Why Diversity in Data & AI Matters – And What We Can Do About It — Little Miss Data
Why Diversity in Data & AI Matters – And What We Can Do About It

Why Diversity in Data & AI Matters – And What We Can Do About It

Recently, I contributed to a study by KPMG LLP, WLDA TECH, and Revelio Labs on the career outlook for women in data, analytics, and AI. The results were a real eye-opener, even after decades in the field. With only 39% of the workforce in data, analytics and AI being women, and 77% of executives saying Generative AI (GenAI) will have an outsized impact on society, there’s a lot at stake. We need to attract and retain talent from all backgrounds, not just to fill roles, but to make sure the systems we build are fair, inclusive, and reflect the diversity of the world they’re meant to serve.

Data isn’t just a tech thing—it’s the backbone of every industry, driving decisions and sparking innovation across healthcare, finance, education, and beyond. It’s become a shared language across disciplines, so we need a variety of voices adding their perspectives to it. Tapping into diverse talent pools not only helps close the gender gap but also fills crucial skill shortages in these fast-growing fields. And let’s be real: different perspectives aren’t just “nice to have”; they’re essential for reducing bias and creating technology that genuinely serves everyone.

Why This Matters:

  • Fairer, Less Biased Technology: Diverse perspectives help identify and mitigate bias, making data and AI systems more reflective of real-world experiences.

  • Addressing the Skills Shortage: With D&A and AI growing so quickly, we need to open doors to all talent pools to meet demand and drive innovation.

  • Solutions for Everyone: When our teams reflect the diversity of our communities, the solutions they create are more inclusive and relevant.

What We Can Do to Make a Difference:

  1. Reach Out to New Talent Pools: Partner with educational institutions, nontraditional programs, and community organizations to recruit talent from a wide variety of backgrounds.

  2. Make Workplaces More Inclusive: It’s not enough to hire diverse talent; we need to make sure they feel supported and valued. This means creating spaces for mentorship, allyship, and policies that ensure everyone can thrive.

  3. Invest in Diverse Leadership: Diverse teams need diverse leaders. Providing career advancement paths, leadership training, and mentorship helps ensure that our leadership reflects our communities.

  4. Encourage Cross-Industry Collaboration: Data and AI impact every sector, so we need leaders from all backgrounds helping shape these fields. Different industries bring unique insights that make for better, more thoughtful AI.

Creating an inclusive and diverse future in data and AI isn’t just good for business; it’s essential for building technology that serves everyone. With every voice we add, we get closer to an industry that’s as innovative and resilient as the world it represents. Let’s start now and make it a priority to bring more diverse perspectives into this work—we all stand to benefit.

Embracing AI Governance: Lessons Learned from The Data Boom

Embracing AI Governance: Lessons Learned from The Data Boom