely to resign?

Organizations can use predictive analytics to determine future behavior as well, such as identifying employees at risk for resigning. Recruiting diverse talent is one thing, but if your minority talent has a high voluntary turnover rate, you haven't done much to improve the diversity of your workforce. Predictive analytics can look at specific populations to determine who is likely to resign, and HR can use that information to create initiatives to improve the work experience of those populations.

3) Will a candidate feel welcome at your company?

Using data can also help companies identify the core values and behavioral traits of candidates—and vice versa. For example, survey company Saberr uses algorithms to compile, process and compare fundamental values, behavioral compatibility and diversity to predict the potential strength of interpersonal relationships between certain applicants and potential employers. They do this with a survey for both the applicant and the employer that moves past skills and credentials, thereby bypassing initial bias in the hiring process.

4) Do we really need to address this issue?

Perhaps the most impactful use of HR analytics is presenting data visually to easily demonstrate an issue and influence decision-makers. Data can be presented in graphic and statistical reports that are easy for leaders to understand—and take action on. For example, let's say 45 percent of your job candidates are people of color, yet only 3 percent of the hires are minorities. If leadership just isn't seeing the big picture when you explain it verbally, presenting the hard facts in a way that is straightforward, easy to understand and irrefutable may be the only way to enact change.

Examples like these give us just a glimpse at the potential of big data to enhance the effectiveness of HR leaders. However, data is not the solution in and of itself—you need to ask the right questions. Minority candidates have been employed within a culture of systemic discrimination from the start, which often influences their work history. Simply taking names off of a resume and evaluating candidates by job title and education will only perpetuate the problem.

HR professionals need to be careful to keep the human in human resources. If the right questions are asked, data-driven decision-making will prove to be a powerful ally to HR professionals working to reflect our country's rich culture diversity in the U.S. workforce.

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