If there’s one thread that has run through my career, it’s data—not always in obvious ways, but it’s always been there, guiding decisions, shaping strategies, and even challenging my thinking. It started back in my early days as an art director. I’d spend hours poring over client sales figures and scanning data to find the patterns that could unlock a better campaign. I could get lost in time working on this data. That beautiful mix of being challenged and timelines that make time fly by.
In many cases in the past, I relied heavily on third-party data sources like Nielsen and Roy Morgan Research to provide context. When those didn’t cut it, I’d go scavenging on the internet, often ending up deep in spreadsheets or obscure reports. The Australian Bureau of Statistics (ABS) has been my saviour more times than I can count—even if I did crash TableBuilder tool a few times. The downside is that the data is consistently outdated due to the time it takes to collect census data for publication. This impacts other third-party demographic profile tools; they never reflect the current environment. Then there is the cost of data, I’ll come back to this.
In those early days of the internet, data analytics was beyond primitive. I remember adding page counters to websites and using tools like Analog to interpret the results. It was clunky and basic compared to today’s platforms, but it gave us a glimpse of what was possible. Platforms like Google Analytics emerged as the internet evolved, offering a much richer picture of user behaviour. Suddenly, we could easily track clicks, bounce rates, and conversions. For instance, in one campaign for a national retail chain, Google Analytics revealed that mobile users were abandoning their carts at an alarming rate. By focusing on optimising the mobile checkout experience, we were able to reduce cart abandonment within a quarter. These tools transformed how we approached campaigns and laid the groundwork for the data-driven strategies that became central to my work. Tools were starting to emerge, and the seeds of what would become my passion for data were being planted.
Fast forward to my time at Roy Morgan, where I stumbled upon Tableau. It was a revelation. Suddenly, data wasn’t just numbers on a page; it was visual, dynamic, and — dare I say — eye-opening. One memorable project involved using Tableau to analyse customer satisfaction data for a banking client client. By visualising the hierarchy of results patterns across branches and time, I uncovered rolling impacts of change to the grassroots of the banking industry. I became obsessed with attending user groups and diving into the community. I even wrangled a seat at the table with Pat Hanrahan (sorry about the name drop), one of Tableau’s founders at dinner in Sydney. That experience cemented my love for the tool, and while I’ve worked with PowerBI and others, Tableau has always felt natural.
Of course, data analysis is never just about the tools. I’ve always believed in understanding the story behind the numbers. It’s not enough to know what the data says; you must understand why it says it. This belief became even more significant when I began incorporating AI into my work. Tools like LLMs have revolutionised how I clean, categorise, and extrapolate data, especially when working with something as complex as Google Search Console data. AI takes the grunt work out of the process, freeing me to focus on what the data actually means.
What’s exciting now is how my background in anthropology is weaving into this mix. When viewed through an anthropological lens, data becomes more than just statistics. It’s about human behaviour, culture, and context. My thesis on Geertz’s “wink” analogy explored this idea: numbers can tell you what happened but rarely tell you why. To find the why, you must dig deeper—to look at the subtle cultural layers the data represents.
One of the significant challenges throughout my journey has been the lack of accessible data. Often, data is too expensive to acquire or not collected by businesses. I vividly recall working with a regional retail chain with no formal customer data collection process. To overcome this, we relied on publicly available census data and online customer reviews to gather insights about their audience. While this workaround provided some direction, it underscored the limitations of not having comprehensive first-party data. This has been a recurring roadblock, especially for smaller organisations that lack the resources to invest in significant data infrastructure. The cost of entry can be prohibitive, excluding many from developing robust data practices. Without proper data collection and analysis investment, businesses miss out on crucial insights that could drive their growth and strategy.
5-6 years ago a door was opened to PWC. I felts like I was definetly not ready for the role. this becmae one of the driving factors to studying Anthropology. Today, I find myself drawn back to data analysis. It feels like a return to something foundational with new tools, new perspectives, and a fresh sense of purpose. It’s not just about crunching numbers anymore; it’s about combining data with anthropological insights to tell richer, more meaningful stories. I envision data playing an even greater role in bridging the gap between raw analytics and human context. With emerging technologies and a deeper integration of interdisciplinary approaches, there’s an incredible opportunity to uncover insights that inform strategies and inspire new ways of thinking and connecting with audiences.
After all, isn’t that what it’s always been about—using creativity, technology, and strategy to make sense of the world? It’s not just about crunching numbers anymore; it’s about combining data with anthropological insights to tell richer, more meaningful stories. After all, isn’t that what it’s always been about—using creativity, technology, and strategy to make sense of the world?
