Who gets counted? Power, data, and the people left out

We live in a country obsessed with data – GDP numbers, exam results, cricket stats. But when it comes to real lives, especially those of women, LGBTQ+, Dalit, Adivasi, disabled and other marginalized communities, the numbers are full of blanks. And what isn’t counted rarely counts.

Let’s be clear: Data isn’t just a development tool, it’s political currency. It decides who gets seen, who gets served, and who gets sidelined. And right now, that system is failing far too many.

In a world driven by dashboards and targets, most data systems still revolve around a narrow, outdated idea of the “default human” – male, cisgender, urban, and privileged. Everyone else? Either generalized into “others” or conveniently left out.

Missing people, missing truths

Let’s start with the basics. Globally, only 42% of the gender-specific data needed to track the Sustainable Development Goals (SDGs) is available. That means over half the data we should be collecting, on issues like gender-based violence, access to reproductive healthcare, unpaid care work, or LGBTQ+ discrimination, simply doesn’t exist.

In India, this plays out across every sector. Consider health: most studies still focus on cisgender male bodies. We still lack large-scale data on trans health access, intersex medical care, or even mental health in LGBTQ+ youth. In education, we rarely disaggregate dropout rates by gender identity or caste. In employment surveys, informal work by women, trans people, or persons with disabilities is either miscounted or erased.

In short: we’re measuring a fraction of reality and calling it evidence-based policy.

Who designed this system?

The answer is unsurprising. Most of our data institutions were shaped during colonial or patriarchal nation-building moments – designed for control, not care. These systems weren’t built to count everyone, especially not those at the margins of caste, gender, sexuality, class, or ability.

Traditional economic metrics like GDP ignore unpaid care work, disproportionately done by women and girls, especially from lower-income and marginalized caste communities. Public infrastructure projects often skip input from disabled or trans communities, leading to hostile and inaccessible environments. Even transport planning revolves around male commute patterns, ignoring the lived reality of those making multi-stop journeys or traveling at “off-peak” hours for caregiving or informal work.

It’s not a glitch. It’s a design flaw. And it reproduces inequality, year after year.

The feminist (and LGBTQ+, anti-caste) pushback

Feminist data isn’t just about “adding women in.” It’s about reimagining what we measure, why, and for whom. It challenges the idea of objectivity that erases lived experiences, and demands that the margins move to the center.

In India, we’ve seen slum-dwelling women mapping their own neighbourhoods, documenting where streetlights don’t work, where harassment is frequent, where toilets are missing. In Tamil Nadu, trans collectives have started gathering their own community data on access to housing, ration cards, and healthcare, because no one else is doing it. These aren’t just anecdotes. They are data points born from struggle, and rooted in lived reality.

And globally, initiatives like #VisibleWikiWomen and Equal Measures 2030 are expanding what gets counted – from the number of women in politics to visibility gaps in open data platforms.

This is data as activism. Data as resistance. Data as dignity.

Technology: opportunity or trap?

The explosion of big data, AI, and algorithmic governance presents both potential and peril. Sure, we can now analyze social media sentiment to detect misogyny or track migration flows through mobile usage. But we’ve also seen how facial recognition fails on dark-skinned women and trans folks. Or how hiring algorithms “learn” to reject female or LGBTQ+ coded resumes. When tech is built on biased data, it scales discrimination.

What we need is not just diversity in datasets, but diversity in data teams – LGBTQ+ coders, Dalit researchers, feminist technologists, disability rights advocates, all shaping what gets measured and how it’s interpreted.

Because if AI is the future of policy, inclusive data is our firewall.

The money problem: glamor vs groundwork

Let’s talk money –  or the lack of it. Funding for gender statistics fell by more than 50% between 2019 and 2020, globally. In India, our statistical infrastructure is overstretched and underfunded. Vital surveys – on domestic violence, time-use, or LGBTQ+ realities are delayed, deprioritized, or missing entirely.

This isn’t an accident. It’s a reflection of what systems value. Flashy dashboards get headlines. Community-run data audits? Not so much.

But if we’re serious about justice, we must fund data as public infrastructure, on par with roads, electricity, or healthcare. Because data is how we see, and if we don’t see everyone, we build for no one.

Changing the narrative with numbers

We’ve all heard the stereotypes:
“Trans people don’t want jobs.”
“Girls just aren’t into STEM.”
“Women-owned businesses aren’t scalable.”
“Dalit women are too ‘traditional’ for leadership.”

Let’s be honest, these are lazy myths upheld by lazy data.

What the numbers actually show: women-led startups often grow faster but get less funding. Trans entrepreneurs face legal and social hurdles, not lack of ambition. Marginalized caste women lead movements, mentor youth, and sustain entire economies in rural India, but they don’t show up in LinkedIn trends.

Data, when done right, dismantles lies. It gives us proof, not pity. And it makes injustice undeniable.

So, what do we do?

It starts with demanding more from our data systems. Not just “gender disaggregation” – but caste, disability, sexuality, age, geography, and gender identity. We need:

• Participatory research that trains local communities to collect and interpret their own data
• Intersectional national surveys that go beyond binaries and include trans, non-binary, and intersex persons
• Time-use data that captures unpaid labour by caste, class, and region
• Youth and gender diverse advisory panels in data governance bodies
• And a national, open-access intersectional data dashboard – visual, transparent, and regularly updated

Because justice doesn’t begin in the courtroom or Parliament, it begins in the dataset.

Beyond visibility, toward power

Feminist, anti-caste, and LGBTQ+ movements have long fought for representation, but visibility alone isn’t enough. We need accountability. We need the numbers that prove the system is broken, and the ones that show how to fix it.

So yes, we need to count women. But we also need to count queer women, Dalit women, trans men, disabled non-binary folks, tribal girls, and every identity that defies the “default human.”

Because data isn’t neutral – it’s power. And until we democratize that power, equality will stay a promise we keep breaking.

This is the final chapter, but not the end

This piece marks the final article in my Unfinished Business series – a journey through the gendered cracks in our systems. If there’s one thing this series has made clear, it’s this: equality isn’t a checklist we’ve completed, it’s ongoing, uncomfortable work. The business remains unfinished, and it’s on all of us to keep asking: who’s still missing, and what will it take to count them in?

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