Bridging The Gender Data Gap

By not considering female data, organisations are failing women… And themselves

Women make up around half of the global consumer market. Despite this, and sadly unsurprisingly, products and services across the scale are biased towards men. Equipment, safety systems, public amenities and even technology has been shown to favour the average male, whether that be the size of smartphones or the availability of public toilets. This isn’t because organisations are knowingly sexist. It’s because, on the whole, women are seriously underrepresented in data. Now that data can be collected and analysed at scale, there can be no excuse for the gender data gap.

Addressing the data discrepancy

In the tech world, there are a number of examples of products that have seemingly ignored female users. Take consumer electronics. Women are more likely to own an iPhone than men, but the average smartphone screen is 5.5 inches. While the average man could comfortably use a phone of this size with one hand, the average female would struggle. This doesn’t seem to phase manufacturers, who seem set on bringing out bigger screens. Similarly, research by University of Washington fellow Rachael Tatman found that Google’s speech recognition software was 70 per cent more likely to accurately recognise male speech.

Not only is this a blatant disregard of gender equality… It’s a missed opportunity. Failing to consider female data in the design of products and services is, from a business perspective, illogical. In order to make the most of the consumer market, businesses need to close the gender data gap.

The lack of data about women and girls isn’t just a boardroom issue – it’s a matter of life and death. In Invisible Women: Exposing Data Bias in a World Designed for Men, Caroline Criado Perez shared the harrowing statistics that women are 47 per cent more likely to be seriously injured in a car crash than men. They are also 17 per cent more likely to die. But why? It could have something to do with the fact that since the 1950s, car crash test dummies have been modelled on a 76kg, 1.77m male. As such, the safety systems in cars don’t account for smaller, lighter drivers, and women may be more likely to be injured because of biased safety measures. Perhaps it’s also no coincidence that women are more likely to get injured in combat training… Especially since their personal protective equipment (PPE) may have been designed for male bodies.

From product to policy

The gender data gap is affecting policy as well as products. It’s not difficult to see why. Household surveys, for example, focus on the ‘head of the household’. This is traditionally thought of as the senior male, so female respondents are often overlooked. Female labour is another interesting area. Women certainly carry out manual labour tasks, but they are less likely to do so in institutionalised environments or within a nine to five timeframe. As a result the work is rarely recorded, leading to the assumption that women carry out less manual work than they do in reality. If policy makers are blind to the true activities of women, then the policies they make will reflect their ignorance. This is equally true of any government or corporate organisation.

A universal responsibility

One group hoping to address the gender bias in data is the Population Council, through a toolkit called the Girl Roster. Working across Africa, Asia, Central America and the Middle East, the Girl Roster aims to collect information about adolescent girls who often fall under the data radar. Gathering data about women and girls is hoped to identify sources of inequality, driving economic and social progress. However, generating data isn’t just the responsibility of single, dedicated organisations. There should be a universal recognition that women make up half of the world’s population, and have specific requirements that must be met. Addressing these needs is vital, partly because it improves the commercial viability of products and services, but also because it encourages gender equality.

The gender data gap is a clear indicator of the bias inherent in data. Admittedly, in most cases, failing to collect and apply data about women has not been deliberate. Until recently, the gender data gap could be viewed as a problem of bias rather than outright sexism. Today, however, it’s much harder to ignore the data discrepancy due to the amount of information that can be gathered and, perhaps more importantly, publicised. It’s unethical, but also unfair, to fail to use diverse data. Why should a woman, or anyone, pay for a service (like Google’s voice recognition technology) that doesn’t work as well for them as for a man?

A business that routinely fails to account for women in its products and services is likely to face consumer backlash, which will ultimately punish sales. The point isn’t that companies need to embark on an aggressive campaign to ‘do more for women’. Instead, they have a responsibility to consumers and shareholders to recognise that not all data is created equal, and to challenge the data culture that has so deeply disadvantaged women.

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