The pharmaceutical industry has come a long way in a short period of time. The Covid-19 pandemic has accelerated digital transformation plans and emerging technologies are finally being embraced for operations ranging from drug development to marketing.
However, despite these positive changes, pharma still lags behind other sectors in some areas.
One such area is gender diversity. Pharma still lacks any truly significant female representation within its work force, especially when it comes to data science. Across all sectors, only 15% of the world’s data analysts are female. Given the increased reliance of private and public sector organizations on accurate data collection, organization and interpretation, the role of a data analyst has becoming increasingly critical. So why are we not seeing more women take up these roles? There is a wider issue at play here – despite 55% of university graduates being women only one third of STEM graduates are female. Gender stereotypes and male-domination in the industry have contributed to this issue, as subjects in the STEM field are viewed as being masculine and associated careers are largely dominated by men. This culture of exclusion has led to a lack of female role models for young girls to look up to, which in turn deepens the cycle further.
Why is female representation so important for data?
A data analytic workforce with a diverse range of backgrounds is something that every business should aspire to, as it plays an important role in reducing the risk of bias. If all data scientists were the same gender and ethnicity, and had similar backgrounds, more bias would exist in findings due to a homogenous view in which data should be prioritized. That’s why AI systems require data scientists with a varied range of opinions and experiences to ensure that their data is analyzed fairly.
How do we address this lack of representation and avoid bias in the data that we collect?
At Talking Medicines, data analysts play a crucial role in the performance of our PatientMetRx® platform, providing customers with valuable actionable insights, putting patients at the center of their work. It is at the heart of everything that we do, so it is important that we make a concerted effort to avoid bias.
From the beginning, we have been conscious of bias that exists in healthcare data – our PatientMetRx® data platform isolates the patient voice at a medicine level with the aim of delivering better health outcomes for patients. Female representation in healthcare is important to us as we want to ensure that all patients are listened to, in order to improve everyone’s healthcare experiences.
As a company, we are proud to say that we have prioritized gender diversity in the workplace from day 1. Two of our founders and 60% of our employees are female, and we pride ourselves on fostering an inclusive and welcoming work culture.
We are acutely aware of the importance of both gender diversity at work but also of tackling the bias which is inherent in all data, we hope that we are blazing a trail for others in the industry to follow suit. As the role of a data analyst becomes more mission critical, it’s crucial that the profession better reflects some of the key demographics in our society.
References
Aston University Online. 2020. Why Women in Data Science Are Crucial In a Data-Driven World | Aston University Online. [online] Available at: <https://studyonline.aston.ac.uk/news/2020/11/23/why-women-data-science-are-crucial-data-driven-world#:~:text=Despite%20the%20importance%20of%20these,in%20a%20data%2Ddriven%20world.> [Accessed 1 August 2022].
Brazil, R., 2020. Why we need to talk about sex and clinical trials – The Pharmaceutical Journal. [online] The Pharmaceutical Journal. Available at: <https://pharmaceutical-journal.com/article/feature/why-we-need-to-talk-about-sex-and-clinical-trials#:~:text=In%201993%2C%20the%20FDA%20lifted,ensure%20women’s%20inclusion%20in%20trials.> [Accessed 1 August 2022].
AAUW : Empowering Women Since 1881. 2022. The STEM Gap: Women and Girls in Science, Technology, Engineering and Mathematics. [online] Available at: <https://www.aauw.org/resources/research/the-stem-gap/> [Accessed 2 August 2022].
BCG Global. 2022. What’s Keeping Women Out of Data Science?. [online] Available at: <https://www.bcg.com/publications/2020/what-keeps-women-out-data-science> [Accessed 2 August 2022].
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