I consider myself a multi-passionate professional working in data, with a growing interest in coaching. While I\u2019ve always appreciated the clarity and structure of data, I\u2019ve become increasingly curious about people: how they think, make decisions, and grow, so I\u2019ve started to explore that through coaching. <\/p>\n\n\n\n
These two paths come together in my work, especially in using data to support inclusion and visibility. I\u2019ve mentored at a UN Women datathon in Albania and recently spoke at the Women in Data Flagship Event in London, where I discussed how data and AI can help uncover gender disparities in healthcare.<\/p>\n\n\n\n
I\u2019m particularly interested in how data can be used not just to inform, but to challenge bias and create space for underrepresented voices.<\/p>\n\n\n\n
In the digital age, data plays a central role in how we design services, allocate resources, and make decisions that shape our societies. We rely on data to build technology, improve healthcare, understand economies, and address social challenges. But what happens when this data doesn\u2019t reflect everyone equally?<\/p>\n\n\n\n
This is the core issue at the heart of the gender data gap<\/strong>, a term that refers to the lack of accurate, complete, and gender-inclusive data across many fields. This gap is not just a technical limitation. It can directly influence the effectiveness and fairness of decisions that affect millions of lives.<\/p>\n\n\n\n Why the Gender Data Gap Matters<\/strong><\/p>\n\n\n\n A recent report by McKinsey & Company, Closing the Women\u2019s Health Gap: A $1 Trillion Opportunity in Women\u2019s Health<\/em>, highlights the economic and human potential that remains untapped due to systemic gaps in how data is collected and used\u2014particularly in healthcare. The report draws attention to the fact that many common health conditions, such as cardiovascular or autoimmune diseases, affect women differently, yet medical research often defaults to male-centred models.<\/p>\n\n\n\n These differences are not always acknowledged in clinical studies, resulting in data that treats women\u2019s symptoms as \u201catypical\u201d or less urgent. This can lead to under-diagnosis, delayed treatment, and lower-quality care. The report estimates that addressing these gaps could lead to significant improvements in health outcomes and generate substantial value for societies and economies.<\/p>\n\n\n\n This example illustrates a broader issue: when data doesn\u2019t include diverse perspectives and experiences, the solutions we create may be incomplete or ineffective<\/strong>.<\/p>\n\n\n\n Understanding the Roots of the Gap<\/strong><\/p>\n\n\n\n To address the gender data gap, it is essential to understand where it comes from. In many cases, several interconnected factors contribute to the issue:<\/p>\n\n\n\n 1. Bias in Data and Systems<\/strong> 2. Underrepresentation<\/strong> 3. Social and Cultural Barriers<\/strong> 4. Gaps in Data Collection<\/strong> Towards More Inclusive and Accurate Data<\/strong><\/p>\n\n\n\n Closing the gender data gap is not just a matter of fairness\u2014it is a question of quality and effectiveness. Inclusive data leads to more informed policies, better-designed technologies, and more equitable outcomes.<\/p>\n\n\n\n There are several ways to begin improving how we approach data from a gender perspective:<\/p>\n\n\n\n \u2022 Design inclusive data collection practices<\/strong> \u2022 Use comparative analysis<\/strong> \u2022 Rely on global benchmarks and research<\/strong> \u2022 Promote transparency and accountability<\/strong> \u2022 Support data-driven policymaking<\/strong> A Shared Responsibility<\/strong><\/p>\n\n\n\n The gender data gap is a shared challenge that requires collaboration across disciplines\u2014data science, public policy, healthcare, education, and beyond. Recognising the gap is only the first step. To address it, we must commit to developing tools, methods, and practices that reflect the full reality of the world we live in.<\/p>\n\n\n\n By building awareness and sharing knowledge, we can contribute to a more inclusive and informed future\u2014one where data works for everyone.<\/p>\n\n\n\n Isabella Renzetti<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" I consider myself a multi-passionate professional working in data, with a growing interest in coaching. 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Bias can occur unintentionally when data reflects historical inequalities or when algorithms are trained on unbalanced datasets. If systems are built on biased data, they risk reinforcing existing disparities.<\/p>\n\n\n\n
In fields where women and gender-diverse individuals are underrepresented\u2014such as STEM or leadership roles\u2014the available data may not capture their experiences accurately. This leads to conclusions that may not apply universally.<\/p>\n\n\n\n
Real-world inequalities, including access to education, employment, or healthcare, often limit participation. These structural barriers can result in missing data or skewed datasets that do not reflect the full population.<\/p>\n\n\n\n
In many cases, data related to gender is either not collected or is collected in overly simplistic ways. This reduces the ability to perform meaningful analysis and can make certain issues invisible in public or private decision-making.<\/p>\n\n\n\n
Ensure that data gathering reflects the diversity of the population and is sensitive to different gender identities and experiences.<\/p>\n\n\n\n
Breaking down indicators\u2014such as access to education, healthcare, or financial services\u2014by gender can reveal important patterns and disparities.<\/p>\n\n\n\n
Reports like the Global Gender Gap Report<\/em> by the World Economic Forum offer valuable insights and help contextualise national or local data.<\/p>\n\n\n\n
Institutions and organisations can help by being clear about how their data is collected, categorised, and used.<\/p>\n\n\n\n
Accurate gender data is essential for developing laws, initiatives, and programs that promote equal opportunities in all areas of life.<\/p>\n\n\n\n