The Benefits of Big Data: How Business Analytics Can Increase Inclusion

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According to the World Economic Forum’s 2018 Global Gender Gap Report, there is a 32% average gender gap in the workplace globally. While society has made progress toward achieving gender parity, there is still a long way to go.

To learn more, check out the infographic below created by the University of Maryland’s Robert H. Smith School of Business online MS in Business Analytics program:

How data analysis software could help to bring gender equality to the workforce more effectively.

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Gender Inclusion in the U.S.

In 2018, women’s median weekly earnings were 81.1% of men’s median weekly earnings. The wage gap across business occupations can vary significantly.

Statistics of Employed Women

In 2018, median weekly earnings across occupations tracked by the U.S. Bureau of Labor statistics were $789 for women, and $973 for men. There were also substantial gender gaps in the role women played in specific industries in 2017. For instance, women constituted 14% of the country’s civil engineers, 18.7% of the country’s software developers, and 24.3% of the country’s chief executives. BLS also pinpointed significant gender pay gaps in chief executive, personal financial advisor, and marketing and sales manager roles.

Addressing Bias

Though there are many reasons for the wide pay gap, bias is one that can be combined at many stages, including hiring.

Examples of Unconscious Bias

Recruiters are more likely to view the profiles of men than those of women. Additionally, women are 26% less likely to request a referral. Women also apply for 20% fewer jobs and believe they must meet 100% of hiring criteria. Men, on the other hand, believe they need to meet only 60% of the criteria.

Benefits of Gender Inclusivity

Calculations by the International Monetary Fund revealed that gender inclusivity increased the GDP by an average of 35%. The calculations also revealed inclusivity yields higher productivity, higher incomes for men, and greater returns on efforts to reduce gender barriers.

How Data & Analytics Can Be used to Reduce Bias in Hiring

While human resources departments can handle some steps, other steps will require tech solutions. Some of these steps include removing names and gender indicators like profile photos, creating standardized decision-making criteria, and identifying areas where bias impacts decisions.

Creating a Balance in Pay

Research by University of Maryland faculty Dr. Margret Bjarnadottir and Christian Dezso cautions against recalibrating “pay based on certain workforce qualifications, such as seniority and education,” as this could lead to giving raises to men and changing pay indicators, rather than achieving pay equity. Organizations should also avoid only considering cost-efficiency when providing raises, doing so could lead to paying less qualified women more than more qualified women.

Instead, they suggest organizations should use standard analysis to quantify the pay gap. They also suggest following HR strategies and policies for fair pay, and offering raises efficiently and reasonably.

Automating the Pursuit of Gender Inclusivity

Because many data collection, management and analysis processes are automated, data analysts should be aware of the challenges posed by artificial intelligence (AI). AI technologies, unfortunately, are prone to reinforcing society’s current gender biases.

Challenges in Using Data & Artificial Intelligence For Gender Inclusivity

AI is advancing faster than regulators, which is leaving many gray areas. Therefore, automated processes must be transparent and used appropriately. This means that AI technology must be screened for bias.

How Software is Using Data to Reduce Gender Bias

Managers can use tools that compare an employee’s tenure with his or her performance to check if the employee is being consistently passed over for projects and promotions, thereby identifying unconscious bias. Pipeline, a software company, has become especially popular among finance businesses, tech companies and restaurants.

How Pipeline Works

Pipeline’s software starts out by analyzing a company’s internal workforce data. It then scores a team’s gender makeup and an employee’s skills. Next, it analyzes performance review utilizing natural language processing to screen for gender bias. After this step is complete, the software provides recommendations for a performance review, promotion or salary-related change. Next, the software monitors and records the results of the decision-maker’s action. Finally, each company receives a gender equity score.

Pipeline is not the only tool using analytics to promote inclusion. Platforms like LinkedIn Recruiter, Hirevue, Pymetrics, and Textio Hire all use analytics as a process to ward off gender bias.

Tips for Leveraging Data Analytics to Reduce Gender Inequality

Data must be contextualized, which means that data analysts should be prepared to experiment with solutions to optimize data modeling. The data must also be actionable with enough highly detailed data to empower informed decision-making. Additionally, solutions must be customized for each use case. Finally, the sources of data must be varied and analyzed for bias.

Conclusion

Organizations are addressing inequality in the workplace using many tools and methods, including data analysis technology. In the coming years, the challenge for data analysts will be leveraging technology to eliminate bias and promote gender equality.