I consider myself a multi-passionate professional working in data, with a growing interest in coaching. While I’ve always appreciated the clarity and structure of data, I’ve become increasingly curious about people: how they think, make decisions, and grow, so I’ve started to explore that through coaching.
These two paths come together in my work, especially in using data to support inclusion and visibility. I’ve 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.
I’m particularly interested in how data can be used not just to inform, but to challenge bias and create space for underrepresented voices.
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’t reflect everyone equally?
This is the core issue at the heart of the gender data gap, 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.
Why the Gender Data Gap Matters
A recent report by McKinsey & Company, Closing the Women’s Health Gap: A $1 Trillion Opportunity in Women’s Health, highlights the economic and human potential that remains untapped due to systemic gaps in how data is collected and used—particularly 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.
These differences are not always acknowledged in clinical studies, resulting in data that treats women’s symptoms as “atypical” 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.
This example illustrates a broader issue: when data doesn’t include diverse perspectives and experiences, the solutions we create may be incomplete or ineffective.
Understanding the Roots of the Gap
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:
1. Bias in Data and Systems
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.
2. Underrepresentation
In fields where women and gender-diverse individuals are underrepresented—such as STEM or leadership roles—the available data may not capture their experiences accurately. This leads to conclusions that may not apply universally.
3. Social and Cultural Barriers
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.
4. Gaps in Data Collection
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.
Towards More Inclusive and Accurate Data
Closing the gender data gap is not just a matter of fairness—it is a question of quality and effectiveness. Inclusive data leads to more informed policies, better-designed technologies, and more equitable outcomes.
There are several ways to begin improving how we approach data from a gender perspective:
• Design inclusive data collection practices
Ensure that data gathering reflects the diversity of the population and is sensitive to different gender identities and experiences.
• Use comparative analysis
Breaking down indicators—such as access to education, healthcare, or financial services—by gender can reveal important patterns and disparities.
• Rely on global benchmarks and research
Reports like the Global Gender Gap Report by the World Economic Forum offer valuable insights and help contextualise national or local data.
• Promote transparency and accountability
Institutions and organisations can help by being clear about how their data is collected, categorised, and used.
• Support data-driven policymaking
Accurate gender data is essential for developing laws, initiatives, and programs that promote equal opportunities in all areas of life.
A Shared Responsibility
The gender data gap is a shared challenge that requires collaboration across disciplines—data 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.
By building awareness and sharing knowledge, we can contribute to a more inclusive and informed future—one where data works for everyone.
Isabella Renzetti