Most people, when they hear the words ‘data science’, imagine a field that draws entirely on the far left side of the brain. A world of input and output, algorithms and calculation that provides you with the hard analysis you need to make objective decisions.

It’s a well-intentioned but misleading perspective.

The truth is, data science is multifaceted. It’s investigative work, it’s innovative, and it’s an evolving field - one where technology, methodology and problem solving collide.

And fundamentally, at its heart, data science is a creative process.

Ultimately, to make the most of your data, you want to commercialise it. For the record, we’re not talking about selling user data to other companies here. This is about using the information you have to make better decisions - for your customers, in your operations and in your strategy.

So let’s break that down.

You want to make your data projects count. There’s no point wasting time and resources when there are simple ways to make the most of the process. But before we jump into what you shouldn’t do when outsourcing a data project, first let’s take a step back and look at a project that ran like a dream for one of our clients.

On the surface, life sciences and financial services have little in common. But when it comes to the consequences of AI and decision engines, there’s more than a little overlap.