I started out in the financial services sector, working at a brokerage firm in university, which appealed to me because I liked that math could be interpreted to make predictions.

From there, I moved to Nielsen, a global leader in audience measurement, data and analytics, with my sights set on becoming a best-in-class market analyst. It was in this role that I developed my passion for analytics—and I still consider myself to be an analyst first. When someone hands me a dataset, my initial inclination all these years later is to interpret what’s in front of me.

But in my role at Nielsen, an interesting thing started to happen. I began to notice that the storytelling element—how insights are identified and communicated to an audience—came naturally to me. I leaned into this and honed my skills, which included building the internal data storytelling program at Nielsen, while also working on the external client service side to help clients translate what their data meant for them.

With every role on my CV, at Nielsen and in the positions I held at other companies in the years to follow, it became clear that the collection of data was less important than making the data work. The amount of data has increased exponentially since I started out as an analyst, but the aim is still the same: to help people tell their data stories so that the intended audience understands and action is possible. But this isn’t as easy as it sounds. Data storytelling is a skill set, and one where there are often gaps to fill. That’s where Storylytics comes in. After more than two decades in corporate roles, I realized there was an opportunity to help people learn to make their data work more effectively. It’s a funny thing when you think about it: I’m still using math to make predictions 25+ years later, but it’s so much more than that.