In 2019, as a doe-eyed economics graduate, I was ready to make my mark on the world. Joining a Big-4’s analytics team, I hoped to merge my academic background in economics with big data to generate actionable insights for clients. My first learning lesson? R is not just an alphabet, Python is no longer just a reptile, Jupyter isn't a ‘fetch’ way to spell the planet, and Excel is as underrated and disrespected as a sleep schedule.
Life at Analytics consulting firms
My first project was a time series forecasting assignment, where I realised that 'ceteris paribus' and 'classical assumptions for linear regression' really mean one thing: 'cover your bases.' Somewhere between my transition from academic rigour to the corporate world, which only cared about R-squared and accuracy, I discovered the true power of data. What many saw as a meaningless dump could be transformed into insights that truly embody the "from drab to fab" mantra.
With every project, my understanding of data broadened and my love for data deepened. Everything was now data. Every choice became an ‘if’ clause, every decision a decision tree, and every statistic a visual chart. From time series machine learning model building to market mix modelling, price elasticity and sensitivity analysis, fraud and anomaly detection, customer segmentation, and profitability analysis, I experienced a power trip. I stunned everyone in the room as the humanities student who could not only code but also explain technical details like they were talking to a 5-year-old.
During my four-year tenure, I spent eight months working within the confines of my four walls—building a structured code base from my makeshift work table and creating aesthetic dashboards in my old rags, all while making sense of unprecedented times brought by SARS-CoV-2 virus.
During one such project, while analysing and quantifying the impact of COVID-19 on marketing strategies, I used Google's mobility index to factor in restrictions on mobility into trend analysis and predictive models. That moment, even as I write this, is still fresh in my memory. It felt like a drop in my stomach, a roller coaster sensation—I knew I could use my skills for a greater purpose.
As I continued to make the rich richer, add deltas to profit margins, and drive shareholder value, I realised I was working in an echo chamber—a room full of individuals who understood the importance of data but were leveraging it to manufacture choice. The suffocating vacuum created by clients' addiction to AI/ML, as an advancement wash, had become stifling.
Transition to Strategy
I knew I didn't want to be a pure-play data scientist, staring at four massive screens searching for a missing semicolon. Thus began my journey to find the perfect mix—something exciting yet purposeful. Finding the right job was harder than finding a matching pair of socks, but then GDi Partners entered my life—much like Morgan Freeman theatrically assuring, "It'll be alright."
At GDi Partners, I went from building models to building strategies. Now, my role involves showing senior government leaders the value of data. Like a starry-eyed salesman, I convince those in power just how transformative data could be. I went from "We've got to do this" to "Did you know this could be done?" It feels like a full-circle moment—a sense of closure. My biggest asset is my ability to talk to both sides of the world: translating tech jargon into strategic insights and business requirements into binary.
Building Data-Driven Cultures
My current role at GDi Partners enables me to help government and social sector organisations adopt data-driven decision-making. This means untangling layers of bureaucracy, translating technical jargon into strategic insights, and helping senior administrators understand the transformative power of data.
One example that comes to mind is a project with a central ministry, where we developed a comprehensive data strategy focused on centralizing data collection and enhancing cross-departmental sharing. This enabled data systems that tackled data silos, and facilitated cross-departmental collaboration. Through effective data storytelling, I helped senior leaders understand the value of these strategies, transforming their mindset and leading to an increase in data literacy across the organisation.
Bridging Gaps
In my role, I bridge the gap between data scientists and decision-makers. Often, data scientists become so engrossed in technical details, such as hyper parameters and neural networks, that they overlook the importance of effectively communicating their findings. At the same time, administrative leaders struggle to visualise the true potential of data and technology, a situation exacerbated when developers delve too deeply into the technical details.
By translating insights into strategic language, I help leaders see the bigger picture. But how do I approach this? First and foremost, with empathy. The first step in building this bridge is understanding both sides' perspectives. I listen to data scientists' challenges and the strategic goals of senior leaders, ensuring that solutions meet both parties' needs. Then, I proceed to simplify. Explaining complex models in simple terms is crucial. Instead of discussing neural networks and decision trees, I focus on outcomes such as “increased beneficiary coverage" or "better policy compliance." Finally, I bring in storytelling because data is most compelling when presented as a story. By linking analytics to a narrative that aligns with leaders' goals, I keep them engaged and invested in data-driven decision-making.
In one memorable instance, developers began explaining the technical modalities of using random forest models, leaving senior leaders unsure of the reliability and utility of the approach. By reframing the explanation into a narrative about how the model could help identify fraudulent claims and save millions, I could quickly turn confusion into enthusiasm.
Embracing Change and Learning
Moving from model-building to strategy-building required a complete shift in mindset. Along the way, I've learned that technical skills are just the foundation, not the destination. While knowing how to build models is essential, the real value lies in translating them into strategic insights. I've also discovered that storytelling is a superpower—numbers may not lie, but it's the narratives woven around them that truly captivate and drive action. Perhaps most importantly, data advocacy is about empowerment. It's not just about showcasing what data can do but equipping others with the tools and mindset to explore its full potential.
Advocating for a Data-Driven Future
My journey from an analytics generalist to an analytics advocate has taught me the importance of building strategies that empower organisations to see beyond the numbers. I have come to understand that advocating for data-driven cultures isn't just about replacing intuition with algorithms. Instead, it's about complementing human expertise with data insights. The real value lies not in blindly adopting new technologies but in thoughtfully integrating them into decision-making processes.
As I continue on this path with GDi Partners, my goal is clear: to help governments and social organisations harness the power of data in ways that drive meaningful and lasting change. Whether it's improving welfare distribution, streamlining government services, or advocating for a data strategy that transforms decision-making, I am committed to building a future where data is not just collected and analysed but truly understood and embraced.