Gender Gap in Data Analytics - The Common Forum

As businesses continue to flourish and develop across the globe, the root of their success lies in data analytics. There is no doubt that data-driven decision-making has become the standard for modern businesses. Data analytics enable businesses to extract valuable insights, identify patterns, and make decisions that drive innovation and optimize operations. However, “data” that is being used by numerous developing businesses is almost always inherently biased and excludes half the world’s population: women.

To begin, the gender gap in data analytics comes from the overall discrimination or some form of marginalization against women; shown through numerous cases from the past. For example when Aristotle, one of the world’s most famous philosophers, argued that men are the main gender and the prototype of a human.¹ Or more recently, when a 2007 international study found that only 32% of movie characters were female.² These instances of women being undermined in the past have translated into the way women are represented in today’s society, creating a gender data gap that roots from the unseen struggles that women face day to day.

Historically, societal norms and biases have shaped research methodologies, data collection practices, and decision-making processes, often prioritizing the male perspective and experience. This bias has led to a lack of female representation in research studies, surveys, and data collection efforts that is seen today. Consequently, the resulting datasets and information have offered skewed understanding of the world and a neglect of women’s specific needs, challenges, and contributions. Corporations continuing to use this data will continue to harness their business based on the data that mainly represents males. This gender data gap in data analytics is further extended by systemic inequalities of women. Limited access to resources and the underrepresentation of women in positions of power and influence perpetuate the everlasting cycle of exclusion and reinforce the existing biases.

The workplace is a prime example of how data can exploit women. A 2010 US study on the imbalance between the amount of unpaid work done by male and female scientists found that female scientists do 54% of the cooking, cleaning, and laundry in their households, adding more than ten hours to their nearly sixty-hour work week. While men’s household contributions (28%) only add half that time to their week.³ Unfortunately, it is not surprising that the occurrence of mothers juggling both family caregiving responsibilities and full-time employment is more common compared to men undertaking these roles. As a result, women end up working in jobs below their skill level that offer them the flexibility they need — but not the pay they deserve. To the data analysts, it is easy to see the statistics of the ratio between female to male in part time jobs and conclude that women are just not as capable. What the data doesn’t include is the fact that all the unpaid work that gets done around men, is done by the women. The data doesn’t show that the reason a mans works longer than his female coworker or doesn’t need Fridays off is not that he’s better than his female co-worker, but rather that, unlike him, she doesn’t have a full-time partner at home to do household chores. It should be encouraged for companies to start recognizing, valuing, and structuring the paid workplace to account for the underappreciated work that so many women put up with.

Another issue that data and statistics can cause is the lack of female representation in positions of power. In business, a vast group of start-ups are backed by venture capitalists, the issue being that 93% of them are men.⁴ Unsurprisingly, it was found by a study in 2018 that female-owned businesses receive half the level of investment that their male counterparts get.⁵ Given the male domination of venture capitalists, data gaps are particularly problematic when it comes to tech aimed at women. Women in this field often face the problem of ‘pattern recognition.’ This is the concept that investors base their decisions on successful investments in the past and compare its components with the current. Unfortunately, for women that means, almost 100% of the time, they will not fit into the ‘pattern.’ Once again, data has prevented women from reaching their maximum potential.

While data analytics have become integral to the success of businesses worldwide, it is essential to recognize the inherent biases and limitations within the data being used. The gender data gap, rooted in historical discrimination and marginalization against women, results in the exclusion of women’s experiences and needs from important decision-making processes. This ongoing exclusion perpetuates systemic inequalities, hindering female representation in positions of power and limiting opportunities for women in the workplace, further misleading data and creating a false representation of women.

The workplace serves as a stark example of how data exploitation causes women to have unpaid domestic work, often overshadowing their professional endeavors. Additionally, the lack of female representation in positions of power and the disproportionate investment received by female-owned businesses highlight the biases embedded in data-driven decision-making. To address these issues, it is crucial for society to recognize, challenge, and rectify the gender data gap through inclusive data collection practices, policy changes, and promoting diversity and equal opportunities. By doing so, we can foster a more equitable and inclusive society that harnesses the full potential of women’s contributions, thus enabling them to drive meaningful change in all aspects of life.

Cover Image: Photo by Stephen Dawson on Unsplash

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