Some months ago, a LinkedIn post showcasing an excerpt from the Chasing Financial Equality podcast with Cindy Galop stopped me in my tracks.
I didn’t know who Cindy was. Later, I discovered she’s a brand and business innovator, consultant, coach, and keynote speaker who participated in the UK Apprentice. She’s been building a business out of teaching sex and she’s also a women’s entrepreneur advocate.
Still, that one-minute video in my feedback was so powerful that I didn’t care who was speaking.
“F*ck data. Data does f*ck all.
We have literally for decades had the data you reference that says female founders exit faster, female founders burn less cash, female founders get to profitability quicker, female founders build better business cultures, but none of that data makes any difference.
[…] Information goes through the heart, not the head. It’s not about rationality. It’s about emotion.
The reason women don’t get funded is due to plain old-fashioned sexism and misogyny.”
My background is in engineering and computer simulation and I’m Director of Scientific Support and Customer Operations for a tech corporation. I’m also a diversity and inclusion advocate. I’ve been using data for 30 years for everything I’ve done.
Using simulation to guide the development of new materials, leading the migration of all our customer support data after an acquisition, monitoring customer satisfaction KPIs, supporting the business case for enhanced maternity leave in the company I work for, and surveying professional women about the impact of COVID-19 on their unpaid work are only a few examples.
Still, Cindy’s post triggered an epiphany.
I began to recall all the ways data — or its absence — has been manipulated to foster gender inequality. From entrenching the status quo to promoting “busy work”, wearing out activists, or even benefiting those who profit from inequality.
Let’s show you what I found.
Gender Data Myths
“In God we trust, all others bring data.”
Data has been heralded as the key to innovation, solving systemic issues, and exponential growth (Big Data anyone?). We “just” need data, don’t we?
In theory, women have accounted for half of the population throughout humanity. We should have collected millions of data points over millennia. How come we haven’t solved gender inequality yet?
Because we’ve been using data against women.
At a time when we abide by the creed “data is the new oil”, it cannot be a coincidence that we’re solving this “data problem”
Here are the 7 ways data is weaponised against gender equity.
Lack of data
In the absence of data, we will always make up stories.
Recorded historical contributions to science and humanities — medicine, literature, chemistry, philosophy, politics, or engineering — have XY chromosomes.
From that “data”, the world feels very comfortable making up stories about the reasons why “progress” has been driven by men. If we have data, we must have a story about it.
The story we’re told about the lack of data on women’s contributions is that women haven’t contributed. Yes, for millennia, women were just in the background waiting for men to learn about fire, cure their children, or bring money home.
We’ve also told ourselves that the proof that indeed there are very few women who wrote books (George Eliot), won a Nobel Prize (Marie Skłodowska-Curie), or spurred the creation of a National Health System (Florence Nightingale) proves the negative: If women had contributed more, it’d be recorded.
Or maybe, who gets credited has something to do with the fact that history was written by privileged men.
I’ll share three examples
- Nobel prizes are known only for recognising the work of male scientists when the research was performed in partnership with their female wives or students.
- Female healers and midwives using herbs were the real medicine trailblazers, both creating remedies and developing healthcare practices. Did we recognise them? No, we burnt them as witches.
- Michelin-starred chefs like Mark Dodson, Alfred Prasad, Antimo Maria Merone or Massimo Bottura credit their mothers and grandmothers for their skills and recipes. But who gets in the history books?
How do you prove that women contributed to innovation if there is no trace? You don’t.
Women Are No Men
“Without a standard there is no logical basis for making a decision or taking action.
“Average” is a masculine word. When we want to refer to all humanity, we often use words like “men” or “mankind”.
Don’t believe me? Last year, I was involved in performing a Root Cause Analysis (RCA) — a method of uncovering the causes of problems to identify appropriate solutions — after a customer escalation. The first step was to create a fishbone diagram to highlight the potential causes of the event.
The headlines were labelled as
- Man
- Machine
- Measurement
- Method
- Management
Interestingly, the originator was a woman and other women — besides me — were supposed to contribute to the document. Of course, I pushed back on the nomenclature and the template got updated from “man” to “people”.
Still, for a company that has run RCA for years, it’s shocking that nobody appeared to have challenged the “old-school” template before. Moreover, it goes without saying that if it had been changed to “women” everybody would have thought that was not inclusive enough!
But it’s not only about naming conventions. The result of men being considered the average and women a 4 billion outliner is that data models are often not designed to capture women’s experiences.
For example, it took Apple and Fitbit years to incorporate menstruation into their fitness apps and, once they did it, do it under the male gaze
Fitbit confirmed to the BBC that “currently a period must be less than 11 days” [and longer periods cannot be recorded].
Were women not menstruating during all those years? They were but tech companies didn’t see a financial upside in recording that data at that time.
It’s a no-brainer that we don’t have more data on women when we purposely exclude them from the means to communicate their experiences, contributing to epistemic injustice — the injustice done against someone “specifically in their capacity as a knower”.
If we don’t record women’s experiences — menstruation, miscarriage, abortions, menopause, mansplaining, hepeating, manterrupting, manels— we don’t have to act on them, do we?
Data Manipulation
“You get what you measure. Measure the wrong thing and you get the wrong behaviors.”
We know that women’s health outcomes are worse than men’s. Yes, women live longer than men in many countries but their quality of life is consistently worse.
“Women spend 25 percent more of their lives in poor health compared to men, yet millions of women around the world can’t access the health care they need. Globally, we lose nearly 800 womenevery day to preventable pregnancy- or childbirth-related complications. More than 1 billion women and girls suffer from malnutrition, and more women than men are living with depression and anxiety.”
Still, every year the World Economic Forum (WEF) tries to convince us that gender equality in health outcomes has been achieved.
WTF!?! I meant, how? With data!
Albeit, a very specific kind of data. In their Global Gender Gap Reports, the WEF “decided” — a magic word that tells us a lot about power imbalance — that gender equality in healthcare is calculated by the contribution of two variables
- 70% is survival at birth
- 30% is healthy life expectancy
That is, 70% of gender equality depends on whether female fetuses are killed or not when compared to men. Under that metric, the WEF claims in their 2024 report that “the Health and Survival gender gap has closed by 96%” and it’s the smallest of the gender gaps across all dimensions analysed.
That overreliance on birth rates and the fact that men are more prone to die earlier from violence in countries in conflict are the reasons we see countries like El Salvador or Uganda are at the top of the gender gap index, whilst countries like Sweden, Norway, and Iceland appear to be at the queue of gender equality in health…
How does this capture that it takes an average of 8 to 12 years to diagnose endometriosis? Or that many countries ban abortion and can be punished with up to 50-year prison sentences? Or that worldwide, nearly 75% of all hip fractures occur in women (International Osteoporosis Foundation)?
Data Stalling
Data by itself is useless . . . You can’t pour data on a broken bone and heal it. You can’t pour data on the street and fix it. Data is only useful if it is applied for useful public benefit.
Todd Park, US CTO (2012–2014)
Throwing around the seemingly harmless sentence “We need more data” is a great tool to stall initiatives to decrease gender equity. How?
If you try to build a business case to reduce gender equality, there is no better way to postpone action than asking for more data to confirm what we already know.
Do we really need more data to substantiate that
- in the UK, Black women have much worse birth outcomes than White women
- menstruations are extremelly painful for many women
- gender violence is mainly exerted by the victims’ partners and family
- or peri and post-menopause are very disruptive for a lot of women
before we act?
I rest my case.
Data Profiteering
Insanity is doing the same thing over and over again, but expecting different results.
Do you need a new article or scientific paper? Easy. Just repeat a survey on something we know about gender inequality and label it as a “New study”. Is not that great?
You don’t need to provide solutions or critique inaction. You simply rerun the survey, present the data using a different chart, or “forget” to acknowledge previous similar studies and you’re done.
Moreover, you will look — and feel — awesome about your good deed. And I know it because I have that T-shirt.
As a diversity, inclusion, and equity strategist, I’ve spearheaded and supported many initiatives to drive gender equality. Unsurprisingly, all of them started with some kind of diversity survey. Organisations ask for it and governments do too (e.g. the UK Gender Pay Gap reporting). We repeat forever hoping to mislead the public into believing that “data” equals “results”.
And the media profits from it too. Gender diversity reporting is often a money machine: We rehash the same topics over and over knowing that they’ll polarise the audiences and drive engagement.
For example, we’ve known for years that women receive less assistance for cardiac arrest by bystandards. Still, it continues to make the news from time to time.
The research by St John Ambulance said nearly a quarter of the 1,000 Britons surveyed admitted they were less likely to perform cardiopulmonary resuscitation (CPR) on a woman in public, with a third of men worrying they would be accused of “inappropriate” touching when giving CPR to a woman compared with 13% of females.
[…] Previous research has shown that more than 8,200 women in England and Wales could have survived a heart attack if they had been given the same treatment as men, according to an analysis of data from between 2003 and 2013.
Why does it work? Because it makes everybody happy. The ones requesting the data know they won’t find anything they didn’t know. For example, if you don’t see women managers in your organisation, the survey isn’t going to magically create them. Still, you look great because you paid for the survey.
For the same reason, DEI consultants know that the “D” on DEI is the easiest money.
Next year, rinse and repeat!
Data Complexity
“All animals are equal, but some animals are more equal than others.”
George Orwell, Animal Farm
As discussed above, collecting women’s data is usually not a priority. And on the few occasions we do, it’s important to label it as“special”, “complex”, or “puzzling” so everybody understands it stays from “the norm”.
As Caroline Criado Perez profusely demonstrates in her book “Invisible Women”, the patriarchy has weaponised “complexity” against women data’s for decades.
And we have examples all over the place.
For example, women were excluded from clinical trials for many years with the excuse that hormones made the results complex to analyse (SCOOP! Men also have hormones).
As a result, most drugs have been developed without including female data or — often the case now— sex disaggregation. We have consistent data confirming that the current drug doses are harmful to women — who often receive too much or not enough of the active ingredients. Still, the only acknowledgement to women on drugs’ leaflets is the warning for pregnancy and breastfeeding — not medicine dosage.
When will we stop using gender biological differences as a plausible excuse to exclude women’s data?
Data Snub
“What gets measured gets done.”
Finally, there are cases where simply we gaslight girls and women.
Suicide is deemed a male issue in Western societies. For example, men are significantly more likely to die by suicide than women in the UK, US, and Canada — a trend that has held true for decades.
However, what is much less publicised is the well-documented research that shows that women attempt suicide at higher rates than men. For example, in 2022, in the US men died by suicide 3.85 times more than women but suicide attempts happen 10 times more often than suicide deaths in women.
And it’s not only the quantity, but how those attempts are rated that is gendered. Suicide attempts by men are rated as ‘Serious Suicide Attempt’ more frequently than in women. Why? Because the rating of ‘Serious Suicide Attempt’ for men might be assigned according to the methods chosen, rather than differences in suicidal intent.
So, because women are less “successful” at dying by suicide — even if they perform more attempts than men — we’ve decided is a “men’s problem”.
While this ‘gender paradox’ in suicide rates is well documented — men are significantly more likely than women to take their own lives although they are less likely, compared to women, to experience suicidal thoughts and to attempt suicide — the reasons behind it are complex and remain only partially understood.
This burial of women’s experiences of suicide doesn’t benefit men. It actually backfires. For example, some research hypothesises that men hide their failed attempts more than women because the stigma attached to discussing suicidal thoughts or ‘failing’ to complete suicide is associated with weakness, which may prevent reporting among men.
And gender violence has a special place in this section.
On average, a woman is killed by a man in the UK every three days. In the UK, a survey of families of women killed by men found that 78% of respondents had suffered a history of abuse that was reported to authorities. Police and courts continue to ignore partner gender violence, even within their ranks.
But it’s not only women that are affected by gender violence. Children who witness domestic abuse are at risk of mental health problems, such as becoming anxious or depressed. Low mental health can also lead to big impacts on physical health, including self-harm or developing an eating disorder, having a lowered sense of self-worth, using alcohol and other drugs as unhealthy coping mechanisms, and repeating behaviours seen in their domestic setting.
What’s the evidence that disregarding women’s qualitative and quantitative data makes us progress towards a safer society for men?
If Not More Data, Then What?
“Data without a decision is a distraction”.
We don’t need more data to prove that Black women are underrepresented in leadership positions. We don’t need more reports to show us that women’s startups outperform men’s. Or that women’s education and access to family planning would unlock trillions of wealth.
Still, every year we dedicate massive effort towards generating, requesting, and sharing data on those topics. It’s data procrastination.
We know the problems and have the data. The issue is that we don’t like the solutions because they irrevocably point to a massive upending of a gender power asymmetry lasting millennia.
What if next time we’re asked for more “data” to build a business case about gender equality we’d ask what has been done about the data already collected and what hasn’t been effective?
What if we realised that data is only the end of the beginning?
Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning.
WORK WITH ME
I’m a technologist with 20+ years of experience in digital transformation. I’m also an award-winning inclusion strategist and certified life and career coach.
- I help ambitious women in tech who are overwhelmed to break the glass ceiling and achieve success without burnout through bespoke coaching and mentoring.
- I empower non-tech leaders to harness the potential of AI for sustainable growth and responsible innovation through consulting and facilitation programs.
- I’m a sought-after international keynote speaker on strategies to empower women and underrepresented groups in tech, sustainable and ethical artificial intelligence, and inclusive workplaces and products.
Contact me to book a call and discuss how I can help you achieve the success you deserve in 2025.