When you want to cook a wonderful meal for someone special, you don’t just open the fridge and stare at the half-empty shelves thinking: what can I make with this? Instead, you ask yourself what will they love and what else do I need to make it?
Yet when thinking about data analytics, many businesses choose the leftovers option. They look at the data they already have and think: what can I make of this and what can I learn from it? But it is far more effective to think about the urgent or routine business decisions that have to be made, and then determine what data you need (or already have or need to elicit) in order to make them.
Looking more closely, it’s clear that the leftovers approach doesn’t work in any industry. The tabloid press don’t simply ask: “What happened yesterday and what can we make of it?” Instead, they ask: “What do people want to read and where can we get the ‘facts’?” Tesla isn’t successful because it already had a huge assembly platform and then asked: “what kind of car can we make with this?”. Instead, they built the car that they thought would excite people – and sourced or developed the components they needed. The iPhone wasn’t an evolution of existing mobile phone technology (“what can we make with this?”). Instead, they wondered how to make the ultimate mobile device that would excite consumers, and then procured or developed the technology they needed to produce it.
