When most people think about public policy, it’s often through a narrow lens, confined to politics or government administration. I used to share this perspective as well. Public policy seemed distant, abstract, and largely theoretical—until my undergraduate studies at Sri Venkateswara College, University of Delhi challenged that view.
An elective on public opinion and surveys introduced me to a completely different dimension of the field. This course explored the design, implementation, and analysis of surveys to understand societal attitudes, preferences, and behaviors. We examined case studies on how public opinion shapes policy decisions and learned to interpret data critically to uncover trends and biases. It was here that I encountered the transformative potential of data—not just as a collection of numbers but as a dynamic tool capable of unraveling societal complexities and driving meaningful change.
This realization fundamentally shifted how I viewed public policy. I began to see it as more than just an academic discipline or a government function. Instead, public policy emerged as a living, breathing process—a mechanism to identify societal problems, envision solutions, and measure their effectiveness.
As I progressed in my academic journey, particularly during my Master’s in Development Studies at Ambedkar University Delhi, I came to understand that data is the key to bridging the gap between ideas and outcomes. It empowers decision-makers to craft informed, impactful policies.
My dissertation exemplified this as I investigated the high incidence of out-migration in the hilly districts of Uttarakhand. To substantiate my arguments, I relied on large-scale datasets from the Census and the National Sample Survey Office (NSSO), using quantitative analysis to explore underlying drivers of this pervasive phenomenon across the state.
This perspective has come full circle as I now pursue my second Master’s in Information Systems and Technology with a focus in Data Science and Analytics from Claremont Graduate University in Claremont, California.
For me, this journey represents a natural evolution, combining my existing expertise in public policy with cutting-edge analytical tools. Public policy and data science, in my view, are deeply interconnected. Where public policy identifies and frames problems, data science provides the means to analyze, predict, and measure the impact of potential solutions with unparalleled precision.
My professional experiences have underscored the importance of integrating data into public policy work. For example, during my time at UNESCO, I worked on youth engagement initiatives in Sri Lanka and the Maldives. These initiatives involved analyzing regional trends to inform program design, fostering inclusion, and combating hate speech.
While I made significant strides in understanding the cultural and social nuances of these challenges, I often wished for more robust analytical tools to identify patterns, track progress, and predict outcomes. With my growing expertise in machine learning and advanced statistical techniques, I now see how I could deepen such analyses, tailoring solutions to be even more impactful and responsive.
Similarly, in the Safe School Zone Program, I evaluated school preparedness for road safety in Delhi. This work required assessing diverse qualitative and quantitative data to determine how schools could better protect students in high-traffic areas. Today, with the knowledge I am acquiring, I can use predictive analytics to assess risk factors, simulate potential scenarios, and optimize resource allocation to create safer environments more efficiently.
Environmental sustainability has also been a cornerstone of my work. During my research with the SDC Foundation in Uttarakhand, I explored waste management challenges in the tourism sector, crafting policy recommendations to mitigate environmental degradation. Similarly, my engagement with Sadhana Forest allowed me to contribute to reforestation projects and zero-waste initiatives. While these experiences were impactful, they also highlighted the limitations of traditional qualitative approaches.
Today, I can envision using geospatial analysis and predictive modeling to understand waste generation patterns or forecast the long-term environmental impact of tourism, enabling more proactive and effective interventions. For instance, geospatial analysis can map waste generation hotspots, track the movement of waste across regions, and identify areas most vulnerable to environmental degradation.
Predictive modeling, on the other hand, can simulate the long-term impact of tourism activities on natural resources, allowing policymakers to foresee potential crises and plan interventions in advance. Even in domains like gender equity and youth development, data science offers transformative pathways to deepen impact.
By combining qualitative narratives and analysis with quantitative insights, data science enables us to design scalable, data-informed solutions that tackle the root causes of these challenges. This approach ensures interventions are firmly grounded in empirical evidence, moving beyond theoretical or solely qualitative frameworks to deliver measurable and impactful outcomes.
My decision to pursue data science arises from a desire to bridge the gap between qualitative understanding and quantitative precision in public policy. While public policy today is often driven by theory, there is a growing need for stronger quantitative skills. Data science for public policy is not just a technical discipline in isolation; it is a way of thinking—a lens through which we can approach public policy challenges with greater clarity, depth, and impact.
As I continue on this journey, I am driven by the belief that the most effective policies are those that are both data-informed and people-centered. By embracing the intersection of data science and public policy, I aim to contribute to a future where decision-making is guided not only by evidence but also by empathy. This is the bridge I hope to build—one that connects the abstract to the actionable and transforms challenges into opportunities for meaningful change.
Dolma Rawat is a Master’s student in Information Systems and Technology at Claremont Graduate University, specializing in data science and analytics. With a background in Development Studies, she focuses on using data-driven approaches to advance public policy, health equity, and sustainable development.
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