Data is people in disguise

 

People in disguise

I recently wrote about how data and creativity work together. When I shared that post on LinkedIn, one of the comments from someone in my network really struck me. 

I shared the post with a quote from towards the end, “Data isn’t just the remit of the analysts and accountants. If we leave it with them because ‘it’s just numbers’, we give away the opportunity to analyse that data through a creative lens.”

In response, a contact shared his favourite quote, “”Data is just people in disguise”.

 

What does that even mean?

We may look at our data as a collection of information, an amalgamation of data points, a huge mass of stuff. But when you zoom in, every single data point represents an element of an individual person. Basically, data is just people – well, data about people is, and that’s what we’re talking about here. 

We can think of that data in different ways – it might be demographic data that tells us about an individual, or behavioural data that tells us about decisions they’ve made, for example their purchases or shows they’ve watched. 

We don’t tend to look at individual data – partly because it’s too granular for what we do as a business, and more importantly because it starts to feel a little intrusive. We look at cohorts of people who have either got the same demographics or have made the same decisions.

But when we look at big groups of data, it’s easy to forget the fact that we’re looking at people. Lots of people. 

 

Why does it matter?

The idea that data is just people in disguise is a nice sentiment, but does it really matter? Especially if we’re not looking at information on an individual basis.

Well, when any brand makes decisions based on data, it’s important to recognise that individual people will be affected. 

When a bus company drops a route, for example, because the data doesn’t support keeping it running, there are people who will be affected. If the comms announcing the closure don’t recognise that there are individual humans at the other end, the message may not be as sensitive as it needs to be. 

When communicating with any large group of people, we need to recognise that they’re not a monolith. They may be in the same data segment, but they’re still different. Take this example: 

• Male
• Born in 1948
• Raised in the UK
• Married twice
• Wealthy and famous
• Lives in a castle

This is data that describes both King Charles and Ozzy Osbourne. If we forget that data is just people, we can expect our segments to be too homogenic and not recognise how wildly different the people within them could be. 

 

How does this all work in practice?

No one wants an email that’s so hyper-personalised that they feel stalked. Equally, no one who’s a vegan wants to receive emails about beef burgers. There’s always a balance to be found when segmenting and personalising emails. 

When we craft copy, for example, we have to think about how different individuals will receive it. A little while ago I wrote a headline for an email that I really liked. It linked back to the origins of a beloved character and had a nice nod to the past. 

When a couple of younger colleagues read it, they didn’t know that history, so the headline didn’t land with them. Once I explained, they thought it was great – but we can’t go and explain the context of an email to everyone who receives it! 

That’s not to say that every piece of creative has to be universal, but if the copy relies on knowledge not included in the email, it’s important to think about whether or not it will still work for the people who don’t have that prior knowledge. Clever copy can reference something while still working on its own merits. 

Bearing in mind that every data point is a person reminds us that they don’t all have the same knowledge or experience. They may be in the same segment because of demographics or behaviours, but they’re still unique individuals. We can’t lose sight of that just because we’re looking at them through cohorts of data.