rethinking qualitative analysis
Reflecting on my early days at Google, I recall a project that involved analyzing thousands of free-text comments from our internal survey, Googlegeist. The task was straightforward in concept: distill this qualitative data into actionable insights by identifying common themes, categorizing feedback, and quantifying sentiments. But in practice, it was a tedious and time-consuming process, taking countless hours to produce anything meaningful. At the time, we didn’t have effective tools for this. There were options, but they were often clunky, lacked the nuance required to understand context, and frequently missed the mark on detecting tone and sentiment accurately.
Fast forward to today, and the tools for qualitative analysis have come a long way, largely due to advancements in artificial intelligence and sophisticated natural language models. I’m often asked about AI's role in data storytelling and whether and in what instances it can replace human judgment. The answer isn’t black-and-white; AI isn’t a perfect solution, nor does it eliminate the need for human insight. However, it has advanced enough to solve challenges that seemed insurmountable in the past—for example, in the realm of qualitative analysis.
Recently, I saw a demo of a tool created by a friend of mine—also a former Googler—who founded a company called Insight7. As he walked my team at storytelling with data through the tool’s capabilities, I found myself thinking back to that early Google project and how much time it could have saved me. Insight7’s tool automatically categorizes and analyzes text, drawing out themes and insights in seconds. What struck me wasn’t just the speed, but the quality of the analysis. It was a reminder that as the technology around us evolves, so should our openness to exploring new possibilities. Tools like this can augment our work, help us uncover insights we might have missed, and ultimately free us to focus on what we do best—crafting stories and building understanding.
It’s easy to hold onto previous beliefs about what’s possible, especially when we’ve experienced the frustrations of earlier versions. For me, one example is my long-held view of pie charts as suboptimal for most data storytelling purposes. But I’ve found that staying open to revisiting these beliefs keeps me open to innovation (sometimes, to using even pie charts in the right context!). In the same way, we may hold outdated beliefs about AI’s capabilities, but if we remain curious, we’ll be better positioned to make use of advancements that are made.
As data storytelling and analysis in the age of AI continue to evolve, let’s not be limited by past assumptions. Who knows what new possibilities might emerge if we stay open to tools and ideas that can help us tell even richer, more compelling stories with data?
If you’re curious to explore Insight7, they’ve just launched new product features, and my friend has offered a special discount for SWD readers. Use the code SWD40 at Insight7.io to get 40% off all paid plans through November 15. If you work with qualitative data—or have been hesitant due to the time investment involved—this tool could be worth a look.
