The Evolution of Data Strategies: Why the Old Ways Are Holding You Back
Is your data strategy stuck in the past?
Data strategies designed five—or even three—years ago are already becoming outdated. The speed of change in today’s business environment means that what worked well before might now be limiting your ability to compete. But here’s the counterintuitive truth: it’s not just about adopting the latest tech or techniques—it’s about rethinking your entire approach to data strategy.
Why? Because the assumptions that shaped traditional data strategies no longer hold true.
I recently worked with a large financial services company that had a meticulously planned data strategy. They had invested in building a centralised data warehouse, hired a team of data scientists, and implemented a series of processes to govern data use. The strategy looked robust on paper—but in reality, it couldn’t keep up.
Teams struggled to access relevant data in real-time, the rigidity of the implemented governance structures slowed down innovation, and despite all the investment, data was still siloed across departments. The strategy that was once considered best-in-class had now become a barrier to agility and growth.
The truth? Traditional data strategies often create rigidity instead of adaptability.
Old models assume that you can plan out your entire data strategy years in advance, but today’s market dynamics require flexibility, speed, and the ability to pivot on the fly. The most successful companies aren’t the ones with the most sophisticated data plans—they’re the ones that can adapt and scale their data capabilities in response to (or even in advance of) change.
So, what does a modern, adaptive data strategy look like?
Here’s what I’ve seen work in organisations that are leading the way with their data strategies: