Most companies are drowning in data, but starving for insight. The difference between collecting information and actually using it comes down to how you structure your transformation from the start. Christopher Bannocks has spent over 25 years solving this exact problem for global organizations such as ING, Danone, and most recently QBE, turning raw data into business results that show up on the bottom line.
Start With Strategic Alignment
Too many data teams treat transformation as their project to own. That’s the first mistake. Bannocks learned this early in his career: if the business isn’t leading, you’re already behind. “A successful transformation isn’t a data team initiative—it’s a business-wide mandate,” he explains. At QBE, he worked directly with three divisional CEOs to build a generative AI platform. It had to do real work for underwriting, sales, and customer service. No vanity metrics, no theoretical benefits. Within 12 weeks, they saw a 58% increase in quote to bind ratio. “When the business leads, data accelerates,” Bannocks says. That’s the difference between a transformation that matters and one that dies in a pilot program.
Build Federated Capability, Not Centralized Control
Centralization sounds logical on paper. One team, one standard, total control. But Bannocks has watched that approach fail too many times. Companies try to centralize everything and end up killing innovation in the process. The alternative? Federated models. You keep core platforms and standards consistent, but divisions get room to move. “Think of it as a jazz band—different instruments, same rhythm,” he explains. At ING, this model stretched across 12 countries and more than 500 team members. Each market could adapt to local needs without breaking the broader architecture. Standards matter, but so does speed.
Activate Data, Don’t Just Store It
Walk into any large company and you’ll find the same thing: beautifully organized data that nobody touches. The governance is perfect, the documentation is complete, and none of it connects to actual decisions. “Too many transformation programs end up with well-governed data that nobody uses,” Bannocks points out. Data needs to move from policy documents into production. That means pushing analytics and AI into the workflows where people actually make decisions. At Danone, the supply chain team didn’t just get clean data. They received it at the exact moment they needed to decide on inventory, production schedules, and distribution routes. This changes everything about how you measure success. “Transformation isn’t about owning more data—it’s about making better decisions with what you already have,” Bannocks says. Companies waste time chasing new data sources when they haven’t figured out how to use what’s already sitting in their systems.
Structuring an enterprise-wide data transformation comes down to three moves. Align everything to business strategy, not IT priorities. Build federated models so teams can innovate without chaos. Push data into production where it drives decisions instead of just generating reports. The winners aren’t collecting more data than everyone else. They know what to do with it. As Bannocks puts it, “The companies that win aren’t the ones with the most data—they’re the ones who know what to do with it.” His final point matters more than any technical detail: “Transformation isn’t a project—it’s a mindset.” You can’t put transformation on a timeline and call it done. The companies getting this right have built it into how they operate every day.
Connect with Christopher Bannocks on LinkedIn to explore more about data-driven transformation.
